kserve/python/custom_model/model_remote.py

# Copyright 2024 The KServe Authors.
#
# 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.

import argparse

from torchvision import models, transforms
from typing import Dict
import torch
from PIL import Image
import base64
import io
from ray import serve
from kserve import Model, ModelServer, logging, model_server
from kserve.ray import RayModel


# the model handle name should match the model endpoint name
@serve.deployment(name="custom-model", num_replicas=1)
class AlexNetModel(Model):
    def __init__(self):
        self.name = "custom-model"
        super().__init__(self.name)
        self.model = None
        self.ready = False
        self.load()

    def load(self):
        self.model = models.alexnet(pretrained=True, progress=False)
        self.model.eval()
        self.ready = True

    async def predict(self, payload: Dict, headers: Dict[str, str] = None) -> Dict:
        inputs = payload["instances"]

        # Input follows the Tensorflow V1 HTTP API for binary values
        # https://www.tensorflow.org/tfx/serving/api_rest#encoding_binary_values
        data = inputs[0]["image"]["b64"]

        raw_img_data = base64.b64decode(data)
        input_image = Image.open(io.BytesIO(raw_img_data))

        preprocess = transforms.Compose(
            [
                transforms.Resize(256),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                transforms.Normalize(
                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
                ),
            ]
        )

        input_tensor = preprocess(input_image)
        input_batch = input_tensor.unsqueeze(0)

        output = self.model(input_batch)

        torch.nn.functional.softmax(output, dim=1)

        values, top_5 = torch.topk(output, 5)

        return {"predictions": values.tolist()}


parser = argparse.ArgumentParser(parents=[model_server.parser])
args, _ = parser.parse_known_args()

if __name__ == "__main__":
    if args.configure_logging:
        logging.configure_logging(args.log_config_file)
    app = AlexNetModel.bind()
    handle = serve.run(app)
    model = RayModel(name="custom-model", handle=handle)
    model.load()
    ModelServer().start([model])