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fix run in docker

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doc/RUN_IN_DOCKER.md
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......@@ -32,63 +32,9 @@ The `-p` option is to map the `9292` port of the container to the `9292` port of
### 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
```
You may need to use a domestic mirror source (in China, you can use the Tsinghua mirror source of the following example) to speed up the download:
```shell
pip install paddle-serving-server -i https://pypi.tuna.tsinghua.edu.cn/simple
```
### Test example
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 '{"feed":{"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)
```
The mirror comes with `paddle_serving_server`, `paddle_serving_client`, and `paddle_serving_app` corresponding to the mirror tag version. If users don’t need to change the version, they can use it directly, which is suitable for environments without extranet services.
If you need to change the version, please refer to the instructions on the homepage to download the pip package of the corresponding version.
## GPU
......@@ -100,7 +46,7 @@ The GPU version is basically the same as the CPU version, with only some differe
Refer to [this document](DOCKER_IMAGES.md) for a docker image, the following is an example of an `cuda9.0-cudnn7` image:
```shell
nvidia-docker pull hub.baidubce.com/paddlepaddle/serving:latest-cuda9.0-cudnn7
docker pull hub.baidubce.com/paddlepaddle/serving:latest-cuda9.0-cudnn7
```
### Create container
......@@ -110,77 +56,21 @@ nvidia-docker run -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/se
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:
or
```bash
pip install paddle-serving-server-gpu
```
You may need to use a domestic mirror source (in China, you can use the Tsinghua mirror source of the following example) to speed up the download:
```shell
pip install paddle-serving-server-gpu -i https://pypi.tuna.tsinghua.edu.cn/simple
```
### Test example
When running the GPU Server, you need to set the GPUs used by the prediction service through the `--gpu_ids` option, and the CPU is used by default. An error will be reported when the value of `--gpu_ids` exceeds the environment variable `CUDA_VISIBLE_DEVICES`. The following example specifies to use a GPU with index 0:
```shell
export CUDA_VISIBLE_DEVICES=0,1
python -m paddle_serving_server_gpu.serve --model uci_housing_model --port 9292 --gpu_ids 0
```
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
docker run --gpus all -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/serving:latest-cuda9.0-cudnn7
docker exec -it test bash
```
- 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 '{"feed":{"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)
```
The `-p` option is to map the `9292` port of the container to the `9292` port of the host.
### Install PaddleServing
The mirror comes with `paddle_serving_server_gpu`, `paddle_serving_client`, and `paddle_serving_app` corresponding to the mirror tag version. If users don’t need to change the version, they can use it directly, which is suitable for environments without extranet services.
If you need to change the version, please refer to the instructions on the homepage to download the pip package of the corresponding version.
## Attention
## Precautious
Runtime images cannot be used for compilation. If you want to compile from source, refer to [COMPILE](COMPILE.md).
doc/RUN_IN_DOCKER_CN.md
浏览文件 @ 014248d6
......@@ -20,7 +20,6 @@ Docker（GPU版本需要在GPU机器上安装nvidia-docker）
docker pull hub.baidubce.com/paddlepaddle/serving:latest
```
### 创建容器并进入
```bash
......@@ -32,74 +31,11 @@ docker exec -it test bash
### 安装PaddleServing
为了减小镜像的体积，镜像中没有安装Serving包，要执行下面命令进行安装。
```bash
pip install paddle-serving-server
```
您可能需要使用国内镜像源（例如清华源）来加速下载。
```shell
pip install paddle-serving-server -i https://pypi.tuna.tsinghua.edu.cn/simple
```
### 测试example
通过下面命令获取训练好的Boston房价预估模型：
```bash
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
tar -xzf uci_housing.tar.gz
```
- 测试HTTP服务
在Server端（容器内）运行：
镜像里自带对应镜像tag版本的`paddle_serving_server`，`paddle_serving_client`，`paddle_serving_app`，如果用户不需要更改版本，可以直接使用，适用于没有外网服务的环境。
```bash
python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292 --name uci >std.log 2>err.log &
```
如果需要更换版本，请参照首页的指导，下载对应版本的pip包。
在Client端（容器内或容器外）运行：
```bash
curl -H "Content-Type:application/json" -X POST -d '{"feed":{"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
```
- 测试RPC服务
在Server端（容器内）运行：
```bash
python -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9292 >std.log 2>err.log &
```
在Client端（容器内或容器外，需要安装`paddle-serving-client`包）运行下面Python代码：
```python
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版本
GPU版本与CPU版本基本一致，只有部分接口命名的差别（GPU版本需要在GPU机器上安装nvidia-docker）。
### 获取镜像
参考[该文档](DOCKER_IMAGES_CN.md)获取镜像，这里以 `cuda9.0-cudnn7` 的镜像为例：
```shell
nvidia-docker pull hub.baidubce.com/paddlepaddle/serving:latest-cuda9.0-cudnn7
```
## GPU 版本
### 创建容器并进入
......@@ -107,74 +43,19 @@ nvidia-docker pull hub.baidubce.com/paddlepaddle/serving:latest-cuda9.0-cudnn7
nvidia-docker run -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/serving:latest-cuda9.0-cudnn7
nvidia-docker exec -it test bash
```
`-p`选项是为了将容器的`9292`端口映射到宿主机的`9292`端口。
### 安装PaddleServing
为了减小镜像的体积，镜像中没有安装Serving包，要执行下面命令进行安装。
```bash
pip install paddle-serving-server-gpu
```
您可能需要使用国内镜像源（例如清华源）来加速下载。
```shell
pip install paddle-serving-server-gpu -i https://pypi.tuna.tsinghua.edu.cn/simple
```
### 测试example
在运行GPU版Server时需要通过`--gpu_ids`选项设置预测服务使用的GPU，缺省状态默认使用CPU。当设置的`--gpu_ids`超出环境变量`CUDA_VISIBLE_DEVICES`时会报错。下面的示例为指定使用索引为0的GPU：
```shell
export CUDA_VISIBLE_DEVICES=0,1
python -m paddle_serving_server_gpu.serve --model uci_housing_model --port 9292 --gpu_ids 0
```
通过下面命令获取训练好的Boston房价预估模型：
或者
```bash
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing.tar.gz
tar -xzf uci_housing.tar.gz
docker run --gpus all -p 9292:9292 --name test -dit hub.baidubce.com/paddlepaddle/serving:latest-cuda9.0-cudnn7
docker exec -it test bash
```
- 测试HTTP服务
在Server端（容器内）运行：
```bash
python -m paddle_serving_server_gpu.serve --model uci_housing_model --thread 10 --port 9292 --name uci --gpu_ids 0
```
在Client端（容器内或容器外）运行：
```bash
curl -H "Content-Type:application/json" -X POST -d '{"feed":{"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
```
- 测试RPC服务
在Server端（容器内）运行：
`-p`选项是为了将容器的`9292`端口映射到宿主机的`9292`端口。
```bash
python -m paddle_serving_server_gpu.serve --model uci_housing_model --thread 10 --port 9292 --gpu_ids 0
```
### 安装PaddleServing
在Client端（容器内或容器外，需要安装`paddle-serving-client`包）运行下面Python代码：
镜像里自带对应镜像tag版本的`paddle_serving_server_gpu`，`paddle_serving_client`，`paddle_serving_app`，如果用户不需要更改版本，可以直接使用，适用于没有外网服务的环境。
```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)
```
如果需要更换版本，请参照首页的指导，下载对应版本的pip包。
## 注意事项
......
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