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#!/usr/bin/env python3
# Copyright (c) 2022-2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Very simple example with python identity operation."""
import logging
import numpy as np
from pytriton.decorators import batch
from pytriton.model_config import ModelConfig, Tensor
from pytriton.triton import Triton
logger = logging.getLogger("examples.identity_python.server")
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(name)s: %(message)s")
def _infer_raw_fn(inputs): # noqa: N803
return [
{
"OUTPUT_1": request["INPUT_1"],
"OUTPUT_2": request["INPUT_2"],
}
for request in inputs
]
@batch
def _infer_fn(**inputs): # noqa: N803
return {
"OUTPUT_1": inputs["INPUT_1"],
"OUTPUT_2": inputs["INPUT_2"],
}
with Triton() as triton:
logger.info("Loading Identity model.")
triton.bind(
model_name="Identity",
infer_func=_infer_fn,
inputs=[
Tensor(dtype=np.float64, shape=(-1,)),
Tensor(dtype=object, shape=(1,)),
],
outputs=[
Tensor(dtype=np.float64, shape=(-1,)),
Tensor(dtype=object, shape=(1,)),
],
config=ModelConfig(max_batch_size=128),
strict=True,
)
logger.info("Serving inference")
triton.serve()
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