commit
stringlengths 40
40
| commit_date
stringdate 2023-02-23 09:31:55
2025-08-02 08:59:34
| Dockerfile
stringclasses 59
values | category
stringclasses 3
values |
|---|---|---|---|
d4bc1a4d248a5d23e1f731ecb53511a9a54f5dfc
|
2023-02-23T09:31:55+00:00
| null |
nvidia
|
c45f3c3ab60f4bf4eaab791a76028b8b07ffe9bd
|
2023-04-01T00:51:08+08:00
| null |
nvidia
|
897cb2ae28e93de1b22ecfbffcccfb9493f8f4d9
|
2023-04-02T00:30:17-07:00
| null |
nvidia
|
0f40557af6141ced118b81f2a04e651a0c6c9dbd
|
2023-04-07T17:45:07-07:00
| null |
nvidia
|
ec3b5ce9ccb4262194a16a8b1c31ffd6b3b824b9
|
2023-10-13T09:59:07-07:00
| null |
nvidia
|
21d93c140d0a97af5f0c59e660cf04bd417fd424
|
2023-12-13T23:55:07-08:00
|
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# image to build pytorch extensions
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# copy input files
COPY csrc csrc
COPY setup.py setup.py
COPY requirements.txt requirements.txt
COPY pyproject.toml pyproject.toml
COPY vllm/__init__.py vllm/__init__.py
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
RUN python3 setup.py build_ext --inplace
# image to run unit testing suite
FROM dev AS test
# copy pytorch extensions separately to avoid having to rebuild
# when python code changes
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY tests tests
COPY vllm vllm
ENTRYPOINT ["python3", "-m", "pytest", "tests"]
# use CUDA base as CUDA runtime dependencies are already installed via pip
FROM nvidia/cuda:12.1.0-base-ubuntu22.04 AS vllm-base
# libnccl required for ray
RUN apt-get update -y \
&& apt-get install -y python3-pip
WORKDIR /workspace
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
FROM vllm-base AS vllm
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY vllm vllm
EXPOSE 8000
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.api_server"]
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY vllm vllm
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
|
nvidia
|
fd4ea8ef5c17a8b991107402a414f6ed355d854d
|
2024-01-03T11:30:22-08:00
|
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# image to build pytorch extensions
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# copy input files
COPY csrc csrc
COPY setup.py setup.py
COPY requirements.txt requirements.txt
COPY pyproject.toml pyproject.toml
COPY vllm/__init__.py vllm/__init__.py
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
RUN python3 setup.py build_ext --inplace
# image to run unit testing suite
FROM dev AS test
# copy pytorch extensions separately to avoid having to rebuild
# when python code changes
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY tests tests
COPY vllm vllm
ENTRYPOINT ["python3", "-m", "pytest", "tests"]
# use CUDA base as CUDA runtime dependencies are already installed via pip
FROM nvidia/cuda:12.1.0-base-ubuntu22.04 AS vllm-base
# libnccl required for ray
RUN apt-get update -y \
&& apt-get install -y python3-pip
WORKDIR /workspace
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
FROM vllm-base AS vllm
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY vllm vllm
EXPOSE 8000
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.api_server"]
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY vllm vllm
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
|
nvidia
|
f8ecb84c0283a7f1ba02ee732c9f044f8f9d36ee
|
2024-01-27T17:46:56-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
#################### BASE BUILD IMAGE ####################
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
#################### BASE BUILD IMAGE ####################
#################### EXTENSION BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# copy input files
COPY csrc csrc
COPY setup.py setup.py
COPY requirements.txt requirements.txt
COPY pyproject.toml pyproject.toml
COPY vllm/__init__.py vllm/__init__.py
# cuda arch list used by torch
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
RUN python3 setup.py build_ext --inplace
#################### EXTENSION Build IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
FROM dev AS test
# copy pytorch extensions separately to avoid having to rebuild
# when python code changes
WORKDIR /vllm-workspace
# ADD is used to preserve directory structure
ADD . /vllm-workspace/
COPY --from=build /workspace/vllm/*.so /vllm-workspace/vllm/
# ignore build dependencies installation because we are using pre-complied extensions
RUN rm pyproject.toml
RUN --mount=type=cache,target=/root/.cache/pip VLLM_USE_PRECOMPILED=1 pip install . --verbose
#################### TEST IMAGE ####################
#################### RUNTIME BASE IMAGE ####################
# use CUDA base as CUDA runtime dependencies are already installed via pip
FROM nvidia/cuda:12.1.0-base-ubuntu22.04 AS vllm-base
# libnccl required for ray
RUN apt-get update -y \
&& apt-get install -y python3-pip
WORKDIR /workspace
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
#################### RUNTIME BASE IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY vllm vllm
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
9474e89ba4ecae253b585eb6b3e1d85f4e108f01
|
2024-03-20T00:11:11-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
#################### BASE BUILD IMAGE ####################
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.1/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
#################### BASE BUILD IMAGE ####################
#################### EXTENSION BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# copy input files
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements.txt requirements.txt
COPY pyproject.toml pyproject.toml
COPY vllm/__init__.py vllm/__init__.py
# cuda arch list used by torch
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
RUN python3 setup.py build_ext --inplace
#################### EXTENSION Build IMAGE ####################
#################### FLASH_ATTENTION Build IMAGE ####################
FROM dev as flash-attn-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# flash attention version
ARG flash_attn_version=v2.5.6
ENV FLASH_ATTN_VERSION=${flash_attn_version}
WORKDIR /usr/src/flash-attention-v2
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
--no-build-isolation --no-deps --no-cache-dir
#################### FLASH_ATTENTION Build IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
FROM dev AS test
# copy pytorch extensions separately to avoid having to rebuild
# when python code changes
WORKDIR /vllm-workspace
# ADD is used to preserve directory structure
ADD . /vllm-workspace/
COPY --from=build /workspace/vllm/*.so /vllm-workspace/vllm/
# Install flash attention (from pre-built wheel)
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
# ignore build dependencies installation because we are using pre-complied extensions
RUN rm pyproject.toml
RUN --mount=type=cache,target=/root/.cache/pip VLLM_USE_PRECOMPILED=1 pip install . --verbose
