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Install Paddle Serving with Docker

(简体中文|English)

Strongly recommend you build Paddle Serving in Docker. For more images, please refer to Docker Image List.

Tip-1: This project only supports Python3.6/3.7/3.8/3.9, all subsequent operations related to Python/Pip need to select the correct Python version.

Tip-2: The GPU environments in the following examples are all cuda10.2-cudnn7. If you use Python Pipeline to deploy and need Nvidia TensorRT to optimize prediction performance, please refer to Supported Mirroring Environment and Instructions to choose other versions.

1. Start the Docker Container

Both Serving Dev Image and Paddle Dev Image are supported at the same time. You can choose 1 from the operation 2 in chapters 1.1 and 1.2.Deploying the Serving service on the Paddle docker image requires the installation of additional dependency libraries. Therefore, we directly use the Serving development image.

1.1 Serving Dev Images (CPU/GPU 2 choose 1)

CPU:

# Start CPU Docker Container
docker pull registry.baidubce.com/paddlepaddle/serving:0.8.0-devel
docker run -p 9292:9292 --name test -dit registry.baidubce.com/paddlepaddle/serving:0.8.0-devel bash
docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving

GPU:

# Start GPU Docker Container
nvidia-docker pull registry.baidubce.com/paddlepaddle/serving:0.8.0-cuda10.2-cudnn7-devel
nvidia-docker run -p 9292:9292 --name test -dit registry.baidubce.com/paddlepaddle/serving:0.8.0-cuda10.2-cudnn7-devel bash
nvidia-docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving

1.2 Paddle Dev Images (choose any codeblock of CPU/GPU)

CPU:

# Start CPU Docker Container
docker pull registry.baidubce.com/paddlepaddle/paddle:2.2.2
docker run -p 9292:9292 --name test -dit registry.baidubce.com/paddlepaddle/paddle:2.2.2 bash
docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving

# Paddle dev image needs to run the following script to increase the dependencies required by Serving
bash Serving/tools/paddle_env_install.sh

GPU:

# Start GPU Docker
nvidia-docker pull registry.baidubce.com/paddlepaddle/paddle:2.2.2-gpu-cuda10.2-cudnn7
nvidia-docker run -p 9292:9292 --name test -dit registry.baidubce.com/paddlepaddle/paddle:2.2.2-gpu-cuda10.2-cudnn7 bash
nvidia-docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving

# Paddle development image needs to execute the following script to increase the dependencies required by Serving
bash Serving/tools/paddle_env_install.sh

2. Install Paddle Serving stable wheel packages

Install the required pip dependencies

cd Serving
pip3 install -r python/requirements.txt

Install the service whl package. There are three types of client, app and server. The server is divided into CPU and GPU. Choose one installation according to the environment.

  • GPU with CUDA10.2 + Cudnn7 + TensorRT6(Recommended)
pip3 install paddle-serving-client==0.8.3 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install paddle-serving-app==0.8.3 -i https://pypi.tuna.tsinghua.edu.cn/simple

# CPU Server
pip3 install paddle-serving-server==0.8.3 -i https://pypi.tuna.tsinghua.edu.cn/simple

# GPU environments need to confirm the environment before choosing which one to execute
pip3 install paddle-serving-server-gpu==0.8.3.post102 -i https://pypi.tuna.tsinghua.edu.cn/simple 
pip3 install paddle-serving-server-gpu==0.8.3.post101 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install paddle-serving-server-gpu==0.8.3.post112 -i https://pypi.tuna.tsinghua.edu.cn/simple

By default, the domestic Tsinghua mirror source is turned on to speed up the download. If you use a proxy, you can turn it off（-i https://pypi.tuna.tsinghua.edu.cn/simple).

If you need to use the installation package compiled by the develop branch, please download the download address from Download wheel packages, and use the pip install command to install. If you want to compile by yourself, please refer to Paddle Serving Compilation Document.

The paddle-serving-server and paddle-serving-server-gpu installation packages support Centos 6/7, Ubuntu 16/18 and Windows 10.

The paddle-serving-client and paddle-serving-app installation packages support Linux and Windows, and paddle-serving-client only supports python3.6/3.7/3.8/3.9.

