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| import os | |
| from pathlib import Path | |
| import pandas as pd | |
| from trackio import deploy, utils | |
| from trackio.sqlite_storage import SQLiteStorage | |
| def import_csv( | |
| csv_path: str | Path, | |
| project: str, | |
| name: str | None = None, | |
| space_id: str | None = None, | |
| dataset_id: str | None = None, | |
| ) -> None: | |
| """ | |
| Imports a CSV file into a Trackio project. The CSV file must contain a `"step"` | |
| column, may optionally contain a `"timestamp"` column, and any other columns will be | |
| treated as metrics. It should also include a header row with the column names. | |
| TODO: call init() and return a Run object so that the user can continue to log metrics to it. | |
| Args: | |
| csv_path (`str` or `Path`): | |
| The str or Path to the CSV file to import. | |
| project (`str`): | |
| The name of the project to import the CSV file into. Must not be an existing | |
| project. | |
| name (`str` or `None`, *optional*, defaults to `None`): | |
| The name of the Run to import the CSV file into. If not provided, a default | |
| name will be generated. | |
| name (`str` or `None`, *optional*, defaults to `None`): | |
| The name of the run (if not provided, a default name will be generated). | |
| space_id (`str` or `None`, *optional*, defaults to `None`): | |
| If provided, the project will be logged to a Hugging Face Space instead of a | |
| local directory. Should be a complete Space name like `"username/reponame"` | |
| or `"orgname/reponame"`, or just `"reponame"` in which case the Space will | |
| be created in the currently-logged-in Hugging Face user's namespace. If the | |
| Space does not exist, it will be created. If the Space already exists, the | |
| project will be logged to it. | |
| dataset_id (`str` or `None`, *optional*, defaults to `None`): | |
| If provided, a persistent Hugging Face Dataset will be created and the | |
| metrics will be synced to it every 5 minutes. Should be a complete Dataset | |
| name like `"username/datasetname"` or `"orgname/datasetname"`, or just | |
| `"datasetname"` in which case the Dataset will be created in the | |
| currently-logged-in Hugging Face user's namespace. If the Dataset does not | |
| exist, it will be created. If the Dataset already exists, the project will | |
| be appended to it. If not provided, the metrics will be logged to a local | |
| SQLite database, unless a `space_id` is provided, in which case a Dataset | |
| will be automatically created with the same name as the Space but with the | |
| `"_dataset"` suffix. | |
| """ | |
| if SQLiteStorage.get_runs(project): | |
| raise ValueError( | |
| f"Project '{project}' already exists. Cannot import CSV into existing project." | |
| ) | |
| csv_path = Path(csv_path) | |
| if not csv_path.exists(): | |
| raise FileNotFoundError(f"CSV file not found: {csv_path}") | |
| df = pd.read_csv(csv_path) | |
| if df.empty: | |
| raise ValueError("CSV file is empty") | |
| column_mapping = utils.simplify_column_names(df.columns.tolist()) | |
| df = df.rename(columns=column_mapping) | |
| step_column = None | |
| for col in df.columns: | |
| if col.lower() == "step": | |
| step_column = col | |
| break | |
| if step_column is None: | |
| raise ValueError("CSV file must contain a 'step' or 'Step' column") | |
| if name is None: | |
| name = csv_path.stem | |
| metrics_list = [] | |
| steps = [] | |
| timestamps = [] | |
| numeric_columns = [] | |
| for column in df.columns: | |
| if column == step_column: | |
| continue | |
| if column == "timestamp": | |
| continue | |
| try: | |
| pd.to_numeric(df[column], errors="raise") | |
| numeric_columns.append(column) | |
| except (ValueError, TypeError): | |
| continue | |
| for _, row in df.iterrows(): | |
| metrics = {} | |
| for column in numeric_columns: | |
| value = row[column] | |
| if bool(pd.notna(value)): | |
| metrics[column] = float(value) | |
| if metrics: | |
| metrics_list.append(metrics) | |
| steps.append(int(row[step_column])) | |
| if "timestamp" in df.columns and bool(pd.notna(row["timestamp"])): | |
| timestamps.append(str(row["timestamp"])) | |
| else: | |
| timestamps.append("") | |
| if metrics_list: | |
| SQLiteStorage.bulk_log( | |
| project=project, | |
| run=name, | |
| metrics_list=metrics_list, | |
| steps=steps, | |
| timestamps=timestamps, | |
| ) | |
| print( | |
| f"* Imported {len(metrics_list)} rows from {csv_path} into project '{project}' as run '{name}'" | |
| ) | |
| print(f"* Metrics found: {', '.join(metrics_list[0].keys())}") | |
| space_id, dataset_id = utils.preprocess_space_and_dataset_ids(space_id, dataset_id) | |
| if dataset_id is not None: | |
| os.environ["TRACKIO_DATASET_ID"] = dataset_id | |
| print(f"* Trackio metrics will be synced to Hugging Face Dataset: {dataset_id}") | |
| if space_id is None: | |
| utils.print_dashboard_instructions(project) | |
| else: | |
| deploy.create_space_if_not_exists(space_id, dataset_id) | |
| deploy.wait_until_space_exists(space_id) | |
| deploy.upload_db_to_space(project, space_id) | |
| print( | |
| f"* View dashboard by going to: {deploy.SPACE_URL.format(space_id=space_id)}" | |
| ) | |
| def import_tf_events( | |
| log_dir: str | Path, | |
| project: str, | |
| name: str | None = None, | |
| space_id: str | None = None, | |
