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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 requests
import tritonclient.http as httpclient
from tritonclient.utils import *
model_name = "custom_metrics"
shape = [4]
def get_metrics():
metrics_url = "http://localhost:8002/metrics"
r = requests.get(metrics_url)
r.raise_for_status()
return r.text
with httpclient.InferenceServerClient("localhost:8000") 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)
output0_data = response.as_numpy("OUTPUT0")
output1_data = response.as_numpy("OUTPUT1")
if not np.allclose(input0_data + input1_data, output0_data):
print("custom_metrics example error: incorrect sum")
sys.exit(1)
if not np.allclose(input0_data - input1_data, output1_data):
print("custom_metrics example error: incorrect difference")
sys.exit(1)
metrics = get_metrics()
patterns = [
"# HELP requests_process_latency_ns Cumulative time spent processing requests",
"# TYPE requests_process_latency_ns counter",
'requests_process_latency_ns{model="custom_metrics",version="1"}',
]
for pattern in patterns:
if pattern not in metrics:
print(
"custom_metrics example error: missing pattern '{}' in metrics".format(
pattern
)
)
sys.exit(1)
else:
print(
"custom_metrics example: found pattern '{}' in metrics".format(pattern)
)
print("PASS: custom_metrics")
sys.exit(0)