#################### TEST IMAGE ####################
#################### RUNTIME BASE IMAGE ####################
# We used base cuda image because pytorch installs its own cuda libraries.
# However cupy depends on cuda libraries so we had to switch to the runtime image
# In the future it would be nice to get a container with pytorch and cuda without duplicating cuda
FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 AS vllm-base
# libnccl required for ray
RUN apt-get update -y \
&& apt-get install -y python3-pip
WORKDIR /workspace
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# Install flash attention (from pre-built wheel)
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
#################### RUNTIME BASE IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY vllm vllm
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
cf2f084d56a1293cb08da2393984cdc7685ac019
|
2024-03-22T12:28:14-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
#################### BASE BUILD IMAGE ####################
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.1/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
#################### BASE BUILD IMAGE ####################
#################### EXTENSION BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# copy input files
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements.txt requirements.txt
COPY pyproject.toml pyproject.toml
COPY vllm/__init__.py vllm/__init__.py
# cuda arch list used by torch
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
RUN python3 setup.py build_ext --inplace
#################### EXTENSION Build IMAGE ####################
#################### FLASH_ATTENTION Build IMAGE ####################
FROM dev as flash-attn-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# flash attention version
ARG flash_attn_version=v2.5.6
ENV FLASH_ATTN_VERSION=${flash_attn_version}
WORKDIR /usr/src/flash-attention-v2
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
--no-build-isolation --no-deps --no-cache-dir
#################### FLASH_ATTENTION Build IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
FROM dev AS test
# copy pytorch extensions separately to avoid having to rebuild
# when python code changes
WORKDIR /vllm-workspace
# ADD is used to preserve directory structure
ADD . /vllm-workspace/
COPY --from=build /workspace/vllm/*.so /vllm-workspace/vllm/
# Install flash attention (from pre-built wheel)
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
# ignore build dependencies installation because we are using pre-complied extensions
RUN rm pyproject.toml
RUN --mount=type=cache,target=/root/.cache/pip VLLM_USE_PRECOMPILED=1 pip install . --verbose
#################### TEST IMAGE ####################
#################### RUNTIME BASE IMAGE ####################
# We used base cuda image because pytorch installs its own cuda libraries.
# However cupy depends on cuda libraries so we had to switch to the runtime image
# In the future it would be nice to get a container with pytorch and cuda without duplicating cuda
FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 AS vllm-base
# libnccl required for ray
RUN apt-get update -y \
&& apt-get install -y python3-pip
WORKDIR /workspace
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# Install flash attention (from pre-built wheel)
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
#################### RUNTIME BASE IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY vllm vllm
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
bfdb1ba5c3fb14387c69acb1f5067102d8028e56
|
2024-03-22T13:44:12-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
#################### BASE BUILD IMAGE ####################
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.1/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
#################### BASE BUILD IMAGE ####################
#################### EXTENSION BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# copy input files
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements.txt requirements.txt
COPY pyproject.toml pyproject.toml
COPY vllm/__init__.py vllm/__init__.py
# cuda arch list used by torch
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
RUN python3 setup.py build_ext --inplace
#################### EXTENSION Build IMAGE ####################
#################### FLASH_ATTENTION Build IMAGE ####################
FROM dev as flash-attn-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# flash attention version
ARG flash_attn_version=v2.5.6
ENV FLASH_ATTN_VERSION=${flash_attn_version}
WORKDIR /usr/src/flash-attention-v2
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
--no-build-isolation --no-deps --no-cache-dir
#################### FLASH_ATTENTION Build IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
FROM dev AS test
# copy pytorch extensions separately to avoid having to rebuild
# when python code changes
WORKDIR /vllm-workspace
# ADD is used to preserve directory structure
ADD . /vllm-workspace/
COPY --from=build /workspace/vllm/*.so /vllm-workspace/vllm/
# Install flash attention (from pre-built wheel)
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
# ignore build dependencies installation because we are using pre-complied extensions
RUN rm pyproject.toml
RUN --mount=type=cache,target=/root/.cache/pip VLLM_USE_PRECOMPILED=1 pip install . --verbose
#################### TEST IMAGE ####################
#################### RUNTIME BASE IMAGE ####################
# We used base cuda image because pytorch installs its own cuda libraries.
# However cupy depends on cuda libraries so we had to switch to the runtime image
# In the future it would be nice to get a container with pytorch and cuda without duplicating cuda
FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 AS vllm-base
# libnccl required for ray
RUN apt-get update -y \
&& apt-get install -y python3-pip
WORKDIR /workspace
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# Install flash attention (from pre-built wheel)
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
#################### RUNTIME BASE IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY vllm vllm
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
3a243095e5e7b655b63ab08fbd5936cb40850415
|
2024-03-25T16:03:02-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
#################### BASE BUILD IMAGE ####################
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.1/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
#################### BASE BUILD IMAGE ####################
#################### EXTENSION BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# copy input files
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements.txt requirements.txt
COPY pyproject.toml pyproject.toml
COPY vllm/__init__.py vllm/__init__.py
# cuda arch list used by torch
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
RUN python3 setup.py build_ext --inplace
#################### EXTENSION Build IMAGE ####################
#################### FLASH_ATTENTION Build IMAGE ####################
FROM dev as flash-attn-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# flash attention version
ARG flash_attn_version=v2.5.6
ENV FLASH_ATTN_VERSION=${flash_attn_version}
WORKDIR /usr/src/flash-attention-v2
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
--no-build-isolation --no-deps --no-cache-dir
#################### FLASH_ATTENTION Build IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
FROM dev AS test
# copy pytorch extensions separately to avoid having to rebuild
# when python code changes
WORKDIR /vllm-workspace
# ADD is used to preserve directory structure
ADD . /vllm-workspace/
COPY --from=build /workspace/vllm/*.so /vllm-workspace/vllm/
# Install flash attention (from pre-built wheel)
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
# ignore build dependencies installation because we are using pre-complied extensions
RUN rm pyproject.toml
RUN --mount=type=cache,target=/root/.cache/pip VLLM_USE_PRECOMPILED=1 pip install . --verbose
