for most users, we do not need to read this section. But if you deploy your Paddle Serving on a machine without network, you will encounter a problem that the binary executable tar file cannot be downloaded. Therefore, here we give you all the download links for various environment.
- download the serving server whl package and bin package, and make sure they are for the same environment
- download the serving client whl and serving app whl, pay attention to the Python version.
-`pip install ` the serving and `tar xf ` the binary package, then `export SERVING_BIN=$PWD/serving-gpu-cuda10-0.0.0/serving` (take Cuda 10.0 as the example)
# An End-to-end Tutorial from Training to Inference Service Deployment
([简体中文](./TRAIN_TO_SERVICE_CN.md)|English)
Paddle Serving is Paddle's high-performance online inference service framework, which can flexibly support the deployment of most models. In this article, the IMDB review sentiment analysis task is used as an example to show the entire process from model training to deployment of inference service through 9 steps.
## Step1：Prepare for Running Environment
Paddle Serving can be deployed on Linux environments.Currently the server supports deployment on Centos7. [Docker deployment is recommended](RUN_IN_DOCKER.md). The rpc client supports deploymen on Centos7 and Ubuntu 18.On other systems or in environments where you do not want to install the serving module, you can still access the server-side prediction service through the http service.
You can choose to install the cpu or gpu version of the server module according to the requirements and machine environment, and install the client module on the client machine. When you want to access the server with http, there is not need to install client module.
```shell
pip install paddle_serving_server #cpu version server side
pip install paddle_serving_server_gpu #gpu version server side
pip install paddle_serving_client #client version
```
After simple preparation, we will take the IMDB review sentiment analysis task as an example to show the process from model training to deployment of prediction services. All the code in the example can be found in the [IMDB example](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/imdb) of the Paddle Serving code base, the data and dictionary used in the example The file can be obtained by executing the get_data.sh script in the IMDB sample code.
## Step2：Determine Tasks and Raw Data Format
IMDB review sentiment analysis task is to classify the content of movie reviews to determine whether the review is a positive review or a negative review.
First let's take a look at the raw data:
```
saw a trailer for this on another video, and decided to rent when it came out. boy, was i disappointed! the story is extremely boring, the acting (aside from christopher walken) is bad, and i couldn't care less about the characters, aside from really wanting to see nora's husband get thrashed. christopher walken's role is such a throw-away, what a tease! | 0
```
This is a sample of English comments. The sample uses | as the separator. The content of the comment is before the separator. The label is the sample after the separator. 0 is the negative while 1 is the positive.
## Step3：Define Reader, divide training set and test set
For the original text we need to convert it to a numeric id that the neural network can use. The imdb_reader.py script defines the method of text idization, and the words are mapped to integers through the dictionary file imdb.vocab.
In this way, the neural network can train the transformed text information as feature values.
## Step4：Define CNN network for training and saving
Net we use [CNN Model](https://www.paddlepaddle.org.cn/documentation/docs/zh/user_guides/nlp_case/understand_sentiment/README.cn.html#cnn) for training, in nets.py we define the network structure.
Use training dataset for training. The training script is local_train.py. After training, use the paddle_serving_client.io.save_model function to save the model files and configuration files used by the servingdeployment.
 As can be seen from the above figure, the loss of the model starts to converge after the 65th round. We save the model and configuration file after the 65th round of training is completed. The saved files are divided into imdb_cnn_client_conf and imdb_cnn_model folders. The former contains client-side configuration files, and the latter contains server-side configuration files and saved model files.
The parameter list of the save_model function is as follows:
| server_model_folder | Directory for server-side configuration files and model files |
| client_config_folder | Directory for saving client configuration files |
| feed_var_dict | The input of the inference model. The dict type and key can be customized. The value is the input variable in the model. Each key corresponds to a variable. When using the prediction service, the input data uses the key as the input name. |
| fetch_var_dict | The output of the model used for prediction, dict type, key can be customized, value is the input variable in the model, and each key corresponds to a variable. When using the prediction service, use the key to get the returned data |
| main_program | Model's program |
## Step5: Deploy RPC Prediction Service
The Paddle Serving framework supports two types of prediction service methods. One is to communicate through RPC and the other is to communicate through HTTP. The deployment and use of RPC prediction service will be introduced first. The deployment and use of HTTP prediction service will be introduced at Step 8. .
The parameter --model in the command specifies the server-side model and configuration file directory previously saved, --port specifies the port of the prediction service. When deploying the gpu prediction service using the gpu version, you can use --gpu_ids to specify the gpu used.
After executing one of the above commands, the RPC prediction service deployment of the IMDB sentiment analysis task is completed.
## Step6: Reuse Reader, define remote RPC client
Below we access the RPC prediction service through Python code, the script is test_client.py
<details>
<summary>test_client.py</summary>
```python
frompaddle_serving_clientimportClient
fromimdb_readerimportIMDBDataset
importsys
client=Client()
client.load_client_config(sys.argv[1])
client.connect(["127.0.0.1:9292"])
#The code of the data preprocessing part is reused here to convert the original text into a numeric id
Using 2084 samples in the test_data/part-0 file for test testing, the model prediction accuracy is 88.19%.
**Note**: The effect of each model training may be slightly different, and the accuracy of predictions using the trained model will be close to the examples but may not be exactly the same.
## Step8: Deploy HTTP Prediction Service
When using the HTTP prediction service, the client does not need to install any modules of Paddle Serving, it only needs to be able to send HTTP requests. Of course, the HTTP method consumes more time in the communication phase than the RPC method.
For the IMDB sentiment analysis task, the original text needs to be preprocessed before prediction. In the RPC prediction service, we put the preprocessing in the client's script, and in the HTTP prediction service, we put the preprocessing on the server. Paddle Serving's HTTP prediction service framework prepares data pre-processing and post-processing interfaces for this situation. We just need to rewrite it according to the needs of the task.
Serving provides sample code, which is obtained by executing the imdb_web_service_demo.sh script in [IMDB Example](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/imdb).
Let's take a look at the script text_classify_service.py that starts the HTTP prediction service.
In the above command, the first parameter is the saved server-side model and configuration file. The second parameter is the working directory, which will save some configuration files for the prediction service. The directory may not exist but needs to be specified. The prediction service will be created by itself. the third parameter is Port number, the fourth parameter is the dictionary file.
## Step9: Call the prediction service with plaintext data
After starting the HTTP prediction service, you can make prediction with a single command:
```
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "i am very sad | 0"}], "fetch":["prediction"]}' http://127.0.0.1:9292/imdb/prediction
```
When the inference process is normal, the prediction probability is returned, as shown below.
**Note**: The effect of each model training may be slightly different, and the inferred probability value using the trained model may not be consistent with the example.
saw a trailer for this on another video, and decided to rent when it came out. boy, was i disappointed! the story is extremely boring, the acting (aside from christopher walken) is bad, and i couldn't care less about the characters, aside from really wanting to see nora's husband get thrashed. christopher walken's role is such a throw-away, what a tease! | 0
curl -H "Content-Type:application/json" -X POST -d '{"feed":[{"words": "i am very sad | 0"}], "fetch":["prediction"]}' http://127.0.0.1:9292/imdb/prediction
