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Running
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Zero
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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.
"""Server with simple python model performing adding and subtract operation."""
import logging
import cupy as cp # pytype: disable=import-error
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.linear_cupy.server")
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(name)s: %(message)s")
VECTOR_SIZE = 10
class LinearModel:
def __init__(self):
self.alpha = 2
self.beta = cp.arange(VECTOR_SIZE)
@batch
def linear(self, **inputs):
u_batch, v_batch = inputs.values()
u_batch_cp, v_batch_cp = cp.asarray(u_batch), cp.asarray(v_batch)
lin = u_batch_cp * self.alpha + v_batch_cp + self.beta
return {"result": cp.asnumpy(lin)}
with Triton() as triton:
LOGGER.info("Loading linear model")
lin_model = LinearModel()
triton.bind(
model_name="Linear",
infer_func=lin_model.linear,
inputs=[
Tensor(dtype=np.float64, shape=(VECTOR_SIZE,)),
Tensor(dtype=np.float64, shape=(VECTOR_SIZE,)),
],
outputs=[
Tensor(name="result", dtype=np.float64, shape=(-1,)),
],
config=ModelConfig(max_batch_size=128),
strict=True,
)
LOGGER.info("Serving model")
triton.serve()
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