#################### TEST IMAGE ####################
#################### RUNTIME BASE IMAGE ####################
# We used base cuda image because pytorch installs its own cuda libraries.
# However cupy depends on cuda libraries so we had to switch to the runtime image
# In the future it would be nice to get a container with pytorch and cuda without duplicating cuda
FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 AS vllm-base
# libnccl required for ray
RUN apt-get update -y \
&& apt-get install -y python3-pip
WORKDIR /workspace
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# Install flash attention (from pre-built wheel)
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
#################### RUNTIME BASE IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY vllm vllm
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
b6d103542c654fb63013a1e45a586d654ae36a2a
|
2024-03-30T14:26:38-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
#################### BASE BUILD IMAGE ####################
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.1/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
#################### BASE BUILD IMAGE ####################
#################### EXTENSION BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# copy input files
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements.txt requirements.txt
COPY pyproject.toml pyproject.toml
COPY vllm/__init__.py vllm/__init__.py
# cuda arch list used by torch
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
python3 setup.py build_ext --inplace
#################### EXTENSION Build IMAGE ####################
#################### FLASH_ATTENTION Build IMAGE ####################
FROM dev as flash-attn-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# flash attention version
ARG flash_attn_version=v2.5.6
ENV FLASH_ATTN_VERSION=${flash_attn_version}
WORKDIR /usr/src/flash-attention-v2
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
--no-build-isolation --no-deps --no-cache-dir
#################### FLASH_ATTENTION Build IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
FROM dev AS test
# copy pytorch extensions separately to avoid having to rebuild
# when python code changes
WORKDIR /vllm-workspace
# ADD is used to preserve directory structure
ADD . /vllm-workspace/
COPY --from=build /workspace/vllm/*.so /vllm-workspace/vllm/
# Install flash attention (from pre-built wheel)
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
# ignore build dependencies installation because we are using pre-complied extensions
RUN rm pyproject.toml
RUN --mount=type=cache,target=/root/.cache/pip VLLM_USE_PRECOMPILED=1 pip install . --verbose
#################### TEST IMAGE ####################
#################### RUNTIME BASE IMAGE ####################
# We used base cuda image because pytorch installs its own cuda libraries.
# However pynccl depends on cuda libraries so we had to switch to the runtime image
# In the future it would be nice to get a container with pytorch and cuda without duplicating cuda
FROM nvidia/cuda:12.1.0-runtime-ubuntu22.04 AS vllm-base
# libnccl required for ray
RUN apt-get update -y \
&& apt-get install -y python3-pip
WORKDIR /workspace
COPY requirements.txt requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
# Install flash attention (from pre-built wheel)
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
#################### RUNTIME BASE IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
COPY --from=build /workspace/vllm/*.so /workspace/vllm/
COPY vllm vllm
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
2f1928354903ae0c6edfe76cc90081eb513ead2c
|
2024-04-06T19:14:06-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.1/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
python3 setup.py bdist_wheel --dist-dir=dist
# the `vllm_nccl` package must be installed from source distribution
# pip is too smart to store a wheel in the cache, and other CI jobs
# will directly use the wheel from the cache, which is not what we want.
# we need to remove it manually
RUN --mount=type=cache,target=/root/.cache/pip \
pip cache remove vllm_nccl*
#################### EXTENSION Build IMAGE ####################
#################### FLASH_ATTENTION Build IMAGE ####################
FROM dev as flash-attn-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# flash attention version
ARG flash_attn_version=v2.5.6
ENV FLASH_ATTN_VERSION=${flash_attn_version}
WORKDIR /usr/src/flash-attention-v2
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
--no-build-isolation --no-deps --no-cache-dir
#################### FLASH_ATTENTION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.1.0-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.1/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
--mount=type=cache,target=/root/.cache/pip \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
ad8d696a99ca1eee19f1404e16e8e82df592ff85
|
2024-04-22T21:11:06+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.1/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
python3 setup.py bdist_wheel --dist-dir=dist
# the `vllm_nccl` package must be installed from source distribution
# pip is too smart to store a wheel in the cache, and other CI jobs
# will directly use the wheel from the cache, which is not what we want.
# we need to remove it manually
RUN --mount=type=cache,target=/root/.cache/pip \
pip cache remove vllm_nccl*
#################### EXTENSION Build IMAGE ####################
#################### FLASH_ATTENTION Build IMAGE ####################
FROM dev as flash-attn-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# flash attention version
ARG flash_attn_version=v2.5.6
ENV FLASH_ATTN_VERSION=${flash_attn_version}
WORKDIR /usr/src/flash-attention-v2
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
--no-build-isolation --no-deps --no-cache-dir
#################### FLASH_ATTENTION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.1.0-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.1/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
--mount=type=cache,target=/root/.cache/pip \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
2a052011ca473a9dc8160f3daa1f5f63a2ad1fe3
|
2024-05-04T11:45:16-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.1/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
python3 setup.py bdist_wheel --dist-dir=dist
# the `vllm_nccl` package must be installed from source distribution
# pip is too smart to store a wheel in the cache, and other CI jobs
# will directly use the wheel from the cache, which is not what we want.
# we need to remove it manually
RUN --mount=type=cache,target=/root/.cache/pip \
pip cache remove vllm_nccl*
#################### EXTENSION Build IMAGE ####################
#################### FLASH_ATTENTION Build IMAGE ####################
FROM dev as flash-attn-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# flash attention version
ARG flash_attn_version=v2.5.8
ENV FLASH_ATTN_VERSION=${flash_attn_version}
WORKDIR /usr/src/flash-attention-v2
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
--no-build-isolation --no-deps --no-cache-dir
#################### FLASH_ATTENTION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.1.0-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.1/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
--mount=type=cache,target=/root/.cache/pip \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
d7740ea4dcee4ab75d7d6eef723f33cae957b288
|
2024-05-08T08:42:28-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
python3 setup.py bdist_wheel --dist-dir=dist
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
# the `vllm_nccl` package must be installed from source distribution
# pip is too smart to store a wheel in the cache, and other CI jobs
# will directly use the wheel from the cache, which is not what we want.
# we need to remove it manually
RUN --mount=type=cache,target=/root/.cache/pip \
pip cache remove vllm_nccl*
#################### EXTENSION Build IMAGE ####################
#################### FLASH_ATTENTION Build IMAGE ####################
FROM dev as flash-attn-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# flash attention version
ARG flash_attn_version=v2.5.8
ENV FLASH_ATTN_VERSION=${flash_attn_version}
WORKDIR /usr/src/flash-attention-v2
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
--no-build-isolation --no-deps --no-cache-dir
#################### FLASH_ATTENTION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
--mount=type=cache,target=/root/.cache/pip \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
