memory_growth_test.py

#!/usr/bin/env python3
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import argparse
import sys
from functools import partial

import numpy as np

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)

    if FLAGS.protocol.lower() == "grpc":
        import tritonclient.grpc as grpcclient

        create_client = partial(
            grpcclient.InferenceServerClient,
            url="localhost:8001",
            verbose=FLAGS.verbose,
        )
        create_input = partial(grpcclient.InferInput)
        create_output = partial(grpcclient.InferRequestedOutput)
    else:
        import tritonclient.http as httpclient

        create_client = partial(
            httpclient.InferenceServerClient,
            url="localhost:8000",
            verbose=FLAGS.verbose,
        )
        create_input = partial(httpclient.InferInput)
        create_output = partial(httpclient.InferRequestedOutput)

    for i in range(FLAGS.repetitions):
        triton_client = create_client()
        # Infer
        inputs = []
        outputs = []
        inputs.append(create_input("INPUT0", [1, 16], "INT32"))
        inputs[0].set_data_from_numpy(input0_data)
        outputs.append(create_output("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)