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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.
"""Simple classifier example based on Hugging Face Pytorch BART model."""
import logging
import numpy as np
from transformers import pipeline # pytype: disable=import-error
from pytriton.decorators import batch
from pytriton.model_config import ModelConfig, Tensor
from pytriton.triton import Triton
logger = logging.getLogger("examples.perf_analyzer.server")
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(name)s: %(message)s")
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli", device=0)
@batch
def _infer_fn(**inputs: np.ndarray):
sequences_batch, labels_batch = inputs.values()
# need to convert dtype=object to bytes first
# end decode unicode bytes
sequences_batch = np.char.decode(sequences_batch.astype("bytes"), "utf-8")
labels_batch = np.char.decode(labels_batch.astype("bytes"), "utf-8")
scores = []
for sequence, labels in zip(sequences_batch, labels_batch):
classification_result = classifier(sequence.item(), labels.tolist())
scores.append(classification_result["scores"])
scores_batch = np.array(scores, dtype=np.float32)
return {"scores": scores_batch}
with Triton() as triton:
logger.info("Loading BART model.")
triton.bind(
model_name="BART",
infer_func=_infer_fn,
inputs=[
Tensor(name="sequence", dtype=np.bytes_, shape=(1,)),
Tensor(name="labels", dtype=np.bytes_, shape=(-1,)),
],
outputs=[
Tensor(name="scores", dtype=np.float32, shape=(-1,)),
],
config=ModelConfig(max_batch_size=8),
strict=True,
)
logger.info("Serving inference")
triton.serve()
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