ad932a221d2a4c1e6355021bb9e9c47f7a179e51
|
2024-05-08T10:33:18-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
python3 setup.py bdist_wheel --dist-dir=dist
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
# the `vllm_nccl` package must be installed from source distribution
# pip is too smart to store a wheel in the cache, and other CI jobs
# will directly use the wheel from the cache, which is not what we want.
# we need to remove it manually
RUN --mount=type=cache,target=/root/.cache/pip \
pip cache remove vllm_nccl*
#################### EXTENSION Build IMAGE ####################
#################### FLASH_ATTENTION Build IMAGE ####################
FROM dev as flash-attn-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# flash attention version
ARG flash_attn_version=v2.5.8
ENV FLASH_ATTN_VERSION=${flash_attn_version}
WORKDIR /usr/src/flash-attention-v2
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
--no-build-isolation --no-deps --no-cache-dir
#################### FLASH_ATTENTION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
--mount=type=cache,target=/root/.cache/pip \
pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
379da6dcb5f5d062d0452b2fc23291e5113dcf04
|
2024-05-09T16:38:07-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
python3 setup.py bdist_wheel --dist-dir=dist
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
# the `vllm_nccl` package must be installed from source distribution
# pip is too smart to store a wheel in the cache, and other CI jobs
# will directly use the wheel from the cache, which is not what we want.
# we need to remove it manually
RUN --mount=type=cache,target=/root/.cache/pip \
pip cache remove vllm_nccl*
#################### EXTENSION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
8bc68e198c4c90ddc2e54fa76eb81c2c714bb1cd
|
2024-05-13T14:57:07-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
python3 setup.py bdist_wheel --dist-dir=dist
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
# the `vllm_nccl` package must be installed from source distribution
# pip is too smart to store a wheel in the cache, and other CI jobs
# will directly use the wheel from the cache, which is not what we want.
# we need to remove it manually
RUN --mount=type=cache,target=/root/.cache/pip \
pip cache remove vllm_nccl*
#################### EXTENSION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
eb6d3c264d0cd8e44dec16bca7947fbe96415ce9
|
2024-05-23T06:17:27+09:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
python3 setup.py bdist_wheel --dist-dir=dist
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
# the `vllm_nccl` package must be installed from source distribution
# pip is too smart to store a wheel in the cache, and other CI jobs
# will directly use the wheel from the cache, which is not what we want.
# we need to remove it manually
RUN --mount=type=cache,target=/root/.cache/pip \
pip cache remove vllm_nccl*
#################### EXTENSION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
a377f0bd5e1fa0ca069e3dbf28f4de5af64d0bb1
|
2024-05-31T13:14:50+08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
python3 setup.py bdist_wheel --dist-dir=dist
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
8d75fe48ca5f46b7af0f5201d8500b9604eed769
|
2024-06-07T08:42:35+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
python3 setup.py bdist_wheel --dist-dir=dist
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
80aa7e91fcd547a7a1396f71b9bdce18e5c92245
|
2024-06-13T09:33:14-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git curl sudo
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=vllm-build-sccache \
&& export SCCACHE_REGION=us-west-2 \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
319ad7f1d386699e94f629341c9988a926821f24
|
2024-06-13T22:36:20-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
RUN apt-get update -y \
&& apt-get install -y python3-pip git curl sudo
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-cuda.txt
# install development dependencies
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM dev AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=vllm-build-sccache \
&& export SCCACHE_REGION=us-west-2 \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS vllm-base
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-12.4/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
pip install dist/*.whl --verbose
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer modelscope
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
7c01f706418d593b3cf23d2ec9110dca7151c539
|
2024-06-29T12:47:53+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv python3-pip \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version \
&& python3 -m pip --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git curl sudo
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=vllm-build-sccache \
&& export SCCACHE_REGION=us-west-2 \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
3476ed0809ec91a3457da0cb90543133a4f4b519
|
2024-07-01T20:10:37-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv python3-pip \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version \
&& python3 -m pip --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git curl sudo
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=vllm-build-sccache \
&& export SCCACHE_REGION=us-west-2 \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
2bb0489cb3367e46e201e84ab629df535544495b
|
2024-07-16T08:13:25-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv python3-pip \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version \
&& python3 -m pip --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git curl sudo
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=vllm-build-sccache \
&& export SCCACHE_REGION=us-west-2 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
WORKDIR /vllm-workspace
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.9/flashinfer-0.0.9+cu121torch2.3-cp310-cp310-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
9ed82e7074a18e25680ab106fc846364ad97bc00
|
2024-07-19T12:10:56-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y git curl sudo
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3.10
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=vllm-build-sccache \
&& export SCCACHE_REGION=us-west-2 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git curl
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.9/flashinfer-0.0.9+cu121torch2.3-cp310-cp310-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
9042d683620a7e3fa75c953fe9cca29086ce2b9a
|
2024-07-20T04:17:24+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y git curl sudo
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3.10
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=vllm-build-sccache \
&& export SCCACHE_REGION=us-west-2 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git curl libibverbs-dev
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.9/flashinfer-0.0.9+cu121torch2.3-cp310-cp310-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
89a84b0bb7b30706a02836234a94493ea8f780bf
|
2024-07-25T21:31:31-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y git curl sudo
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3.10
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& if [ "$CUDA_VERSION" = "11.8.0" ]; then \
export SCCACHE_BUCKET=vllm-build-sccache-2; \
else \
export SCCACHE_BUCKET=vllm-build-sccache; \
fi \
