paddle模型服务器部署分两步走

采用docker部署，将相关文件拷贝到容器中Serving readme
执行命令后，会在当前目录下生成2个目录：serving_server 和 serving_client。serving_server目录包含服务器端所需的模型和配置，需将其拷贝到服务器端容器中；serving_client目录包含客户端所需的配置，需将其拷贝到客户端容器中。

第一步 把动态图转换成静态图模型

# Copyright (c) 2021 PaddlePaddle Authors. 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.

import argparse
import os
from functools import partial

import numpy as np
import paddle
import paddle.nn.functional as F
import paddlenlp as ppnlp
from paddlenlp.data import Stack, Tuple, Pad

# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument("--params_path", type=str, default='./checkpoints/model_state.pdparams', help="The path to model parameters to be loaded.")
parser.add_argument("--output_path", type=str, default='./deploy/output', help="The path of model parameter in static graph to be saved.")
args = parser.parse_args()
# yapf: enable

if __name__ == "__main__":
    # The number of labels should be in accordance with the training dataset.
    label_map = {0: 'negative', 1: 'positive'}
    model = ppnlp.transformers.ErnieForSequenceClassification.from_pretrained(
        "ernie-tiny", num_classes=len(label_map))

    if args.params_path and os.path.isfile(args.params_path):
        state_dict = paddle.load(args.params_path)
        model.set_dict(state_dict)
        print("Loaded parameters from %s" % args.params_path)
    model.eval()

    # Convert to static graph with specific input description
    model = paddle.jit.to_static(
        model,
        input_spec=[
            paddle.static.InputSpec(
                shape=[None, None], dtype="int64"),  # input_ids
            paddle.static.InputSpec(
                shape=[None, None], dtype="int64")  # segment_ids
        ])
    # Save in static graph model.
    save_path = os.path.join(args.output_path, "inference")
    paddle.jit.save(model, save_path)

第二部 使用Serving进行预测部署时，需要将静态图inference model导出为Serving可读入的模型参数和配置。运行方式如下：

# Copyright (c) 2021 PaddlePaddle Authors. 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.

import argparse
import paddle
import paddle_serving_client.io as serving_io


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--inference_model_dir",
        type=str,
        default="../output",
        help="The directory of the inference model.")
    parser.add_argument(
        "--model_file",
        type=str,
        default='inference.pdmodel',
        help="The inference model file name.")
    parser.add_argument(
        "--params_file",
        type=str,
        default='inference.pdiparams',
        help="The input inference parameters file name.")
    return parser.parse_args()


if __name__ == '__main__':
    paddle.enable_static()
    args = parse_args()
    feed_names, fetch_names = serving_io.inference_model_to_serving(
        dirname=args.inference_model_dir,
        serving_server="serving_server",
        serving_client="serving_client",
        model_filename=args.model_file,
        params_filename=args.params_file)
    print("model feed_names : %s" % feed_names)
    print("model fetch_names : %s" % fetch_names)