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model.py
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model.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 json
import time
# triton_python_backend_utils is available in every Triton Python model. You
# need to use this module to create inference requests and responses. It also
# contains some utility functions for extracting information from model_config
# and converting Triton input/output types to numpy types.
import triton_python_backend_utils as pb_utils
class TritonPythonModel:
"""Your Python model must use the same class name. Every Python model
that is created must have "TritonPythonModel" as the class name.
"""
def initialize(self, args):
"""`initialize` is called only once when the model is being loaded.
Implementing `initialize` function is optional. This function allows
the model to initialize any state associated with this model.
Parameters
----------
args : dict
Both keys and values are strings. The dictionary keys and values are:
* model_config: A JSON string containing the model configuration
* model_instance_kind: A string containing model instance kind
* model_instance_device_id: A string containing model instance device ID
* model_repository: Model repository path
* model_version: Model version
* model_name: Model name
"""
# Parse model_config and extract OUTPUT0 and OUTPUT1 configuration
self.model_config = model_config = json.loads(args["model_config"])
output0_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT0")
output1_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT1")
# Convert Triton types to numpy types
self.out0_dtype = pb_utils.triton_string_to_numpy(output0_config["data_type"])
self.out1_dtype = pb_utils.triton_string_to_numpy(output1_config["data_type"])
# Create a MetricFamily object to report the latency of the model
# execution. The 'kind' parameter must be either 'COUNTER' or
# 'GAUGE'.
# If duplicate name is used, both MetricFamily objects
# will reference to the same underlying MetricFamily. If there are two
# MetricFamily objects with the same name and same kind but different
# description, the original description will be used. Note that
# Duplicate name with different kind is not allowed.
self.metric_family = pb_utils.MetricFamily(
name="requests_process_latency_ns",
description="Cumulative time spent processing requests",
kind=pb_utils.MetricFamily.COUNTER, # or pb_utils.MetricFamily.GAUGE
)
# Create a Metric object under the MetricFamily object. The 'labels'
# is a dictionary of key-value pairs. You can create multiple Metric
# objects under the same MetricFamily object with unique labels. Empty
# labels is allowed. The 'labels' parameter is optional. If you don't
# specify the 'labels' parameter, empty labels will be used.
self.metric = self.metric_family.Metric(
labels={"model": "custom_metrics", "version": "1"}
)
def execute(self, requests):
"""`execute` MUST be implemented in every Python model. `execute`
function receives a list of pb_utils.InferenceRequest as the only
argument. This function is called when an inference request is made
for this model. Depending on the batching configuration (e.g. Dynamic
Batching) used, `requests` may contain multiple requests. Every
Python model, must create one pb_utils.InferenceResponse for every
pb_utils.InferenceRequest in `requests`. If there is an error, you can
set the error argument when creating a pb_utils.InferenceResponse
Parameters
----------
requests : list
A list of pb_utils.InferenceRequest
Returns
-------
list
A list of pb_utils.InferenceResponse. The length of this list must
be the same as `requests`
"""
responses = []
# Record the start time of processing the requests
start_ns = time.time_ns()
# Every Python backend must iterate over everyone of the requests
# and create a pb_utils.InferenceResponse for each of them.
for request in requests:
# Get INPUT0
in_0 = pb_utils.get_input_tensor_by_name(request, "INPUT0")
# Get INPUT1
in_1 = pb_utils.get_input_tensor_by_name(request, "INPUT1")
out_0, out_1 = (
in_0.as_numpy() + in_1.as_numpy(),
in_0.as_numpy() - in_1.as_numpy(),
)
# Create output tensors. You need pb_utils.Tensor
# objects to create pb_utils.InferenceResponse.
out_tensor_0 = pb_utils.Tensor("OUTPUT0", out_0.astype(self.out0_dtype))
out_tensor_1 = pb_utils.Tensor("OUTPUT1", out_1.astype(self.out1_dtype))
# Create InferenceResponse. You can set an error here in case
# there was a problem with handling this inference request.
# Below is an example of how you can set errors in inference
# response:
#
# pb_utils.InferenceResponse(
# output_tensors=..., TritonError("An error occurred"))
inference_response = pb_utils.InferenceResponse(
output_tensors=[out_tensor_0, out_tensor_1]
)
responses.append(inference_response)
# Record the end time of processing the requests
end_ns = time.time_ns()
# Update metric to track cumulative requests processing latency.
# There are three operations you can do with the Metric object:
# - Metric.increment(value): Increment the value of the metric by
# the given value. The type of the value is double. The 'COUNTER'
# kind does not support negative value.
# - Metric.set(value): Set the value of the metric to the given
# value. This operation is only supported in 'GAUGE' kind. The
# type of the value is double.
# - Metric.value(): Get the current value of the metric.
self.metric.increment(end_ns - start_ns)
logger = pb_utils.Logger
logger.log_info(
"Cumulative requests processing latency: {}".format(self.metric.value())
)
# You should return a list of pb_utils.InferenceResponse. Length
# of this list must match the length of `requests` list.
return responses
def finalize(self):
"""`finalize` is called only once when the model is being unloaded.
Implementing `finalize` function is OPTIONAL. This function allows
the model to perform any necessary clean ups before exit.
"""
print("Cleaning up...")