&& export SCCACHE_REGION=us-west-2 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim curl libibverbs-dev
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.9/flashinfer-0.0.9+cu121torch2.3-cp310-cp310-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
a0dce9383ab7de0015060fb9fedadeb7d8ffdfb9
|
2024-07-31T14:40:44-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y git curl sudo
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3.10
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& if [ "$CUDA_VERSION" = "11.8.0" ]; then \
export SCCACHE_BUCKET=vllm-build-sccache-2; \
else \
export SCCACHE_BUCKET=vllm-build-sccache; \
fi \
&& export SCCACHE_REGION=us-west-2 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim curl libibverbs-dev
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.9/flashinfer-0.0.9+cu121torch2.3-cp310-cp310-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
6ce01f30667bbae33f112152e07a3b66b841078f
|
2024-08-01T18:29:52-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y git curl sudo
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3.10
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& if [ "$CUDA_VERSION" = "11.8.0" ]; then \
export SCCACHE_BUCKET=vllm-build-sccache-2; \
else \
export SCCACHE_BUCKET=vllm-build-sccache; \
fi \
&& export SCCACHE_REGION=us-west-2 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim curl libibverbs-dev
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
660470e5a36b8e52083615ad7c85e9b4fd4c72ce
|
2024-08-06T12:34:25-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y git curl sudo
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3.10
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& if [ "$CUDA_VERSION" = "11.8.0" ]; then \
export SCCACHE_BUCKET=vllm-build-sccache-2; \
else \
export SCCACHE_BUCKET=vllm-build-sccache; \
fi \
&& export SCCACHE_REGION=us-west-2 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim curl libibverbs-dev
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
e02ac5561748306186aaeaad6dad4c89484a2b45
|
2024-08-08T21:34:28-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y git curl sudo
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3.10
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& if [ "$CUDA_VERSION" = "11.8.0" ]; then \
export SCCACHE_BUCKET=vllm-build-sccache-2; \
else \
export SCCACHE_BUCKET=vllm-build-sccache; \
fi \
&& export SCCACHE_REGION=us-west-2 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim curl libibverbs-dev
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
fc7b8d1eefcbe837a56b7c080509417fe5167e6c
|
2024-08-09T15:49:36+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y git curl sudo
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3.10
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& if [ "$CUDA_VERSION" = "11.8.0" ]; then \
export SCCACHE_BUCKET=vllm-build-sccache-2; \
else \
export SCCACHE_BUCKET=vllm-build-sccache; \
fi \
&& export SCCACHE_REGION=us-west-2 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim curl libibverbs-dev
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.2/flashinfer-0.1.2+cu121torch2.4-cp310-cp310-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
ab7165f2c7ea358df969d68a0fb0ce9bb184a083
|
2024-08-18T01:15:10-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y git curl sudo
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG PYTHON_VERSION=3.10
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# install compiler cache to speed up compilation leveraging local or remote caching
RUN apt-get update -y && apt-get install -y ccache
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& if [ "$CUDA_VERSION" = "11.8.0" ]; then \
export SCCACHE_BUCKET=vllm-build-sccache-2; \
else \
export SCCACHE_BUCKET=vllm-build-sccache; \
fi \
&& export SCCACHE_REGION=us-west-2 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& if [ "${PYTHON_VERSION}" != "3" ]; then update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1; fi \
&& python3 --version
RUN apt-get update -y \
&& apt-get install -y python3-pip git vim curl libibverbs-dev
# Install pip s.t. it will be compatible with our PYTHON_VERSION
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION}
RUN python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.3/flashinfer-0.1.3+cu121torch2.4-cp310-cp310-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
2deb029d115dadd012ce5ea70487a207cb025493
|
2024-08-26T11:24:53-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.4/flashinfer-0.1.4+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
e3580537a41a46b0f3cd750b86b633c1857a8c90
|
2024-08-28T00:36:31-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
COPY requirements-mamba.txt requirements-mamba.txt
RUN python3 -m pip install packaging
RUN python3 -m pip install -r requirements-mamba.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### MAMBA Build IMAGE ####################
FROM dev as mamba-builder
# max jobs used for build
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
WORKDIR /usr/src/mamba
COPY requirements-mamba.txt requirements-mamba.txt
# Download the wheel or build it if a pre-compiled release doesn't exist
RUN pip --verbose wheel -r requirements-mamba.txt \
--no-build-isolation --no-deps --no-cache-dir
#################### MAMBA Build IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=bind,from=mamba-builder,src=/usr/src/mamba,target=/usr/src/mamba \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install /usr/src/mamba/*.whl --no-cache-dir
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.4/flashinfer-0.1.4+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
ce6bf3a2cff4860c5661cac2280e0a28bedb6440
|
2024-08-28T16:10:12-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.4/flashinfer-0.1.4+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
6e36f4fa6ce64619b9ea94c88a157f5783a63a65
|
2024-09-02T14:20:12-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
bd852f2a8b9e9129de69fa7349906a9115538d5a
|
2024-09-03T10:49:18-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
6d646d08a2e0e73e83e313a5ae470c1f9e4f200e
|
2024-09-03T18:50:29+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# files and directories related to build wheels
COPY csrc csrc
COPY setup.py setup.py
COPY cmake cmake
COPY CMakeLists.txt CMakeLists.txt
COPY requirements-common.txt requirements-common.txt
COPY requirements-adag.txt requirements-adag.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY pyproject.toml pyproject.toml
COPY vllm vllm
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# check the size of the wheel, we cannot upload wheels larger than 100MB
COPY .buildkite/check-wheel-size.py check-wheel-size.py
RUN python3 check-wheel-size.py dist
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu20.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.10
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0'
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
83450458339b07765b0e72a822e5fe93eeaf5258
|
2024-10-16T13:37:45-06:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# files and directories related to build wheels
COPY . .
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0' bitsandbytes>=0.44.0 timm==0.9.10
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
81ede99ca44a5b3518932a07ea4a76a719e7416e
|
2024-10-17T11:38:15-05:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
# files and directories related to build wheels
COPY . .
