Appendix

Compile Dependency Table

Dependency package name Version Description Installation command
CMake 3.4
GCC 4.8 / 5.4 recommends using devtools2 for CentOS
Python 2.7.x. depends on libpython2.7.so apt install python-dev or yum install python-devel
SWIG at least 2.0 apt install swig or yum install swig
wget any apt install wget or yum install wget
openblas any
pip at least 9.0.1 apt install python-pip or yum install Python-pip
numpy >=1.12.0 pip install numpy==1.14.0
protobuf 3.1.0 pip install protobuf==3.1.0
wheel any pip install wheel
patchELF any apt install patchelf or read github patchELF official documentation
go >=1.8 optional

Compile Option Table

Option Description Default
WITH_GPU Whether to support GPU ON
WITH_C_API Whether to compile CAPI OFF
WITH_DOUBLE Whether to use double precision floating point numeber OFF
WITH_DSO whether to load CUDA dynamic libraries dynamically at runtime, instead of statically loading CUDA dynamic libraries. ON
WITH_AVX whether to compile PaddlePaddle binaries file containing the AVX instruction set ON
WITH_PYTHON Whether the PYTHON interpreter is embedded ON
WITH_STYLE_CHECK Whether to perform code style checking at compile time ON
WITH_TESTING Whether to turn on unit test OFF
WITH_DOC Whether to compile Chinese and English documents OFF
WITH_SWIG_PY Whether to compile PYTHON's SWIG interface, which can be used for predicting and customizing training Auto
WITH_GOLANG Whether to compile the fault-tolerant parameter server of the go language OFF
WITH_MKL Whether to use the MKL math library, if not,using OpenBLAS ON
WITH_SYSTEM_BLAS Whether to use the system's BLAS OFF
WITH_DISTRIBUTE Whether to Compile with distributed version OFF
WITH_RDMA Whether to compile the relevant parts that supports RDMA OFF
WITH_BRPC_RDMA Whether to use BRPC RDMA as RPC protocol OFF
ON_INFER Whether to turn on prediction optimization OFF
DWITH_ANAKIN Whether to Compile ANAKIN OFF

BLAS

PaddlePaddle supports two BLAS libraries, MKL and OpenBlAS. MKL is used by default. If you use MKL and the machine contains the AVX2 instruction set, you will also download the MKL-DNN math library, for details please refer to here.

If you close MKL, OpenBLAS will be used as the BLAS library.

CUDA/cuDNN

PaddlePaddle automatically finds the CUDA and cuDNN libraries installed in the system for compilation and execution at compile time/runtime. Use the parameter -DCUDA_ARCH_NAME=Auto to specify to enable automatic detection of the SM architecture and speed up compilation.

PaddlePaddle can be compiled and run using any version after cuDNN v5.1, but try to keep the same version of cuDNN in the compiling and running processes. We recommend using the latest version of cuDNN.

Configure Compile Options

PaddePaddle implements references to various BLAS/CUDA/cuDNN libraries by specifying paths at compile time. When cmake compiles, it first searches the system paths ( /usr/liby and /usr/local/lib ) for these libraries, and also reads the relevant path variables for searching. Can be set by using the -D command, for example:

Cmake .. -DWITH_GPU=ON -DWITH_TESTING=OFF -DCUDNN_ROOT=/opt/cudnnv5

Note: The settings introduced here for these compilation options are only valid for the first cmake. If you want to reset it later, it is recommended to clean up the entire build directory ( rm -rf ) and then specify it.


Installation Package List

Version Number Release Discription
paddlepaddle==[version code] such as paddlepaddle==1.2.0 Only support the corresponding version of the CPU PaddlePaddle, please refer to Pypi for the specific version.
paddlepaddle-gpu==1.2.0 Using version 1.2.0 compiled with CUDA 9.0 and cuDNN 7
paddlepaddle-gpu==1.2.0.post87 Using version 1.2.0 compiled with CUDA 8.0 and cuDNN 7
paddlepaddle-gpu==1.2.0.post85 Using version 1.2.0 compiled with CUDA 8.0 and cuDNN 5

You can find various distributions of PaddlePaddle-gpu in the Release History.

