Added triton client, improved client and predict

scikit-learn/adaboost_regressor/client.py

@@ -0,0 +1,61 @@
+import sys
+import argparse
+import numpy as np
+
+import tritonclient.grpc as grpcclient
+import tritonclient.grpc.model_config_pb2 as mc
+
+from sklearn.datasets import fetch_california_housing
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+
+
+def make_prediction(model_server, model_name, model_version, verbose):
+    try:
+        triton_client = grpcclient.InferenceServerClient(url=model_server, verbose=verbose)
+    except Exception as e:
+        print("channel creation failed: " + str(e))
+        sys.exit(1)
+    # Infer
+    inputs = []
+    outputs = []
+    # Load the California Housing dataset
+    california = fetch_california_housing()
+    X, y = california.data, california.target
+    # Split the dataset into training and testing sets
+    _, X_test, _, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
+    input_data = X_test.astype(np.float32)
+    input_label = y_test.astype(np.float32)
+    print(f'input_data:\n{input_data[0]}')
+    print(f'input_label:\n{input_label[0]}')
+    # input_data = np.expand_dims(input_data, axis=0)
+    # Initialize the data
+    inputs.append(grpcclient.InferInput('float_input', [input_data.shape[0], input_data.shape[1]], "FP32"))
+    inputs[0].set_data_from_numpy(input_data)
+    outputs.append(grpcclient.InferRequestedOutput('variable'))
+    # Test with outputs
+    results = triton_client.infer(model_name=model_name, inputs=inputs, outputs=outputs)
+    # print("response:\n", results.get_response())
+    statistics = triton_client.get_inference_statistics(model_name=model_name)
+    # print("statistics:\n", statistics)
+    if len(statistics.model_stats) != 1:
+        print("FAILED: Inference Statistics")
+        sys.exit(1)
+    # Get the output arrays from the results
+    y_pred = results.as_numpy('variable').squeeze()
+    print(f"y_pred:\n{y_pred[0]}")
+    mse = mean_squared_error(y_test, y_pred)
+    print(f'Mean Squared Error: {mse}')
+
+
+"""
+python client.py --model_server localhost:8001 --model_name adaboost_regressor --model_version 1
+"""
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Make predictions using a specific model.")
+    parser.add_argument("--model_server", default="localhost:8001", help="The address of the model server.")
+    parser.add_argument("--model_name", default="adaboost_regressor", help="The name of the model to use.")
+    parser.add_argument("--model_version", default="1", help="The version of the model to use.")
+    parser.add_argument("--verbose", action="store_true", required=False, default=False, help='Enable verbose output')
+    args = parser.parse_args()
+    make_prediction(args.model_server, args.model_name, args.model_version, args.verbose)

scikit-learn/adaboost_regressor/predict.py

@@ -19,11 +19,13 @@ X, y = dataset.data, dataset.target
 
 # Split the dataset into training and testing sets
 _, X_test, _, y_test = train_test_split(X, y, test_size=0.25, random_state=random_seed)
+print(f'X_test:\n{X_test[0]}')
+print(f'y_test:\n{y_test[0]}')
 
 # Use the model to make predictions on the test data
 y_pred = loaded_model.predict(X_test)
+print(f'y_pred:\n{y_pred[0]}')
 
 # Score the model using mean squared error
 mse = mean_squared_error(y_test, y_pred)
-
 print(f'Mean Squared Error: {mse}')