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simple_http_cudashm_client.py
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simple_http_cudashm_client.py
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#!/usr/bin/env python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import argparse
import numpy as np
import sys
from builtins import range
import tritonclient.http as httpclient
import tritonclient.utils.cuda_shared_memory as cudashm
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 Output0 and Output1 in Shared Memory and store shared memory handles
shm_op0_handle = cudashm.create_shared_memory_region(
"output0_data", output_byte_size, 0)
shm_op1_handle = cudashm.create_shared_memory_region(
"output1_data", output_byte_size, 0)
# Register Output0 and Output1 shared memory with Triton Server
triton_client.register_cuda_shared_memory(
"output0_data", cudashm.get_raw_handle(shm_op0_handle), 0,
output_byte_size)
triton_client.register_cuda_shared_memory(
"output1_data", cudashm.get_raw_handle(shm_op1_handle), 0,
output_byte_size)
# Create Input0 and Input1 in Shared Memory and store shared memory handles
shm_ip0_handle = cudashm.create_shared_memory_region(
"input0_data", input_byte_size, 0)
shm_ip1_handle = cudashm.create_shared_memory_region(
"input1_data", input_byte_size, 0)
# Put input data values into shared memory
cudashm.set_shared_memory_region(shm_ip0_handle, [input0_data])
cudashm.set_shared_memory_region(shm_ip1_handle, [input1_data])
# Register Input0 and Input1 shared memory with Triton Server
triton_client.register_cuda_shared_memory(
"input0_data", cudashm.get_raw_handle(shm_ip0_handle), 0,
input_byte_size)
triton_client.register_cuda_shared_memory(
"input1_data", cudashm.get_raw_handle(shm_ip1_handle), 0,
input_byte_size)
# Set the parameters to use data from shared memory
inputs = []
inputs.append(httpclient.InferInput('INPUT0', [1, 16], "INT32"))
inputs[-1].set_shared_memory("input0_data", input_byte_size)
inputs.append(httpclient.InferInput('INPUT1', [1, 16], "INT32"))
inputs[-1].set_shared_memory("input1_data", input_byte_size)
outputs = []
outputs.append(httpclient.InferRequestedOutput('OUTPUT0', binary_data=True))
outputs[-1].set_shared_memory("output0_data", output_byte_size)
outputs.append(httpclient.InferRequestedOutput('OUTPUT1', binary_data=True))
outputs[-1].set_shared_memory("output1_data", 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 = cudashm.get_contents_as_numpy(
shm_op0_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 = cudashm.get_contents_as_numpy(
shm_op1_handle, utils.triton_to_np_dtype(output1['datatype']),
output1['shape'])
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("cudashm infer error: incorrect sum")
sys.exit(1)
if (input0_data[i] - input1_data[i]) != output1_data[0][i]:
print("cudashm infer error: incorrect difference")
sys.exit(1)
print(triton_client.get_cuda_shared_memory_status())
triton_client.unregister_cuda_shared_memory()
assert len(cudashm.allocated_shared_memory_regions()) == 4
cudashm.destroy_shared_memory_region(shm_ip0_handle)
cudashm.destroy_shared_memory_region(shm_ip1_handle)
cudashm.destroy_shared_memory_region(shm_op0_handle)
cudashm.destroy_shared_memory_region(shm_op1_handle)
assert len(cudashm.allocated_shared_memory_regions()) == 0
print('PASS: cuda shared memory')