simple_http_async_infer_client.py

#!/usr/bin/env python
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from functools import partial
import argparse
import numpy as np
import sys

import tritonclient.http as httpclient
from tritonclient.utils import InferenceServerException

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)