Please note that: paddlepaddle-gpu in windows, will download package compiled with CUDA 8.0 and cuDNN 7


Installation Mirrors and Introduction

Version Number Release Description
hub.baidubce.com/paddlepaddle/paddle:latest The latest pre-installed image of the PaddlePaddle CPU version
hub.baidubce.com/paddlepaddle/paddle:latest-dev The latest PaddlePaddle development environment
hub.baidubce.com/paddlepaddle/paddle:[Version] Replace version with a specific version, preinstalled PaddlePaddle image in historical version
hub.baidubce.com/paddlepaddle/paddle:latest-gpu The latest pre-installed image of the PaddlePaddle GPU version

You can find the docker image for each release of PaddlePaddle in the DockerHub.


Multi-version whl package list - Release

Release Instruction cp27-cp27mu cp27-cp27m cp35-cp35m cp36-cp36m cp37-cp37m
cpu-noavx-mkl paddlepaddle-1.4.1-cp27-cp27mu-linux_x86_64.whl paddlepaddle-1.4.1-cp27-cp27m-linux_x86_64.whl paddlepaddle-1.4.1-cp35-cp35m-linux_x86_64.whl paddlepaddle-1.4.1-cp36-cp36m-linux_x86_64.whl paddlepaddle-1.4.1-cp37-cp37m-linux_x86_64.whl
cpu_avx_mkl paddlepaddle-1.4.1-cp27-cp27mu-linux_x86_64.whl paddlepaddle-1.4.1-cp27-cp27m-linux_x86_64.whl paddlepaddle-1.4.1-cp35-cp35m-linux_x86_64.whl paddlepaddle-1.4.1-cp36-cp36m-linux_x86_64.whl paddlepaddle-1.4.1-cp37-cp37m-linux_x86_64.whl
cpu_avx_openblas paddlepaddle-1.4.1-cp27-cp27mu-linux_x86_64.whl paddlepaddle-1.4.1-cp27-cp27m-linux_x86_64.whl paddlepaddle-1.4.1-cp35-cp35m-linux_x86_64.whl paddlepaddle-1.4.1-cp36-cp36m-linux_x86_64.whl paddlepaddle-1.4.1-cp37-cp37m-linux_x86_64.whl
cpu_noavx_openblas paddlepaddle-1.4.1-cp27-cp27mu-linux_x86_64.whl paddlepaddle-1.4.1-cp27-cp27m-linux_x86_64.whl paddlepaddle-1.4.1-cp35-cp35m-linux_x86_64.whl paddlepaddle-1.4.1-cp36-cp36m-linux_x86_64.whl -
cuda8.0_cudnn5_avx_mkl paddlepaddle_gpu-1.4.1-cp27-cp27mu-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp27-cp27m-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp35-cp35m-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp36-cp36m-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp37-cp37m-linux_x86_64.whl
cuda8.0_cudnn7_noavx_mkl paddlepaddle_gpu-1.4.1-cp27-cp27mu-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp27-cp27m-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp35-cp35m-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp36-cp36m-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp37-cp37m-linux_x86_64.whl