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0' bitsandbytes>=0.44.0 timm==0.9.10
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
b2e0ad3b598ed0e022cdbd678a20821d411873c2
|
2024-11-15T00:38:20+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.44.0' timm==0.9.10
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
8c1e77fb585c4f42783a3d88c1efc7c9e15fd89f
|
2024-11-28T08:31:28-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.44.0' timm==0.9.10
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
98f47f2a4032f8c395268de80858c64ffcfc60fa
|
2024-11-28T09:01:02-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.44.0' timm==0.9.10
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
9323a3153b20d4a2ca7ac04a2784609d6ce656e0
|
2024-12-03T15:17:00+08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.44.0' timm==0.9.10
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
3b61cb450d899dc423feb264c297d4d18d701678
|
2024-12-09T12:38:46-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.44.0' timm==0.9.10
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
f092153fbe349a9a1742940e3703bfcff6aa0a6d
|
2024-12-11T23:14:20-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.44.0' timm==0.9.10
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
886936837ca89e5645bc1f71cc0e1492b65b1590
|
2024-12-14T11:38:10-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.44.0' timm==0.9.10
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
25ebed2f8ca6d747d63f2be9ede023c561851ac8
|
2024-12-15T13:33:00-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# install vllm wheel first, so that torch etc will be installed
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' timm==0.9.10
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
f26c4aeecba481ce1445be7a998b0b97460a13bb
|
2024-12-18T23:38:02-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.rst and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
COPY requirements-cuda-arm64.txt requirements-cuda-arm64.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install -r requirements-cuda-arm64.txt; \
fi
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install -r requirements-cuda-arm64.txt; \
fi
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
COPY requirements-cuda-arm64.txt requirements-cuda-arm64.txt
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip uninstall -y torch && \
python3 -m pip install -r requirements-cuda-arm64.txt; \
fi
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10'; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10'; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
b55ed6ef8ab0dce7fb0f79ff292dafdb4d22610c
|
2025-01-02T12:04:58-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/dev/dockerfile/dockerfile.md and
# docs/source/assets/dev/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# openai api server alternative
FROM vllm-base AS vllm-openai
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
526de822d501c792b051c864ba873a836d78d5bf
|
2025-01-08T20:23:15+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
310aca88c984983189a57f1b72e3b1dde89fb92f
|
2025-01-09T07:18:21+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
3127e975fb9417d10513e25b80820870f594c627
|
2025-01-20T17:36:24+08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
aea94362c9bdd08ed2b346701bdc09d278e85f66
|
2025-01-22T22:22:12+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
6dd94dbe94c1820a1e224cba65efcf0befa97995
|
2025-01-24T11:34:27+08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# Default max size of the wheel is 250MB
ARG VLLM_MAX_SIZE_MB=250
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
fa63e710c7fbaae3a445f669d3b5ba6b9a4ef412
|
2025-01-26T00:42:37-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=300
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.1.6/flashinfer-0.1.6+cu121torch2.4-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
fc542144c4477ffec1d3de6fa43e54f8fb5351e8
|
2025-01-31T15:37:30-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=300
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
# How to build this FlashInfer wheel:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.0.post1-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
baeded25699f9f4851843306f27f685c4d4ee7c5
|
2025-01-31T21:52:51-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=300
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
# How to build this FlashInfer wheel:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.0.post1-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
b9986454fe8ba80e2a109d069397b6b59aae658b
|
2025-02-03T13:46:19+08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=300
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
# How to build this FlashInfer wheel:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.0.post1-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
5e5c8e091eacc16672a0a8265eb5cb0ece85d24b
|
2025-02-14T12:53:42-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
# How to build this FlashInfer wheel:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.0.post1-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
30172b4947c52890b808c6da3a6c7580f55cbb74
|
2025-02-18T12:15:33-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
4c822298981a8f7521492075ff72659985fc4c3f
|
2025-02-18T13:19:58-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
0d243f2a54fbd1c56da8a571f0899c30b6aba5d9
|
2025-02-20T04:01:02+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
python3 -m pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
python3 -m pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
6a417b8600d4d1e57698a91b71a38446e8fc5c45
|
2025-02-20T10:59:36-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install uv
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/pip \
uv pip install --system -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
uv pip install --system -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/pip \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/pip \
uv pip install --system -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install uv
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/pip \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/pip \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/pip \
uv pip install --system -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
uv pip install --system -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/pip \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/pip \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
e206b5433109d298e53451015465b2bf8f03ef0a
|
2025-02-26T14:58:24+08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
19d98e0c7db96713f0e2201649159431177a56e2
|
2025-03-03T16:29:53-05:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
9badee53decb3d432dc805336abfb0eb81dfb48f
|
2025-03-04T20:59:22+01:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
9f1710f1ace3535920c0bb6d4cc329c36289080e
|
2025-03-06T09:35:49-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
dae68969774e41b93b01cd31171ca033a92b574a
|
2025-03-06T19:59:14-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install minimal dependencies and uv
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache git curl wget sudo \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install minimal dependencies and uv
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache git curl wget sudo vim \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 libibverbs-dev \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
1e3598edeb694c1a6a1fdfac5e7d12697749561b
|
2025-03-07T13:25:13+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install minimal dependencies and uv
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache git curl wget sudo \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install minimal dependencies and uv
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache git curl wget sudo vim \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 libibverbs-dev \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
ca7a2d5f28eac9621474563cdda0e08596222755
|
2025-03-07T22:18:53-08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install minimal dependencies and uv
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache git curl wget sudo \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements-common.txt requirements-common.txt
COPY requirements-cuda.txt requirements-cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements-build.txt requirements-build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements-lint.txt requirements-lint.txt
COPY requirements-test.txt requirements-test.txt
COPY requirements-dev.txt requirements-dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install minimal dependencies and uv
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache git curl wget sudo vim \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 libibverbs-dev \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements-build.txt requirements-build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements-dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
fb0acb6c72874e98617cabee4ff4851569374fc9
|
2025-03-10T12:06:58-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo vim \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 libibverbs-dev \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
70b808fe1a63322bc6bf5f46a91981a8f6b8af00
|
2025-03-11T07:39:56+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo vim \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 libibverbs-dev \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
fe66b34728e5d383e3d19aefc544eeee808c99fb
|
2025-03-14T16:36:18-04:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo vim \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 libibverbs-dev \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
ccf02fcbaebb1a5b59dfc6c7cb64aa7cc489f04c
|
2025-03-14T20:45:42-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo vim \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 libibverbs-dev \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
99abb8b650c66664cdc84d815b7f306f33bd9881
|
2025-03-18T14:31:54-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo vim \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 libibverbs-dev \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
22d33baca2c0c639cfd45c48e99803e56c3efa74
|
2025-03-19T21:04:41+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo vim \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 libibverbs-dev \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
296f927f2493908984707354e3cc5d7b2e41650b
|
2025-03-20T19:21:08-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121"; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install minimal dependencies and uv
RUN apt-get update -y \
&& apt-get install -y ccache git curl wget sudo vim \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 libibverbs-dev \
&& curl -LsSf https://astral.sh/uv/install.sh | sh
# Add uv to PATH
ENV PATH="/root/.local/bin:$PATH"
# Create venv with specified Python and activate by placing at the front of path
ENV VIRTUAL_ENV="/opt/venv"
RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215"; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
9d72daf4ced05a5fec1ad8ea2914a39296f402da
|
2025-03-24T22:44:08+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
35fad35a485eac9195c510731ba4a9d297dfd963
|
2025-03-26T10:56:47-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
b10e51989551cd80dd74079429ccf91f0807bd92
|
2025-04-06T20:48:14+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
bd6028d6b0bbc0c569ece0535067081c5e8bdc14
|
2025-04-12T14:21:08+08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
93e5f3c5fb4a4bbd49610efb96aad30df95fca66
|
2025-04-12T22:54:37+08:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
3092375e274e9e003961e600e10a6192d33ceaa0
|
2025-04-16T19:28:32-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
COPY benchmarks benchmarks
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
05fcd1b4308aa2dca9a1b24c540b57a94a7ba124
|
2025-04-17T07:45:24-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
COPY benchmarks benchmarks
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
299ebb62b269ce167eb1c71b5e39a1dc1f65ce1c
|
2025-04-21T18:18:22+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
COPY benchmarks benchmarks
COPY ./vllm/collect_env.py .
