simple_http_shm_client.py

#!/usr/bin/env python
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import argparse
import numpy as np
import sys
from builtins import range

import tritonclient.http as httpclient
import tritonclient.utils.shared_memory as shm
from tritonclient import utils

FLAGS = None

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()

    try:
        triton_client = httpclient.InferenceServerClient(url=FLAGS.url,
                                                         verbose=FLAGS.verbose)
    except Exception as e:
        print("channel creation failed: " + str(e))
        sys.exit(1)

    # To make sure no shared memory regions are registered with the
    # server.
    triton_client.unregister_system_shared_memory()
    triton_client.unregister_cuda_shared_memory()

    # We use a simple model that takes 2 input tensors of 16 integers
    # each and returns 2 output tensors of 16 integers each. One
    # output tensor is the element-wise sum of the inputs and one
    # output is the element-wise difference.
    model_name = "simple"
    model_version = ""

    # 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)
    input1_data = np.ones(shape=16, dtype=np.int32)

    input_byte_size = input0_data.size * input0_data.itemsize
    output_byte_size = input_byte_size

    # Create shared memory region for output and store shared memory handle
    shm_op_handle = shm.create_shared_memory_region("output_data",
                                                    "/output_simple",
                                                    output_byte_size * 2)

    # Register shared memory region for outputs with Triton Server
    triton_client.register_system_shared_memory("output_data", "/output_simple",
                                                output_byte_size * 2)

    # Create shared memory region for input and store shared memory handle
    shm_ip_handle = shm.create_shared_memory_region("input_data",
                                                    "/input_simple",
                                                    input_byte_size * 2)

    # Put input data values into shared memory
    shm.set_shared_memory_region(shm_ip_handle, [input0_data])
    shm.set_shared_memory_region(shm_ip_handle, [input1_data],
                                 offset=input_byte_size)

    # Register shared memory region for inputs with Triton Server
    triton_client.register_system_shared_memory("input_data", "/input_simple",
                                                input_byte_size * 2)

    # Set the parameters to use data from shared memory
    inputs = []
    inputs.append(httpclient.InferInput('INPUT0', [1, 16], "INT32"))
    inputs[-1].set_shared_memory("input_data", input_byte_size)

    inputs.append(httpclient.InferInput('INPUT1', [1, 16], "INT32"))
    inputs[-1].set_shared_memory("input_data",
                                 input_byte_size,
                                 offset=input_byte_size)

    outputs = []
    outputs.append(httpclient.InferRequestedOutput('OUTPUT0', binary_data=True))
    outputs[-1].set_shared_memory("output_data", output_byte_size)

    outputs.append(httpclient.InferRequestedOutput('OUTPUT1', binary_data=True))
    outputs[-1].set_shared_memory("output_data",
                                  output_byte_size,
                                  offset=output_byte_size)

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

    # Read results from the shared memory.
    output0 = results.get_output("OUTPUT0")
    if output0 is not None:
        output0_data = shm.get_contents_as_numpy(
            shm_op_handle, utils.triton_to_np_dtype(output0['datatype']),
            output0['shape'])
    else:
        print("OUTPUT0 is missing in the response.")
        sys.exit(1)

    output1 = results.get_output("OUTPUT1")
    if output1 is not None:
        output1_data = shm.get_contents_as_numpy(shm_op_handle,
                                                 utils.triton_to_np_dtype(
                                                     output1['datatype']),
                                                 output1['shape'],
                                                 offset=output_byte_size)
    else:
        print("OUTPUT1 is missing in the response.")
        sys.exit(1)

    for i in range(16):
        print(
            str(input0_data[i]) + " + " + str(input1_data[i]) + " = " +
            str(output0_data[0][i]))
        print(
            str(input0_data[i]) + " - " + str(input1_data[i]) + " = " +
            str(output1_data[0][i]))
        if (input0_data[i] + input1_data[i]) != output0_data[0][i]:
            print("shm infer error: incorrect sum")
            sys.exit(1)
        if (input0_data[i] - input1_data[i]) != output1_data[0][i]:
            print("shm infer error: incorrect difference")
            sys.exit(1)

    print(triton_client.get_system_shared_memory_status())
    triton_client.unregister_system_shared_memory()
    assert len(shm.mapped_shared_memory_regions()) == 2
    shm.destroy_shared_memory_region(shm_ip_handle)
    shm.destroy_shared_memory_region(shm_op_handle)
    assert len(shm.mapped_shared_memory_regions()) == 0

    print('PASS: system shared memory')