Added gradient boosting regressor example

b7e9713 over 3 years ago

2.76 kB

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import sys
import argparse
import numpy as np

import tritonclient.grpc as grpcclient

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 gradient_boosting_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="gradient_boosting_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)

	#!/usr/bin/env python3
	# -- coding: utf-8 --

	import sys
	import argparse
	import numpy as np

	import tritonclient.grpc as grpcclient

	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 gradient_boosting_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="gradient_boosting_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)