mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-12-24 13:28:13 +08:00
178 lines
7.6 KiB
Markdown
178 lines
7.6 KiB
Markdown
[English](README.md) | 简体中文
|
||
|
||
# ERNIE 3.0 服务化部署示例
|
||
|
||
在服务化部署前，需确认
|
||
|
||
- 1. 服务化镜像的软硬件环境要求和镜像拉取命令请参考[FastDeploy服务化部署](../../../../../serving/README_CN.md)
|
||
|
||
## 准备模型
|
||
|
||
下载ERNIE 3.0的新闻分类模型、序列标注模型(如果有已训练好的模型，跳过此步骤):
|
||
```bash
|
||
# 下载并解压新闻分类模型
|
||
wget https://paddlenlp.bj.bcebos.com/models/transformers/ernie_3.0/tnews_pruned_infer_model.zip
|
||
unzip tnews_pruned_infer_model.zip
|
||
|
||
# 将下载的模型移动到分类任务的模型仓库目录
|
||
mv tnews_pruned_infer_model/float32.pdmodel models/ernie_seqcls_model/1/model.pdmodel
|
||
mv tnews_pruned_infer_model/float32.pdiparams models/ernie_seqcls_model/1/model.pdiparams
|
||
|
||
# 下载并解压序列标注模型
|
||
wget https://paddlenlp.bj.bcebos.com/models/transformers/ernie_3.0/msra_ner_pruned_infer_model.zip
|
||
unzip msra_ner_pruned_infer_model.zip
|
||
|
||
# 将下载的模型移动到序列标注任务的模型仓库目录
|
||
mv msra_ner_pruned_infer_model/float32.pdmodel models/ernie_tokencls_model/1/model.pdmodel
|
||
mv msra_ner_pruned_infer_model/float32.pdiparams models/ernie_tokencls_model/1/model.pdiparams
|
||
```
|
||
|
||
模型下载移动好之后，分类任务的models目录结构如下:
|
||
```
|
||
models
|
||
├── ernie_seqcls # 分类任务的pipeline
|
||
│ ├── 1
|
||
│ └── config.pbtxt # 通过这个文件组合前后处理和模型推理
|
||
├── ernie_seqcls_model # 分类任务的模型推理
|
||
│ ├── 1
|
||
│ │ └── model.onnx
|
||
│ └── config.pbtxt
|
||
├── ernie_seqcls_postprocess # 分类任务后处理
|
||
│ ├── 1
|
||
│ │ └── model.py
|
||
│ └── config.pbtxt
|
||
└── ernie_tokenizer # 预处理分词
|
||
├── 1
|
||
│ └── model.py
|
||
└── config.pbtxt
|
||
```
|
||
|
||
## 拉取并运行镜像
|
||
```bash
|
||
# x.y.z为镜像版本号，需参照serving文档替换为数字
|
||
# GPU镜像
|
||
docker pull registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10
|
||
# CPU镜像
|
||
docker pull registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-cpu-only-21.10
|
||
|
||
# 运行
|
||
docker run -it --net=host --name fastdeploy_server --shm-size="1g" -v /path/serving/models:/models registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-cpu-only-21.10 bash
|
||
```
|
||
|
||
## 部署模型
|
||
serving目录包含启动pipeline服务的配置和发送预测请求的代码，包括：
|
||
|
||
```
|
||
models # 服务化启动需要的模型仓库，包含模型和服务配置文件
|
||
seq_cls_rpc_client.py # 新闻分类任务发送pipeline预测请求的脚本
|
||
token_cls_rpc_client.py # 序列标注任务发送pipeline预测请求的脚本
|
||
```
|
||
|
||
*注意*:启动服务时，Server的每个python后端进程默认申请`64M`内存，默认启动的docker无法启动多个python后端节点。有两个解决方案：
|
||
- 1.启动容器时设置`shm-size`参数, 比如:`docker run -it --net=host --name fastdeploy_server --shm-size="1g" -v /path/serving/models:/models registry.baidubce.com/paddlepaddle/fastdeploy:x.y.z-gpu-cuda11.4-trt8.4-21.10 bash`
|
||
- 2.启动服务时设置python后端的`shm-default-byte-size`参数, 设置python后端的默认内存为10M： `tritonserver --model-repository=/models --backend-config=python,shm-default-byte-size=10485760`
|
||
|
||
### 分类任务
|
||
在容器内执行下面命令启动服务:
|
||
```
|
||
# 默认启动models下所有模型
|
||
fastdeployserver --model-repository=/models
|
||
|
||
# 可通过参数只启动分类任务
|
||
fastdeployserver --model-repository=/models --model-control-mode=explicit --load-model=ernie_seqcls
|
||
```
|
||
输出打印如下:
|
||
```
|
||
I1019 09:41:15.375496 2823 model_repository_manager.cc:1183] successfully loaded 'ernie_tokenizer' version 1
|
||
I1019 09:41:15.375987 2823 model_repository_manager.cc:1022] loading: ernie_seqcls:1
|
||
I1019 09:41:15.477147 2823 model_repository_manager.cc:1183] successfully loaded 'ernie_seqcls' version 1
|
||
I1019 09:41:15.477325 2823 server.cc:522]
|
||
...
