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async_client.py
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async_client.py
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# Copyright 2023, NVIDIA CORPORATION & AFFILIATES. 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 sys
import numpy as np
import tritonclient.http as httpclient
from tritonclient.utils import *
model_name = "bls_decoupled_async"
shape = [1]
with httpclient.InferenceServerClient("localhost:8000") as client:
in_values = [4, 2, 0, 1]
for in_value in in_values:
input_data = np.array([in_value], dtype=np.int32)
inputs = [
httpclient.InferInput(
"IN", input_data.shape, np_to_triton_dtype(input_data.dtype)
)
]
inputs[0].set_data_from_numpy(input_data)
outputs = [httpclient.InferRequestedOutput("SUM")]
response = client.infer(model_name, inputs, request_id=str(1), outputs=outputs)
result = response.get_response()
# output_data contains two times of the square value of the input value.
output_data = response.as_numpy("SUM")
print("==========model result==========")
print(
"Two times the square value of {} is {}\n".format(input_data, output_data)
)
if not np.allclose((2 * input_data * input_data), output_data):
print(
"BLS Decoupled Async example error: incorrect output value. Expected {}, got {}.".format(
(2 * input_data * input_data), output_data
)
)
sys.exit(1)
print("PASS: BLS Decoupled Async")
sys.exit(0)