simple_http_async_infer_client.py

#!/usr/bin/env python
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import argparse
import sys

import numpy as np
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(
        "-u",
        "--url",
        type=str,
        required=False,
        default="localhost:8000",
        help="Inference server URL. Default is localhost:8000.",
    )

    FLAGS = parser.parse_args()

    request_count = 2
    try:
        # Need to specify large enough concurrency to issue all the
        # inference requests to the server in parallel.
        triton_client = httpclient.InferenceServerClient(
            url=FLAGS.url, verbose=FLAGS.verbose, concurrency=request_count
        )
    except Exception as e:
        print("context creation failed: " + str(e))
        sys.exit()

    model_name = "simple"

    # Infer
    inputs = []
    outputs = []
    inputs.append(httpclient.InferInput("INPUT0", [1, 16], "INT32"))
    inputs.append(httpclient.InferInput("INPUT1", [1, 16], "INT32"))

    # Create the data for the two input tensors. Initialize the first
    # to unique integers and the second to all ones.
    input0_data = np.arange(start=0, stop=16, dtype=np.int32)
    input0_data = np.expand_dims(input0_data, axis=0)
    input1_data = np.ones(shape=(1, 16), dtype=np.int32)

    # Initialize the data
    inputs[0].set_data_from_numpy(input0_data, binary_data=True)
    inputs[1].set_data_from_numpy(input1_data, binary_data=True)

    outputs.append(httpclient.InferRequestedOutput("OUTPUT0", binary_data=True))
    outputs.append(httpclient.InferRequestedOutput("OUTPUT1", binary_data=True))
    async_requests = []

    for i in range(request_count):
        # Asynchronous inference call.
        async_requests.append(
            triton_client.async_infer(
                model_name=model_name, inputs=inputs, outputs=outputs
            )
        )

    for async_request in async_requests:
        # Get the result from the initiated asynchronous inference request.
        # Note the call will block till the server responds.
        result = async_request.get_result()

        print(result.get_response())
        # Validate the results by comparing with precomputed values.
        output0_data = result.as_numpy("OUTPUT0")
        output1_data = result.as_numpy("OUTPUT1")
        for i in range(16):
            print(
                str(input0_data[0][i])
                + " + "
                + str(input1_data[0][i])
                + " = "
                + str(output0_data[0][i])
            )
            print(
                str(input0_data[0][i])
                + " - "
                + str(input1_data[0][i])
                + " = "
                + str(output1_data[0][i])
            )
            if (input0_data[0][i] + input1_data[0][i]) != output0_data[0][i]:
                print("async infer error: incorrect sum")
                sys.exit(1)
            if (input0_data[0][i] - input1_data[0][i]) != output1_data[0][i]:
                print("async infer error: incorrect difference")
                sys.exit(1)