|
||
I0613 08:59:20.577820 10021 server.cc:592]
|
||
+----------------------------+---------+--------+
|
||
| Model | Version | Status |
|
||
+----------------------------+---------+--------+
|
||
| ernie_seqcls | 1 | READY |
|
||
| ernie_seqcls_model | 1 | READY |
|
||
| ernie_seqcls_postprocess | 1 | READY |
|
||
| ernie_tokenizer | 1 | READY |
|
||
+----------------------------+---------+--------+
|
||
...
|
||
I0601 07:15:15.923270 8059 grpc_server.cc:4117] Started GRPCInferenceService at 0.0.0.0:8001
|
||
I0601 07:15:15.923604 8059 http_server.cc:2815] Started HTTPService at 0.0.0.0:8000
|
||
I0601 07:15:15.964984 8059 http_server.cc:167] Started Metrics Service at 0.0.0.0:8002
|
||
```
|
||
|
||
### 序列标注任务
|
||
在容器内执行下面命令启动序列标注服务:
|
||
```
|
||
fastdeployserver --model-repository=/models --model-control-mode=explicit --load-model=ernie_tokencls --backend-config=python,shm-default-byte-size=10485760
|
||
```
|
||
输出打印如下:
|
||
```
|
||
I1019 09:41:15.375496 2823 model_repository_manager.cc:1183] successfully loaded 'ernie_tokenizer' version 1
|
||
I1019 09:41:15.375987 2823 model_repository_manager.cc:1022] loading: ernie_seqcls:1
|
||
I1019 09:41:15.477147 2823 model_repository_manager.cc:1183] successfully loaded 'ernie_seqcls' version 1
|
||
I1019 09:41:15.477325 2823 server.cc:522]
|
||
...
|
||
I0613 08:59:20.577820 10021 server.cc:592]
|
||
+----------------------------+---------+--------+
|
||
| Model | Version | Status |
|
||
+----------------------------+---------+--------+
|
||
| ernie_tokencls | 1 | READY |
|
||
| ernie_tokencls_model | 1 | READY |
|
||
| ernie_tokencls_postprocess | 1 | READY |
|
||
| ernie_tokenizer | 1 | READY |
|
||
+----------------------------+---------+--------+
|
||
...
|
||
I0601 07:15:15.923270 8059 grpc_server.cc:4117] Started GRPCInferenceService at 0.0.0.0:8001
|
||
I0601 07:15:15.923604 8059 http_server.cc:2815] Started HTTPService at 0.0.0.0:8000
|
||
I0601 07:15:15.964984 8059 http_server.cc:167] Started Metrics Service at 0.0.0.0:8002
|
||
```
|
||
|
||
## 客户端请求
|
||
客户端请求可以在本地执行脚本请求；也可以在容器中执行。
|
||
|
||
本地执行脚本需要先安装依赖:
|
||
```
|
||
pip install grpcio
|
||
pip install tritonclient[all]
|
||
|
||
# 如果bash无法识别括号，可以使用如下指令安装:
|
||
pip install tritonclient\[all\]
|
||
```
|
||
|
||
### 分类任务
|
||
注意执行客户端请求时关闭代理，并根据实际情况修改main函数中的ip地址(启动服务所在的机器)
|
||
```
|
||
python seq_cls_grpc_client.py
|
||
```
|
||
输出打印如下:
|
||
```
|
||
{'label': array([5, 9]), 'confidence': array([0.6425664 , 0.66534853], dtype=float32)}
|
||
{'label': array([4]), 'confidence': array([0.53198355], dtype=float32)}
|
||
acc: 0.5731
|
||
```
|
||
|
||
### 序列标注任务
|
||
注意执行客户端请求时关闭代理，并根据实际情况修改main函数中的ip地址(启动服务所在的机器)
|
||
```
|
||
python token_cls_grpc_client.py
|
||
```
|
||
输出打印如下:
|
||
```
|
||
input data: 北京的涮肉，重庆的火锅，成都的小吃都是极具特色的美食。
|
||
The model detects all entities:
|
||
entity: 北京 label: LOC pos: [0, 1]
|
||
entity: 重庆 label: LOC pos: [6, 7]
|
||
entity: 成都 label: LOC pos: [12, 13]
|
||
input data: 原产玛雅故国的玉米，早已成为华夏大地主要粮食作物之一。
|
||
The model detects all entities:
|
||
entity: 玛雅 label: LOC pos: [2, 3]
|
||
entity: 华夏 label: LOC pos: [14, 15]
|
||
```
|
||
|
||
## 配置修改
|
||
|
||
当前分类任务(ernie_seqcls_model/config.pbtxt)默认配置在CPU上运行OpenVINO引擎; 序列标注任务默认配置在GPU上运行Paddle引擎。如果要在CPU/GPU或其他推理引擎上运行, 需要修改配置，详情请参考[配置文档](../../../../serving/docs/zh_CN/model_configuration.md)
|