cuda8.0_cudnn7_avx_mkl paddlepaddle_gpu-1.4.1.post87-cp27-cp27mu-linux_x86_64.whl paddlepaddle_gpu-1.4.1.post87-cp27-cp27m-linux_x86_64.whl paddlepaddle_gpu-1.4.1.post87-cp35-cp35m-linux_x86_64.whl paddlepaddle_gpu-1.4.1.post87-cp36-cp36m-linux_x86_64.whl paddlepaddle_gpu-1.4.1.post87-cp37-cp37m-linux_x86_64.whl
cuda9.0_cudnn7_avx_mkl paddlepaddle_gpu-1.4.1-cp27-cp27mu-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp27-cp27m-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp35-cp35m-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp36-cp36m-linux_x86_64.whl paddlepaddle_gpu-1.4.1-cp37-cp37m-linux_x86_64.whl
win_cpu_noavx_openblas - paddlepaddle-1.4.1-cp27-cp27m-win_amd64.whl paddlepaddle-1.4.1-cp35-cp35m-win_amd64.whl paddlepaddle-1.4.1-cp36-cp36m-win_amd64.whl paddlepaddle-1.4.1-cp37-cp37m-win_amd64.whl
win_cpu_noavx_mkl - paddlepaddle-1.4.1-cp27-cp27m-win_amd64.whl paddlepaddle-1.4.1-cp35-cp35m-win_amd64.whl paddlepaddle-1.4.1-cp36-cp36m-win_amd64.whl paddlepaddle-1.4.1-cp37-cp37m-win_amd64.whl
win_cpu_avx_openblas - paddlepaddle-1.4.1-cp27-cp27m-win_amd64.whl paddlepaddle-1.4.1-cp35-cp35m-win_amd64.whl paddlepaddle-1.4.1-cp36-cp36m-win_amd64.whl paddlepaddle-1.4.1-cp37-cp37m-win_amd64.whl
win_cuda8.0_cudnn7_gpu_avx_openblas - paddlepaddle_gpu-1.4.1-cp27-cp27m-win_amd64.whl paddlepaddle_gpu-1.4.1-cp35-cp35m-win_amd64.whl paddlepaddle_gpu-1.4.1-cp36-cp36m-win_amd64.whl paddlepaddle_gpu-1.4.1-cp37-cp37m-win_amd64.whl
win_cuda8.0_cudnn7_gpu_noavx_mkl - paddlepaddle_gpu-1.4.1-cp27-cp27m-win_amd64.whl paddlepaddle_gpu-1.4.1-cp35-cp35m-win_amd64.whl paddlepaddle_gpu-1.4.1-cp36-cp36m-win_amd64.whl paddlepaddle_gpu-1.4.1-cp37-cp37m-win_amd64.whl
win_cuda8.0_cudnn7_gpu_noavx_openblas - paddlepaddle_gpu-1.4.1-cp27-cp27m-win_amd64.whl paddlepaddle_gpu-1.4.1-cp35-cp35m-win_amd64.whl paddlepaddle_gpu-1.4.1-cp36-cp36m-win_amd64.whl paddlepaddle_gpu-1.4.1-cp37-cp37m-win_amd64.whl
mac_cpu - paddlepaddle-1.4.1-cp27-cp27m-macosx_10_6_intel.whl paddlepaddle-1.4.1-cp35-cp35m-macosx_10_6_intel.whl paddlepaddle-1.4.1-cp36-cp36m-macosx_10_6_intel.whl paddlepaddle-1.4.1-cp37-cp37m-macosx_10_6_intel.whl

