File size: 2,738 Bytes
2caa1ed b32b0ba |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
#!/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 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)
|