memory_growth_test.py

#!/usr/bin/env python3
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import argparse
import numpy as np
import sys

import tritonclient.grpc as grpcclient
import tritonclient.http as httpclient

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-v',
                        '--verbose',
                        action="store_true",
                        required=False,
                        default=False,
                        help='Enable verbose output')
    parser.add_argument('-i',
                        '--protocol',
                        type=str,
                        required=False,
                        default='HTTP',
                        help='Protocol (HTTP/gRPC) used to communicate with ' +
                        'the inference service. Default is HTTP.')
    parser.add_argument('-r',
                        '--repetitions',
                        type=int,
                        required=False,
                        default=100,
                        help='Number of inferences to run. Default is 100.')

    FLAGS = parser.parse_args()

    model_name = "custom_identity_int32"

    # Create the data for the input tensor.
    input0_data = np.arange(start=0, stop=16, dtype=np.int32)
    input0_data = np.expand_dims(input0_data, axis=0)

    for i in range(FLAGS.repetitions):
        if FLAGS.protocol.lower() != "grpc":
            triton_client = httpclient.InferenceServerClient(
                url="localhost:8000", verbose=FLAGS.verbose)
        else:
            triton_client = grpcclient.InferenceServerClient(
                url="localhost:8001", verbose=FLAGS.verbose)

        # Infer
        inputs = []
        outputs = []

        if FLAGS.protocol.lower() != "grpc":
            inputs.append(httpclient.InferInput('INPUT0', [1, 16], "INT32"))
            inputs[0].set_data_from_numpy(input0_data, binary_data=True)
            outputs.append(
                httpclient.InferRequestedOutput('OUTPUT0', binary_data=True))
        else:
            inputs.append(grpcclient.InferInput('INPUT0', [1, 16], "INT32"))
            inputs[0].set_data_from_numpy(input0_data)
            outputs.append(grpcclient.InferRequestedOutput('OUTPUT0'))

        results = triton_client.infer(model_name=model_name,
                                      inputs=inputs,
                                      outputs=outputs)

        # Get the output arrays from the results and verify
        output0_data = results.as_numpy('OUTPUT0')

        if ((output0_data.dtype != input0_data.dtype) or
            (output0_data.shape != input0_data.shape) or
                not (np.array_equal(output0_data, input0_data))):
            print("incorrect output")
            sys.exit(1)