| dataset_id: str | None = None, | |
| ) -> None: | |
| """ | |
| Imports TensorFlow Events files from a directory into a Trackio project. Each | |
| subdirectory in the log directory will be imported as a separate run. | |
| Args: | |
| log_dir (`str` or `Path`): | |
| The str or Path to the directory containing TensorFlow Events files. | |
| project (`str`): | |
| The name of the project to import the TensorFlow Events files into. Must not | |
| be an existing project. | |
| name (`str` or `None`, *optional*, defaults to `None`): | |
| The name prefix for runs (if not provided, will use directory names). Each | |
| subdirectory will create a separate run. | |
| space_id (`str` or `None`, *optional*, defaults to `None`): | |
| If provided, the project will be logged to a Hugging Face Space instead of a | |
| local directory. Should be a complete Space name like `"username/reponame"` | |
| or `"orgname/reponame"`, or just `"reponame"` in which case the Space will | |
| be created in the currently-logged-in Hugging Face user's namespace. If the | |
| Space does not exist, it will be created. If the Space already exists, the | |
| project will be logged to it. | |
| dataset_id (`str` or `None`, *optional*, defaults to `None`): | |
| If provided, a persistent Hugging Face Dataset will be created and the | |
| metrics will be synced to it every 5 minutes. Should be a complete Dataset | |
| name like `"username/datasetname"` or `"orgname/datasetname"`, or just | |
| `"datasetname"` in which case the Dataset will be created in the | |
| currently-logged-in Hugging Face user's namespace. If the Dataset does not | |
| exist, it will be created. If the Dataset already exists, the project will | |
| be appended to it. If not provided, the metrics will be logged to a local | |
| SQLite database, unless a `space_id` is provided, in which case a Dataset | |
| will be automatically created with the same name as the Space but with the | |
| `"_dataset"` suffix. | |
| """ | |
| try: | |
| from tbparse import SummaryReader | |
| except ImportError: | |
| raise ImportError( | |
| "The `tbparse` package is not installed but is required for `import_tf_events`. Please install trackio with the `tensorboard` extra: `pip install trackio[tensorboard]`." | |
| ) | |
| if SQLiteStorage.get_runs(project): | |
| raise ValueError( | |
| f"Project '{project}' already exists. Cannot import TF events into existing project." | |
| ) | |
| path = Path(log_dir) | |
| if not path.exists(): | |
| raise FileNotFoundError(f"TF events directory not found: {path}") | |
| # Use tbparse to read all tfevents files in the directory structure | |
| reader = SummaryReader(str(path), extra_columns={"dir_name"}) | |
| df = reader.scalars | |
| if df.empty: | |
| raise ValueError(f"No TensorFlow events data found in {path}") | |
| total_imported = 0 | |
| imported_runs = [] | |
| # Group by dir_name to create separate runs | |
| for dir_name, group_df in df.groupby("dir_name"): | |
| try: | |
| # Determine run name based on directory name | |
| if dir_name == "": | |
| run_name = "main" # For files in the root directory | |
| else: | |
| run_name = dir_name # Use directory name | |
| if name: | |
| run_name = f"{name}_{run_name}" | |
| if group_df.empty: | |
| print(f"* Skipping directory {dir_name}: no scalar data found") | |
| continue | |
| metrics_list = [] | |
| steps = [] | |
| timestamps = [] | |
| for _, row in group_df.iterrows(): | |
| # Convert row values to appropriate types | |
| tag = str(row["tag"]) | |
| value = float(row["value"]) | |
| step = int(row["step"]) | |
| metrics = {tag: value} | |
| metrics_list.append(metrics) | |
| steps.append(step) | |
| # Use wall_time if present, else fallback | |
| if "wall_time" in group_df.columns and not bool( | |
| pd.isna(row["wall_time"]) | |
| ): | |
| timestamps.append(str(row["wall_time"])) | |
| else: | |
| timestamps.append("") | |
| if metrics_list: | |
| SQLiteStorage.bulk_log( | |
| project=project, | |
| run=str(run_name), | |
| metrics_list=metrics_list, | |
| steps=steps, | |
| timestamps=timestamps, | |
| ) | |
| total_imported += len(metrics_list) | |
| imported_runs.append(run_name) | |
| print( | |
| f"* Imported {len(metrics_list)} scalar events from directory '{dir_name}' as run '{run_name}'" | |
| ) | |
| print(f"* Metrics in this run: {', '.join(set(group_df['tag']))}") | |
| except Exception as e: | |
| print(f"* Error processing directory {dir_name}: {e}") | |
| continue | |
| if not imported_runs: | |
| raise ValueError("No valid TensorFlow events data could be imported") | |
| print(f"* Total imported events: {total_imported}") | |
| print(f"* Created runs: {', '.join(imported_runs)}") | |
| space_id, dataset_id = utils.preprocess_space_and_dataset_ids(space_id, dataset_id) | |
| if dataset_id is not None: | |
| os.environ["TRACKIO_DATASET_ID"] = dataset_id | |
| print(f"* Trackio metrics will be synced to Hugging Face Dataset: {dataset_id}") | |
| if space_id is None: | |
| utils.print_dashboard_instructions(project) | |
| else: | |
| deploy.create_space_if_not_exists(space_id, dataset_id) | |
| deploy.wait_until_space_exists(space_id) | |
| deploy.upload_db_to_space(project, space_id) | |
| print( | |
| f"* View dashboard by going to: {deploy.SPACE_URL.format(space_id=space_id)}" | |
| ) | |