Compile on Ubuntu from Source Code¶

Determine which version to compile¶

• CPU version of PaddlePaddle, if your system does not have an NVIDIA® GPU, you must install this version. This version is easier than the GPU version. So even if you have a GPU on your computer, we recommend that you first install the CPU version of PaddlePaddle to check if your local environment is suitable.

• GPU version of PaddlePaddle, in order to make the PaddlePaddle program run more quickly, we usually use the GPU to accelerate the PaddlePaddle program, but the GPU version of PaddlePaddle needs to have the NVIDIA® GPU that meets the following conditions (see NVIDIA for the specific installation process and configuration). Official documentation: For CUDA, For cuDNN)

  • CUDA Toolkit 9.0 with cuDNN v7

  • CUDA Toolkit 8.0 with cuDNN v7

  • Hardware devices with GPU compute capability exceeding 1.0

Choose a compilation method¶

Under Ubuntu’s system we offer 2 ways to compile:

• Docker source compilation (this image already contains python2.7, python3.6, python3.7 environment)

• Direct native source code compilation (does not support GPU version under ubuntu18.04)

We recommend using Docker for compilation because we are installing both the tools and the configuration in a Docker image. This way, if you encounter problems, others can reproduce the problem to help. In addition, for developers accustomed to using Windows and MacOS, there is no need to configure a cross-compilation environment using Docker. Someone uses a virtual machine to analogize to Docker. It should be emphasized that Docker does not virtualize any hardware. The compiler tools running in the Docker container are actually running directly on the native CPU and operating system. The performance is the same as installing the compiler on the machine.

We also provide methods that can be compiled from local source code, but since the situation on host machine is more complicated, we only provide support for specific systems.

Compile with Docker¶

In order to better use Docker and avoid problems, we recommend using the highest version of Docker. For details on installing and using Docker, please refer to the official Docker documentation.

Please note that to install and use the PaddlePaddle version that supports GPU, you must first install nvidia-docker

Once you have properly installed Docker, you can start compiling PaddlePaddle with Docker:

1. First select the path where you want to store PaddlePaddle, then use the following command to clone PaddlePaddle’s source code from github to a folder named Paddle in the local current directory:

   git clone https://github.com/PaddlePaddle/Paddle.git

2. Go to the Paddle directory: cd Paddle

3. Take advantage of the image we provided (with this command you don’t have to download the image in advance):

   docker run --name paddle-test -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-dev /bin/bash

   –name paddle-test names the Docker container you created as paddle-test, -v $PWD:/paddle mounts the current directory to the /paddle directory in the Docker container (the PWD variable in Linux will expand to the current path’s absolute path), -it keeps interacting with the host, hub.baidubce.com/paddlepaddle/paddle:latest-dev creates a Docker container with a mirror named hub.baidubce.com/paddlepaddle/paddle:latest-dev, /bin /bash Starts the /bin/bash command after entering the container.

4. After entering Docker, go to the paddle directory: cd paddle

5. Switch to a more stable release branch to compile: (Note that python 3.6, python 3.7 version are supported from the 1.2 branch)

   git checkout release/1.2

6. Create and enter the /paddle/build path:

   mkdir -p /paddle/build && cd /paddle/build

7. Use the following command to install the dependencies: (For Python3: Please select the pip for the python version you wish to use, such as pip3.5, pip3.6)

    For Python2: pip install protobuf==3.1.0
    For Python3: pip3.5 install protobuf==3.1.0
   

   Install protobuf 3.1.0.

   apt install patchelf

   Installing patchelf, PatchELF is a small and useful program for modifying the dynamic linker and RPATH of ELF executables.

8. Execute cmake:

   For the meaning of the specific compiling options, compilation options table is your resort. Please note that the parameter -DPY_VERSION is the python version used in your current environment.

   • For users who need to compile the CPU version PaddlePaddle:

     cmake .. -DPY_VERSION=3.5 -DWITH_FLUID_ONLY=ON -DWITH_GPU=OFF -DWITH_TESTING=OFF -DCMAKE_BUILD_TYPE=Release

   • For users who need to compile the GPU version PaddlePaddle:

     cmake .. -DPY_VERSION=3.5 -DWITH_FLUID_ONLY=ON -DWITH_GPU=ON -DWITH_TESTING=OFF -DCMAKE_BUILD_TYPE=Release

9. Execute compilation:

   make -j$(nproc)

   Use multicore compilation

10. After compiling successfully, go to the /paddle/build/python/dist directory and find the generated .whl package: cd /paddle/build/python/dist

11. Install the compiled .whl package on the current machine or target machine: (For Python3: Please select the pip corresponding to the python version you wish to use, such as pip3.5, pip3.6)

    For Python2: pip install (whl package name)
    For Python3: pip3.5 install (whl package name)
    

Now that you have successfully installed PaddlePaddle using Docker, you only need to run PaddlePaddle after entering the Docker container. For more Docker usage, please refer to the official Docker documentation.

Note: In order to reduce the size, vim is not installed in PaddlePaddle Docker image by default. You can edit the code in the container after executing apt-get install -y vim in the container.

Congratulations, you have now completed the process of compiling PaddlePaddle using Docker.