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
e4d614423283d8beae4a92aeb537a49ad2662864
|
2025-04-22T08:16:19+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
COPY benchmarks benchmarks
COPY ./vllm/collect_env.py .
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
bc7c4d206bbfb56b06d218b6c2971e8ca191db36
|
2025-04-22T19:11:56-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
COPY benchmarks benchmarks
COPY ./vllm/collect_env.py .
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
41ca7eb49192cbee65986527319c6eb929d6aa7c
|
2025-04-24T20:12:21-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.4.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system mamba-ssm==2.2.4 --no-build-isolation
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.4.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
fi
COPY examples examples
COPY benchmarks benchmarks
COPY ./vllm/collect_env.py .
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install development dependencies (for testing)
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system mamba-ssm==2.2.4 --no-build-isolation
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
015069b01741e9ecb9e604c7fe87fbdfc306ebe5
|
2025-05-01T03:29:01-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.8.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
# TESTING: install FlashInfer from source to test 2.7.0 final RC
FLASHINFER_ENABLE_AOT=1 TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX' \
uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/[email protected]" ; \
fi
COPY examples examples
COPY benchmarks benchmarks
COPY ./vllm/collect_env.py .
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
uv pip list
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
ARG TARGETPLATFORM
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
022afbeb4efa22bb8a4656a2712cd66c6a811c23
|
2025-05-07T00:36:41+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.8.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
# TESTING: install FlashInfer from source to test 2.7.0 final RC
FLASHINFER_ENABLE_AOT=1 TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX' \
uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/[email protected]" ; \
fi
COPY examples examples
COPY benchmarks benchmarks
COPY ./vllm/collect_env.py .
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
uv pip list
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
ARG TARGETPLATFORM
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
67da5720d4ed2aa1f615ec812031f4f3753b3f62
|
2025-05-15T23:31:02-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.8.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
# TESTING: install FlashInfer from source to test 2.7.0 final RC
FLASHINFER_ENABLE_AOT=1 TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX' \
uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/[email protected]" ; \
fi
COPY examples examples
COPY benchmarks benchmarks
COPY ./vllm/collect_env.py .
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
uv pip list
# Although we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN mv vllm test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
ARG TARGETPLATFORM
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
e493e48524e9e78ab33eafec6461b3940e361189
|
2025-05-23T03:38:23-07:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/source/contributing/dockerfile/dockerfile.md and
# docs/source/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.8.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0 10.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
SHELL ["/bin/bash", "-c"]
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
# uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.4/flashinfer_python-0.2.4+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
# TESTING: install FlashInfer from source to test 2.7.0 final RC
if [[ "$CUDA_VERSION" == 12.8* ]]; then \
export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0 10.0+PTX'; \
else \
export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0+PTX'; \
fi; \
CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
if [ "$CUDA_MAJOR" -lt 12 ]; then \
export FLASHINFER_ENABLE_SM90=0; \
fi; \
uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/flashinfer@21ea1d2545f74782b91eb8c08fd503ac4c0743fc" ; \
fi
COPY examples examples
COPY benchmarks benchmarks
COPY ./vllm/collect_env.py .
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
uv pip list
# Even when we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
if [ "$CUDA_MAJOR" -ge 12 ]; then \
uv pip install --system -r requirements/dev.txt; \
fi
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN cp -r examples test_docs/
RUN mv vllm test_docs/
RUN mv mkdocs.yaml test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
ARG TARGETPLATFORM
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
d55e446d1320d0f5f22bc3584f81f18d7924f166
|
2025-05-24T06:51:22+00:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/contributing/dockerfile/dockerfile.md and
# docs/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.8.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0 10.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
SHELL ["/bin/bash", "-c"]
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
# FlashInfer alreary has a wheel for PyTorch 2.7.0 and CUDA 12.8. This is enough for CI use
if [[ "$CUDA_VERSION" == 12.8* ]]; then \
uv pip install --system https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.5%2Bcu128torch2.7-cp38-abi3-linux_x86_64.whl; \
else \
export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0+PTX'; \
CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
if [ "$CUDA_MAJOR" -lt 12 ]; then \
export FLASHINFER_ENABLE_SM90=0; \
fi; \
uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/flashinfer@21ea1d2545f74782b91eb8c08fd503ac4c0743fc" ; \
fi \
fi
COPY examples examples
COPY benchmarks benchmarks
COPY ./vllm/collect_env.py .
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
uv pip list
# Even when we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
if [ "$CUDA_MAJOR" -ge 12 ]; then \
uv pip install --system -r requirements/dev.txt; \
fi
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN cp -r examples test_docs/
RUN mv vllm test_docs/
RUN mv mkdocs.yaml test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
ARG TARGETPLATFORM
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
e7523c2e031bc96740723ab63833d1cf94229ab4
|
2025-05-26T11:49:36-04:00
|
# The vLLM Dockerfile is used to construct vLLM image that can be directly used
# to run the OpenAI compatible server.
# Please update any changes made here to
# docs/contributing/dockerfile/dockerfile.md and
# docs/assets/contributing/dockerfile-stages-dependency.png
ARG CUDA_VERSION=12.8.1
#################### BASE BUILD IMAGE ####################
# prepare basic build environment
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu20.04 AS base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
ARG TARGETPLATFORM
ENV DEBIAN_FRONTEND=noninteractive
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl sudo \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
# as it was causing spam when compiling the CUTLASS kernels
RUN apt-get install -y gcc-10 g++-10
RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 110 --slave /usr/bin/g++ g++ /usr/bin/g++-10
RUN <<EOF
gcc --version
EOF
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
WORKDIR /workspace
# install build and runtime dependencies
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
COPY requirements/common.txt requirements/common.txt
COPY requirements/cuda.txt requirements/cuda.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/cuda.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# cuda arch list used by torch
# can be useful for both `dev` and `test`
# explicitly set the list to avoid issues with torch 2.2
# see https://github.com/pytorch/pytorch/pull/123243
ARG torch_cuda_arch_list='7.0 7.5 8.0 8.9 9.0 10.0+PTX'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
# Override the arch list for flash-attn to reduce the binary size
ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
#################### BASE BUILD IMAGE ####################
#################### WHEEL BUILD IMAGE ####################
FROM base AS build
ARG TARGETPLATFORM
# install build dependencies
COPY requirements/build.txt requirements/build.txt
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
COPY . .