Multi-version whl package list - dev

Release Instruction cp27-cp27mu cp27-cp27m cp35-cp35m cp36-cp36m cp37-cp37m
cpu-noavx-mkl paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl paddlepaddle-latest-cp35-cp35m-linux_x86_64.whl paddlepaddle-latest-cp36-cp36m-linux_x86_64.whl paddlepaddle-latest-cp37-cp37m-linux_x86_64.whl
cpu_avx_mkl paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl paddlepaddle-latest-cp35-cp35m-linux_x86_64.whl paddlepaddle-latest-cp36-cp36m-linux_x86_64.whl paddlepaddle-latest-cp37-cp37m-linux_x86_64.whl
cpu_avx_openblas paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl paddlepaddle-latest-cp35-cp35m-linux_x86_64.whl paddlepaddle-latest-cp36-cp36m-linux_x86_64.whl paddlepaddle-latest-cp37-cp37m-linux_x86_64.whl
cuda8.0_cudnn5_avx_mkl paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl paddlepaddle_gpu-latest-cp35-cp35m-linux_x86_64.whl paddlepaddle_gpu-latest-cp36-cp36m-linux_x86_64.whl paddlepaddle_gpu-latest-cp37-cp37m-linux_x86_64.whl
cuda8.0_cudnn7_noavx_mkl paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl paddlepaddle_gpu-latest-cp35-cp35m-linux_x86_64.whl paddlepaddle_gpu-latest-cp36-cp36m-linux_x86_64.whl paddlepaddle_gpu-latest-cp37-cp37m-linux_x86_64.whl
cuda8.0_cudnn7_avx_mkl paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl paddlepaddle_gpu-latest-cp35-cp35m-linux_x86_64.whl paddlepaddle_gpu-latest-cp36-cp36m-linux_x86_64.whl paddlepaddle_gpu-latest-cp37-cp37m-linux_x86_64.whl
cuda9.0_cudnn7_avx_mkl paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl paddlepaddle_gpu-latest-cp35-cp35m-linux_x86_64.whl paddlepaddle_gpu-latest-cp36-cp36m-linux_x86_64.whl paddlepaddle_gpu-latest-cp37-cp37m-linux_x86_64.whl

Execute the PaddlePaddle training program in Docker


Suppose you have written a PaddlePaddle program in the current directory (such as /home/work): train.py ( refer to PaddlePaddleBook to write), you can start the training with the following command:

cd /home/work
docker run -it -v $PWD:/work hub.baidubce.com/paddlepaddle/paddle /work/train.py

In the above commands, the -it parameter indicates that the container has been run interactively; -v $PWD:/work specifies that the current path (the absolute path where the PWD variable in Linux will expand to the current path) is mounted to the :/work directory inside the container: Hub.baidubce.com/paddlepaddle/paddle specifies the container to be used; finally /work/train.py is the command executed inside the container, ie. the training program.

Of course, you can also enter into the Docker container and execute or debug your code interactively:

docker run -it -v $PWD:/work hub.baidubce.com/paddlepaddle/paddle /bin/bash
cd /work
python train.py

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 (which installs vim for you) in the container.

Start PaddlePaddle Book tutorial with Docker


Use Docker to quickly launch a local Jupyter Notebook containing the PaddlePaddle official Book tutorial, which can be viewed on the web. PaddlePaddle Book is an interactive Jupyter Notebook for users and developers. If you want to learn more about deep learning, PaddlePaddle Book is definitely your best choice. You can read tutorials or create and share interactive documents with code, formulas, charts, and text.

We provide a Docker image that can run the PaddlePaddle Book directly, running directly:

docker run -p 8888:8888 hub.baidubce.com/paddlepaddle/book

Domestic users can use the following image source to speed up access:

docker run -p 8888:8888 hub.baidubce.com/paddlepaddle/book

Then enter the following URL in your browser:

http://localhost:8888/

It's that simple and bon voyage! For further questions, please refer to the FAQ.

Perform GPU training using Docker


In order to ensure that the GPU driver works properly in the image, we recommend using nvidia-docker to run the image. Don't forget to install the latest GPU drivers on your physical machine in advance.

Nvidia-docker run -it -v $PWD:/work hub.baidubce.com/paddlepaddle/paddle:latest-gpu /bin/bash

Note: If you don't have nvidia-docker installed, you can try the following to mount the CUDA library and Linux devices into the Docker container:

export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') \
$(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
docker run ${CUDA_SO} \
 ${DEVICES} -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu

About AVX:

AVX is a set of CPU instructions that speeds up the calculation of PaddlePaddle. The latest PaddlePaddle Docker image is enabled by default for AVX compilation, so if your computer does not support AVX, you need to compile PaddlePaddle to no-avx version separately.

The following instructions can check if the Linux computer supports AVX:

if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi

If the output is No, you need to choose a mirror that uses no-AVX.