minimal_client

#!/usr/bin/env python
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import argparse
import numpy as np

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

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-u',
                        '--url',
                        type=str,
                        required=False,
                        default='localhost:8000',
                        help='Inference server URL. Default is localhost:8000.')
    FLAGS = parser.parse_args()

    # For the HTTP client, need to specify large enough concurrency to
    # issue all the inference requests to the server in parallel. For
    # this example we want to be able to send 2 requests concurrently.
    try:
        concurrent_request_count = 2
        triton_client = httpclient.InferenceServerClient(
            url=FLAGS.url, concurrency=concurrent_request_count)
    except Exception as e:
        print("channel creation failed: " + str(e))
        sys.exit(1)

    # First send a single request to the nonbatching model.
    print('=========')
    input0_data = np.array([ 1, 2, 3, 4 ], dtype=np.int32)
    print('Sending request to nonbatching model: IN0 = {}'.format(input0_data))

    inputs = [ httpclient.InferInput('IN0', [4], "INT32") ]
    inputs[0].set_data_from_numpy(input0_data)
    result = triton_client.infer('nonbatching', inputs)

    print('Response: {}'.format(result.get_response()))
    print('OUT0 = {}'.format(result.as_numpy('OUT0')))

    # Send 2 requests to the batching model. Because these are sent
    # asynchronously and Triton's dynamic batcher is configured to
    # delay up to 5 seconds when forming a batch for this model, we
    # expect these 2 requests to be batched within Triton and sent to
    # the minimal backend as a single batch.
    print('\n=========')
    async_requests = []

    input0_data = np.array([[ 10, 11, 12, 13 ]], dtype=np.int32)
    print('Sending request to batching model: IN0 = {}'.format(input0_data))
    inputs = [ httpclient.InferInput('IN0', [1, 4], "INT32") ]
    inputs[0].set_data_from_numpy(input0_data)
    async_requests.append(triton_client.async_infer('batching', inputs))

    input0_data = np.array([[ 20, 21, 22, 23 ]], dtype=np.int32)
    print('Sending request to batching model: IN0 = {}'.format(input0_data))
    inputs = [ httpclient.InferInput('IN0', [1, 4], "INT32") ]
    inputs[0].set_data_from_numpy(input0_data)
    async_requests.append(triton_client.async_infer('batching', inputs))

    for async_request in async_requests:
        # Get the result from the initiated asynchronous inference
        # request. This call will block till the server responds.
        result = async_request.get_result()
        print('Response: {}'.format(result.get_response()))
        print('OUT0 = {}'.format(result.as_numpy('OUT0')))