ARG GIT_REPO_CHECK=0
RUN --mount=type=bind,source=.git,target=.git \
if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
# max jobs used by Ninja to build extensions
ARG max_jobs=2
ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
ARG USE_SCCACHE
ARG SCCACHE_BUCKET_NAME=vllm-build-sccache
ARG SCCACHE_REGION_NAME=us-west-2
ARG SCCACHE_S3_NO_CREDENTIALS=0
# if USE_SCCACHE is set, use sccache to speed up compilation
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
&& export SCCACHE_IDLE_TIMEOUT=0 \
&& export CMAKE_BUILD_TYPE=Release \
&& sccache --show-stats \
&& python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38 \
&& sccache --show-stats; \
fi
ENV CCACHE_DIR=/root/.cache/ccache
RUN --mount=type=cache,target=/root/.cache/ccache \
--mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" != "1" ]; then \
# Clean any existing CMake artifacts
rm -rf .deps && \
mkdir -p .deps && \
python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
fi
# Check the size of the wheel if RUN_WHEEL_CHECK is true
COPY .buildkite/check-wheel-size.py check-wheel-size.py
# sync the default value with .buildkite/check-wheel-size.py
ARG VLLM_MAX_SIZE_MB=400
ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
ARG RUN_WHEEL_CHECK=true
RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
python3 check-wheel-size.py dist; \
else \
echo "Skipping wheel size check."; \
fi
#################### EXTENSION Build IMAGE ####################
#################### DEV IMAGE ####################
FROM base as dev
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
COPY requirements/lint.txt requirements/lint.txt
COPY requirements/test.txt requirements/test.txt
COPY requirements/dev.txt requirements/dev.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/dev.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### DEV IMAGE ####################
#################### vLLM installation IMAGE ####################
# image with vLLM installed
# TODO: Restore to base image after FlashInfer AOT wheel fixed
FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 AS vllm-base
ARG CUDA_VERSION=12.8.1
ARG PYTHON_VERSION=3.12
WORKDIR /vllm-workspace
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETPLATFORM
SHELL ["/bin/bash", "-c"]
RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
# Install Python and other dependencies
RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
&& echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
&& apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
&& apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
&& for i in 1 2 3; do \
add-apt-repository -y ppa:deadsnakes/ppa && break || \
{ echo "Attempt $i failed, retrying in 5s..."; sleep 5; }; \
done \
&& apt-get update -y \
&& apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
&& update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
&& update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
&& python3 --version && python3 -m pip --version
# Install uv for faster pip installs
RUN --mount=type=cache,target=/root/.cache/uv \
python3 -m pip install uv
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# arm64 (GH200) build follows the practice of "use existing pytorch" build,
# we need to install torch and torchvision from the nightly builds first,
# pytorch will not appear as a vLLM dependency in all of the following steps
# after this step
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 "torch==2.8.0.dev20250318+cu128" "torchvision==0.22.0.dev20250319"; \
uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu128 --pre pytorch_triton==3.3.0+gitab727c40; \
fi
# Install vllm wheel first, so that torch etc will be installed.
RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
--mount=type=cache,target=/root/.cache/uv \
uv pip install --system dist/*.whl --verbose \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
# If we need to build FlashInfer wheel before its release:
# $ export FLASHINFER_ENABLE_AOT=1
# $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
# $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
# $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
# $ cd flashinfer
# $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
# $ rm -rf build
# $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
# $ ls dist
# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
# FlashInfer alreary has a wheel for PyTorch 2.7.0 and CUDA 12.8. This is enough for CI use
if [[ "$CUDA_VERSION" == 12.8* ]]; then \
uv pip install --system https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.5%2Bcu128torch2.7-cp38-abi3-linux_x86_64.whl; \
else \
export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0+PTX'; \
CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
if [ "$CUDA_MAJOR" -lt 12 ]; then \
export FLASHINFER_ENABLE_SM90=0; \
fi; \
uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/flashinfer@21ea1d2545f74782b91eb8c08fd503ac4c0743fc" ; \
fi \
fi
COPY examples examples
COPY benchmarks benchmarks
COPY ./vllm/collect_env.py .
RUN --mount=type=cache,target=/root/.cache/uv \
. /etc/environment && \
uv pip list
# Even when we build Flashinfer with AOT mode, there's still
# some issues w.r.t. JIT compilation. Therefore we need to
# install build dependencies for JIT compilation.
# TODO: Remove this once FlashInfer AOT wheel is fixed
COPY requirements/build.txt requirements/build.txt
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt \
--extra-index-url https://download.pytorch.org/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')
#################### vLLM installation IMAGE ####################
#################### TEST IMAGE ####################
# image to run unit testing suite
# note that this uses vllm installed by `pip`
FROM vllm-base AS test
ADD . /vllm-workspace/
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Workaround for #17068
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
CUDA_MAJOR="${CUDA_VERSION%%.*}"; \
if [ "$CUDA_MAJOR" -ge 12 ]; then \
uv pip install --system -r requirements/dev.txt; \
fi
# install development dependencies (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -e tests/vllm_test_utils
# enable fast downloads from hf (for testing)
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system hf_transfer
ENV HF_HUB_ENABLE_HF_TRANSFER 1
# Copy in the v1 package for testing (it isn't distributed yet)
COPY vllm/v1 /usr/local/lib/python3.12/dist-packages/vllm/v1
# doc requires source code
# we hide them inside `test_docs/` , so that this source code
# will not be imported by other tests
RUN mkdir test_docs
RUN mv docs test_docs/
RUN cp -r examples test_docs/
RUN mv vllm test_docs/
RUN mv mkdocs.yaml test_docs/
#################### TEST IMAGE ####################
#################### OPENAI API SERVER ####################
# base openai image with additional requirements, for any subsequent openai-style images
FROM vllm-base AS vllm-openai-base
ARG TARGETPLATFORM
# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
# Reference: https://github.com/astral-sh/uv/pull/1694
ENV UV_HTTP_TIMEOUT=500
# install additional dependencies for openai api server
RUN --mount=type=cache,target=/root/.cache/uv \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
else \
uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.3' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
fi
ENV VLLM_USAGE_SOURCE production-docker-image
# define sagemaker first, so it is not default from `docker build`
FROM vllm-openai-base AS vllm-sagemaker
COPY examples/online_serving/sagemaker-entrypoint.sh .
RUN chmod +x sagemaker-entrypoint.sh
ENTRYPOINT ["./sagemaker-entrypoint.sh"]
FROM vllm-openai-base AS vllm-openai
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
#################### OPENAI API SERVER ####################
|
nvidia
|
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