Commit 374c78ca authored by chenzhuo's avatar chenzhuo
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qwen-1.5

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# Copyright 2021, 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.
include(CMakeFindDependencyMacro)
get_filename_component(
TUTORIALRECOMMENDEDBACKEND_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH
)
list(APPEND CMAKE_MODULE_PATH ${TUTORIALRECOMMENDEDBACKEND_CMAKE_DIR})
if(NOT TARGET TutorialRecommendedBackend::triton-recommended-backend)
include("${TUTORIALRECOMMENDEDBACKEND_CMAKE_DIR}/TutorialRecommendedBackendTargets.cmake")
endif()
set(TUTORIALRECOMMENDEDBACKEND_LIBRARIES TutorialRecommendedBackend::triton-recommended-backend)
# Copyright 2021, 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.
{
global:
TRITONBACKEND_*;
local: *;
};
#!/usr/bin/python
# Copyright 2022, 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 argparse
import numpy as np
import tritonhttpclient as httpclient
from tritonclientutils import np_to_triton_dtype
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()
model_name = "bls_fp32"
shape = [16]
with httpclient.InferenceServerClient(url=FLAGS.url) as client:
input0_data = np.random.rand(*shape).astype(np.float32)
input1_data = np.random.rand(*shape).astype(np.float32)
inputs = [
httpclient.InferInput("INPUT0", input0_data.shape,
np_to_triton_dtype(input0_data.dtype)),
httpclient.InferInput("INPUT1", input1_data.shape,
np_to_triton_dtype(input1_data.dtype)),
]
inputs[0].set_data_from_numpy(input0_data)
inputs[1].set_data_from_numpy(input1_data)
outputs = [
httpclient.InferRequestedOutput("OUTPUT0"),
httpclient.InferRequestedOutput("OUTPUT1"),
]
response = client.infer(model_name,
inputs,
request_id=str(1),
outputs=outputs)
result = response.get_response()
output0_data = response.as_numpy("OUTPUT0")
output1_data = response.as_numpy("OUTPUT1")
print("INPUT0 ({}) + INPUT1 ({}) = OUTPUT0 ({})".format(
input0_data, input1_data, output0_data))
print("INPUT0 ({}) - INPUT1 ({}) = OUTPUT1 ({})".format(
input0_data, input1_data, output1_data))
if not np.allclose(input0_data + input1_data, output0_data):
print("error: incorrect sum")
sys.exit(1)
if not np.allclose(input0_data - input1_data, output1_data):
print("error: incorrect difference")
sys.exit(1)
print('\nPASS')
sys.exit(0)
#!/usr/bin/env python
# Copyright 2021, 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 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')))
#!/usr/bin/env python
# Copyright 2021, 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 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)
# 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 backend as a single batch.
#
# The recommended backend can handle any model with 1 input and 1
# output as long as the input and output datatype and shape are
# the same. The batching model uses datatype FP32 and shape
# [ 4, 4 ].
print('\n=========')
async_requests = []
input0_data = np.array([[[ 1.0, 1.1, 1.2, 1.3 ],
[ 2.0, 2.1, 2.2, 2.3 ],
[ 3.0, 3.1, 3.2, 3.3 ],
[ 4.0, 4.1, 4.2, 4.3 ]]], dtype=np.float32)
print('Sending request to batching model: input = {}'.format(input0_data))
inputs = [ httpclient.InferInput('INPUT', [1, 4, 4], "FP32") ]
inputs[0].set_data_from_numpy(input0_data)
async_requests.append(triton_client.async_infer('batching', inputs))
input0_data = np.array([[[ 10.0, 10.1, 10.2, 10.3 ],
[ 20.0, 20.1, 20.2, 20.3 ],
[ 30.0, 30.1, 30.2, 30.3 ],
[ 40.0, 40.1, 40.2, 40.3 ]]], dtype=np.float32)
print('Sending request to batching model: input = {}'.format(input0_data))
inputs = [ httpclient.InferInput('INPUT', [1, 4, 4], "FP32") ]
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('OUTPUT = {}'.format(result.as_numpy('OUTPUT')))
# Copyright 2022, 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.
name: "addsub_python"
backend: "python"
max_batch_size: 0
input [
{
name: "INPUT0"
data_type: TYPE_FP32
dims: [ 16 ]
}
]
input [
{
name: "INPUT1"
data_type: TYPE_FP32
dims: [ 16 ]
}
]
output [
{
name: "OUTPUT0"
data_type: TYPE_FP32
dims: [ 16 ]
}
]
output [
{
name: "OUTPUT1"
data_type: TYPE_FP32
dims: [ 16 ]
}
]
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