RUN_IN_DOCKER.md

# How to run PaddleServing in Docker

([简体中文](./RUN_IN_DOCKER_CN.md)|English)

## Requirements

Docker (GPU version requires nvidia-docker to be installed on the GPU machine)

## CPU

### Get docker image

You can get images in two ways:

1. Pull image directly

   ```bash
   docker pull hub.baidubce.com/paddlepaddle/serving:0.1.3
   ```

2. Building image based on dockerfile

   Create a new folder and copy [Dockerfile](../tools/Dockerfile) to this folder, and run the following command:

   ```bash
   docker build -t hub.baidubce.com/paddlepaddle/serving:0.1.3 .
   ```

### Create container

```bash
docker run -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/serving:0.1.3
docker exec -it test bash
```

The `-p` option is to map the `9292` port of the container to the `9292` port of the host.

### Install PaddleServing

In order to make the image smaller, the PaddleServing package is not installed in the image. You can run the following command to install it

```bash
pip install paddle-serving-server
```

### Test example

Before running the GPU version of the Server side code, you need to set the `CUDA_VISIBLE_DEVICES` environment variable to specify which GPUs the prediction service uses. The following example specifies two GPUs with indexes 0 and 1:

```bash
export CUDA_VISIBLE_DEVICES=0,1
```

Get the trained Boston house price prediction model by the following command:

```bash
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
tar -xzf uci_housing.tar.gz
```

- Test HTTP service

  Running on the Server side (inside the container):

  ```bash
  python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292 --name uci &>std.log 2>err.log &
  ```

  Running on the Client side (inside or outside the container):

  ```bash
  curl -H "Content-Type:application/json" -X POST -d '{"x": [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332], "fetch":["price"]}' http://127.0.0.1:9292/uci/prediction
  ```

- Test RPC service

  Running on the Server side (inside the container):

  ```bash
  python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292 &>std.log 2>err.log &
  ```

  Running following Python code on the Client side (inside or outside the container, The `paddle-serving-client` package needs to be installed):

  ```bash
  from paddle_serving_client import Client
  
  client = Client()
  client.load_client_config("uci_housing_client/serving_client_conf.prototxt")
  client.connect(["127.0.0.1:9292"])
  data = [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727,
          -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332]
  fetch_map = client.predict(feed={"x": data}, fetch=["price"])
  print(fetch_map)
  ```

  

## GPU

The GPU version is basically the same as the CPU version, with only some differences in interface naming (GPU version requires nvidia-docker to be installed on the GPU machine).

### Get docker image

You can also get images in two ways:

1. Pull image directly

   ```bash
   nvidia-docker pull hub.baidubce.com/paddlepaddle/serving:0.1.3-gpu
   ```

2. Building image based on dockerfile

   Create a new folder and copy [Dockerfile.gpu](../tools/Dockerfile.gpu) to this folder, and run the following command:

   ```bash
   nvidia-docker build -t hub.baidubce.com/paddlepaddle/serving:0.1.3-gpu .
   ```

### Create container

```bash
nvidia-docker run -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/serving:0.1.3-gpu
nvidia-docker exec -it test bash
```

The `-p` option is to map the `9292` port of the container to the `9292` port of the host.

### Install PaddleServing

In order to make the image smaller, the PaddleServing package is not installed in the image. You can run the following command to install it:

```bash
pip install paddle-serving-server-gpu
```

### Test example

When running the GPU Server, you need to set the GPUs used by the prediction service. By default, CPU version is used. You can configure it in two ways:

1. Using the `CUDA_VISIBLE_DEVICES` environment variable, the following example specifies two GPUs with an index of 0 and 1:

   ```shell
    export CUDA_VISIBLE_DEVICES=0,1
   ```

2. Using the `--gpu_ids` option, which will overrides the configuration of `CUDA_VISIBLE_DEVICES`.



Get the trained Boston house price prediction model by the following command:

```bash
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
tar -xzf uci_housing.tar.gz
```

- Test HTTP service

  Running on the Server side (inside the container):

  ```bash
  python -m paddle_serving_server_gpu.serve --model uci_housing_model --thread 10 --port 9292 --name uci --gpu_ids 0
  ```

  Running on the Client side (inside or outside the container):

  ```bash
  curl -H "Content-Type:application/json" -X POST -d '{"x": [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727, -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332], "fetch":["price"]}' http://127.0.0.1:9292/uci/prediction
  ```

- Test RPC service

  Running on the Server side (inside the container):

  ```bash
  python -m paddle_serving_server_gpu.serve --model uci_housing_model --thread 10 --port 9292 --gpu_ids 0
  ```

  Running following Python code on the Client side (inside or outside the container, The `paddle-serving-client` package needs to be installed):

  ```bash
  from paddle_serving_client import Client
  
  client = Client()
  client.load_client_config("uci_housing_client/serving_client_conf.prototxt")
  client.connect(["127.0.0.1:9292"])
  data = [0.0137, -0.1136, 0.2553, -0.0692, 0.0582, -0.0727,
          -0.1583, -0.0584, 0.6283, 0.4919, 0.1856, 0.0795, -0.0332]
  fetch_map = client.predict(feed={"x": data}, fetch=["price"])
  print(fetch_map)
  ```




## Attention

The images provided by this document are all runtime images, which do not support compilation. If you want to compile from source, refer to [COMPILE](COMPILE.md).