If you used the CUDA10.2 environment of paddle serving 0.5.X 0.6.X before, you need to pay attention to version 0.8.0, paddle-serving-server-gpu==0.8.0.post102 uses Cudnn7 and TensorRT6, and 0.6.0.post102 uses cudnn8 and TensorRT7. If 0.6.0 cuda10.2 users need to upgrade, please use paddle-serving-server-gpu==0.8.0.post1028

3. Install Paddle related Python libraries

**You only need to install it when you use the paddle_serving_client.convert command or the Python Pipeline framework. **

# CPU environment please execute
pip3 install paddlepaddle==2.2.2 -i https://pypi.tuna.tsinghua.edu.cn/simple

# GPU CUDA 10.2 environment please execute
pip3 install paddlepaddle-gpu==2.2.2 -i https://pypi.tuna.tsinghua.edu.cn/simple

Note: If your CUDA version is not 10.2 or if you want to use TensorRT(CUDA10.2 included), please do not execute the above commands directly, you need to refer to Paddle-Inference official document-download and install the Linux prediction library Select the URL link of the corresponding GPU environment and install it. Assuming that you use Python3.6, please follow the codeblock.

# CUDA10.1 + CUDNN7 + TensorRT6
pip3 install https://paddle-inference-lib.bj.bcebos.com/2.2.2/python/Linux/GPU/x86-64_gcc8.2_avx_mkl_cuda10.1_cudnn7.6.5_trt6.0.1.5/paddlepaddle_gpu-2.2.2.post101-cp36-cp36m-linux_x86_64.whl

# CUDA10.2 + CUDNN7 + TensorRT6, Attenton that the paddle whl for this env is same to that of CUDA10.1 + Cudnn7 + TensorRT6
pip3 install https://paddle-inference-lib.bj.bcebos.com/2.2.2/python/Linux/GPU/x86-64_gcc8.2_avx_mkl_cuda10.1_cudnn7.6.5_trt6.0.1.5/paddlepaddle_gpu-2.2.2.post101-cp36-cp36m-linux_x86_64.whl

# CUDA10.2 + Cudnn8 + TensorRT7
pip3 install https://paddle-inference-lib.bj.bcebos.com/2.2.2/python/Linux/GPU/x86-64_gcc8.2_avx_mkl_cuda10.2_cudnn8.1.1_trt7.2.3.4/paddlepaddle_gpu-2.2.2-cp36-cp36m-linux_x86_64.whl

# CUDA11.2 + CUDNN8 + TensorRT8
pip3 install https://paddle-inference-lib.bj.bcebos.com/2.2.2/python/Linux/GPU/x86-64_gcc8.2_avx_mkl_cuda11.2_cudnn8.2.1_trt8.0.3.4/paddlepaddle_gpu-2.2.2.post112-cp36-cp36m-linux_x86_64.whl

4. Supported Docker Images and Instruction

Environment Serving Development Image Tag Operating System Paddle Development Image Tag Operating System
CPU 0.8.0-devel Ubuntu 16.04 2.2.2 Ubuntu 18.04.
CUDA10.1 + CUDNN7 0.8.0-cuda10.1-cudnn7-devel Ubuntu 16.04 无 无
CUDA10.2 + CUDNN7 0.8.0-cuda10.2-cudnn7-devel Ubuntu 16.04 2.2.2-gpu-cuda10.2-cudnn7 Ubuntu 16.04
CUDA10.2 + CUDNN8 0.8.0-cuda10.2-cudnn8-devel Ubuntu 16.04 无 无
CUDA11.2 + CUDNN8 0.8.0-cuda11.2-cudnn8-devel Ubuntu 16.04 2.2.2-gpu-cuda11.2-cudnn8 Ubuntu 18.04

For Windows 10 users, please refer to the document Paddle Serving Guide for Windows Platform.

5. Installation Check

After the above steps are completed, you can use the command line to run the environment check function to automatically run the Paddle Serving related examples to verify the environment-related configuration.

python3 -m paddle_serving_server.serve check

For details, please refer to the Installation Env Check