We should know something before converting to serving model
**inference_model_dir**：the directory of Paddle inference model
**serving_client_dir**: the directory of server side configuration
**serving_client_dir**: the directory of client side configuration
**model_filename**: this is model description file whose default value is `__model__`, if it's not default name, set `model_filename` explicitly
**params_filename**: during `save_inference_model` every Variable will be save as a single file. If we have the inference model whose params are compressed into one file, please set `params_filename` explicitly
| `dirname` | str | - | Path of saved model files. Program file and parameter files are saved in this directory. |
| `serving_server` | str | `"serving_server"` | The path of model files and configuration files for server. |
| `serving_client` | str | `"serving_client"` | The path of configuration files for client. |
| `model_filename` | str | None | The name of file to load the inference program. If it is None, the default filename `__model__` will be used. |
| `params_filename` | str | None | The name of file to load all parameters. It is only used for the case that all parameters were saved in a single binary file. If parameters were saved in separate files, set it as None. |
@@ -110,10 +110,10 @@ The first is that GPU Serving and Java Client are in the same image. After start
The second is to deploy GPU Serving and Java Client separately. If they are on the same host, you can learn the IP address of the corresponding container through ifconfig, and then when you connect to client.connect in `examples/src/main/java/PaddleServingClientExample.java` Make changes to the endpoint, and then compile it again. Or select `--net=host` to bind the network device of docker and host when docker starts, so that it can run directly without customizing java code.
**It should be noted that in the example, all models need to use `--use_multilang` to start GRPC multi-programming language support, and the port number is 9393. If you need another port, you need to modify it in the java file**
**It should be noted that in the example, all models(not pipeline) need to use `--use_multilang` to start GRPC multi-programming language support, and the port number is 9393. If you need another port, you need to modify it in the java file**
**Currently Serving has launched the Pipeline mode (see [Pipeline Serving](../doc/PIPELINE_SERVING.md) for details). Pipeline Serving Client for Java is released, the next version multi-thread java client example will be released**
**Currently Serving has launched the Pipeline mode (see [Pipeline Serving](../doc/PIPELINE_SERVING.md) for details). Pipeline Serving Client for Java is released.**
**It should be noted that in the example, Java Pipeline Client code is in path /Java/Examples and /Java/src/main, and the Pipeline server code is in path /python/examples/pipeline/**
**It should be noted that in the example, Java Pipeline Client code is in path /Java/Examples and /Java/src/main, and the Pipeline server code is in path /python/examples/pipeline/ The Client IP and Port(which is configured in java/examples/src/main/java/PipelineClientExample.java) should be corresponding to the Pipeline Server IP and Port(which is configured in config.yaml) **
In the example, a BERT model is used for semantic understanding prediction, and the text is represented as a vector, which can be used for further analysis and prediction.
If your python version is 3.X, replace the 'pip' field in the following command with 'pip3',replace 'python' with 'python3'.
### Getting Model
method 1:
This example use model [BERT Chinese Model](https://www.paddlepaddle.org.cn/hubdetail?name=bert_chinese_L-12_H-768_A-12&en_category=SemanticModel) from [Paddlehub](https://github.com/PaddlePaddle/PaddleHub).
Install paddlehub first
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...
@@ -22,11 +23,13 @@ the 128 in the command above means max_seq_len in BERT model, which is the lengt
the config file and model file for server side are saved in the folder bert_seq128_model.
the config file generated for client side is saved in the folder bert_seq128_client.
method 2:
You can also download the above model from BOS(max_seq_len=128). After decompression, the config file and model file for server side are stored in the bert_chinese_L-12_H-768_A-12_model folder, and the config file generated for client side is stored in the bert_chinese_L-12_H-768_A-12_client folder:
if your model is bert_chinese_L-12_H-768_A-12_model, replace the 'bert_seq128_model' field in the following command with 'bert_chinese_L-12_H-768_A-12_model',replace 'bert_seq128_client' with 'bert_chinese_L-12_H-768_A-12_client'.
### Getting Dict and Sample Dataset
...
...
@@ -36,11 +39,11 @@ sh get_data.sh
this script will download Chinese Dictionary File vocab.txt and Chinese Sample Data data-c.txt
### RPC Inference Service
Run
start cpu inference service,Run
```
python -m paddle_serving_server.serve --model bert_seq128_model/ --port 9292 #cpu inference service
```
Or
Or,start gpu inference service,Run
```
python -m paddle_serving_server_gpu.serve --model bert_seq128_model/ --port 9292 --gpu_ids 0 #launch gpu inference service at GPU 0
the client reads data from data-c.txt and send prediction request, the prediction is given by word vector. (Due to massive data in the word vector, we do not print it).
### HTTP Inference Service
start cpu HTTP inference service,Run
```
python bert_web_service.py bert_seq128_model/ 9292 #launch gpu inference service
```
Or,start gpu HTTP inference service,Run
```
export CUDA_VISIBLE_DEVICES=0,1
```
set environmental variable to specify which gpus are used, the command above means gpu 0 and gpu 1 is used.
```
python bert_web_service.py bert_seq128_model/ 9292 #launch gpu inference service
python bert_web_service_gpu.py bert_seq128_model/ 9292 #launch gpu inference service
