Install DGL

At this stage, we recommend installing DGL from conda or pip.

System requirements

Currently DGL is tested on

  • Ubuntu 16.04
  • macOS X
  • Windows 10

DGL is expected to work on all Linux distributions later than Ubuntu 16.04, macOS X, and Windows 10.

DGL also requires the Python version to be 3.5 or later. Python 3.4 or less is not tested, and Python 2 support is coming.

DGL supports multiple tensor libraries (e.g. PyTorch, MXNet) as backends; refer Working with different backends for requirements on backends and how to select a backend.

Starting from 0.3 DGL is separated into CPU and CUDA builds. The builds share the same Python package name, so installing DGL with CUDA 9 build after installing the CPU build will overwrite the latter.

Install from conda

One can either grab miniconda or the full anaconda if conda has not been installed.

Once the conda environment is activated, run

conda install -c dglteam dgl              # For CPU Build
conda install -c dglteam dgl-cuda9.0      # For CUDA 9.0 Build
conda install -c dglteam dgl-cuda10.0     # For CUDA 10.0 Build

Install from pip

For CPU builds, one can simply run the following command to install via pip:

pip install dgl

For CUDA builds, one needs to specify the CUDA version:

pip install dgl           # For CPU Build
pip install dgl-cu90      # For CUDA 9.0 Build
pip install dgl-cu92      # For CUDA 9.2 Build
pip install dgl-cu100     # For CUDA 10.0 Build

We also provides nightly build from master branch, you can install it by:

pip install --pre dgl           # For CPU Build
pip install --pre dgl-cu90      # For CUDA 9.0 Build
pip install --pre dgl-cu92      # For CUDA 9.2 Build
pip install --pre dgl-cu100     # For CUDA 10.0 Build

Working with different backends

Currently DGL supports PyTorch and MXNet.

Switching backend

The backend is controlled by DGLBACKEND environment variable, which defaults to pytorch. Currently it supports the following values:

Value Backend Memo
pytorch PyTorch Requires 0.4.1 or later; see official website
mxnet MXNet

Requires MXNet 1.5

pip install mxnet

or cuda version (e.g. for cuda 9.0)

pip install mxnet-cu90
numpy NumPy Does not support gradient computation

Install from source

First, download the source files from GitHub:

git clone --recursive https://github.com/dmlc/dgl.git

One can also clone the repository first and run the following:

git submodule init
git submodule update

Linux

Install the system packages for building the shared library, for Debian/Ubuntu users, run:

sudo apt-get update
sudo apt-get install -y build-essential build-dep python3-dev make cmake

For Fedora/RHEL/CentOS users, run:

sudo yum install -y gcc-c++ python3-devel make cmake

Build the shared library. Use the configuration template cmake/config.cmake. Copy it to either the project directory or the build directory and change the configuration as you wish. For example, change USE_CUDA to ON will enable cuda build. You could also pass -DKEY=VALUE to the cmake command for the same purpose.

  • CPU-only build:
    mkdir build
    cd build
    cmake ..
    make -j4
    
  • Cuda build:
    mkdir build
    cd build
    cmake -DUSE_CUDA=ON ..
    make -j4
    

Finally, install the Python binding.

cd ../python
python setup.py install

macOS

Installation on macOS is similar to Linux. But macOS users need to install building tools like clang, GNU Make, cmake first.

Tools like clang and GNU Make are packaged in Command Line Tools for macOS. To install:

xcode-select --install

To install other needed packages like cmake, we recommend first installing Homebrew, which is a popular package manager for macOS. Detailed instructions can be found on its homepage.

After installation of Homebrew, install cmake by:

brew install cmake

Then go to root directory of DGL repository, build shared library and install Python binding for DGL:

mkdir build
cd build
cmake ..
make -j4
cd ../python
python setup.py install

We tested installation on macOS X with clang 10.0.0, GNU Make 3.81, and cmake 3.13.1.

Windows

Currently Windows source build is tested with CMake and MinGW/GCC. We highly recommend using CMake and GCC from conda installations. To do so, run

conda install cmake m2w64-gcc m2w64-make

Then build the shared library and install the Python binding:

md build
cd build
cmake -DCMAKE_CXX_FLAGS="-DDMLC_LOG_STACK_TRACE=0 -DDGL_EXPORTS" -DCMAKE_MAKE_PROGRAM=mingw32-make .. -G "MSYS Makefiles"
mingw32-make
cd ..\python
python setup.py install

We also support building DGL with MSBuild. With MS Build Tools and CMake on Windows installed, run the following in VS2017 x64 Native tools command prompt:

MD build
CD build
cmake -DCMAKE_CXX_FLAGS="/DDGL_EXPORTS" -DCMAKE_CONFIGURATION_TYPES="Release" .. -G "Visual Studio 15 2017 Win64"
msbuild dgl.sln
cd ..\python
python setup.py install