Install DGL

This topic explains how to install DGL. We recommend installing DGL by using conda or pip.

System requirements

DGL works with the following operating systems:

  • Ubuntu 16.04
  • macOS X
  • Windows 10

DGL requires Python version 3.5 or later. Python 3.4 or earlier is not tested.

DGL supports multiple tensor libraries as backends, e.g., PyTorch, MXNet. For requirements on backends and how to select one, see Working with different backends.

Starting at version 0.3, DGL is separated into CPU and CUDA builds. The builds share the same Python package name. If you install DGL with a CUDA 9 build after you install the CPU build, then the CPU build is overwritten.

Install from conda

If conda is not yet installed, get either miniconda or the full anaconda.

With conda installed, you will want install DGL into Python 3.5 conda environment. Run conda create -n dgl python=3.5 to create the environment. Activate the environment by running source activate dgl. After the conda environment is activated, run one of the following commands.

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
conda install -c dglteam dgl-cuda10.1     # For CUDA 10.1 Build
conda install -c dglteam dgl-cuda10.2     # For CUDA 10.2 Build

Install from pip

For CPU builds, run the following command to install with pip.

pip install dgl

For CUDA builds, run one of the following commands and 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
pip install dgl-cu101     # For CUDA 10.1 Build

For the most current nightly build from master branch, run one of the following commands.

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
pip install --pre dgl-cu101     # For CUDA 10.1 Build

Install from source

Download the source files from GitHub.

git clone --recurse-submodules

(Optional) Clone the repository first, and then run the following:

git submodule update --init --recursive


Install the system packages for building the shared library. For Debian and Ubuntu users, run:

sudo apt-get update
sudo apt-get install -y build-essential 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 a 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 install


Installation on macOS is similar to Linux. But macOS users need to install build tools like clang, GNU Make, and cmake first. These installation steps were tested on macOS X with clang 10.0.0, GNU Make 3.81, and cmake 3.13.1.

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

xcode-select --install

To install other needed packages like cmake, we recommend first installing Homebrew, which is a popular package manager for macOS. To learn more, see the Homebrew website.

After you install Homebrew, install cmake.

brew install cmake

Go to root directory of the DGL repository, build a shared library, and install the Python binding for DGL.

mkdir build
cd build
cmake -DUSE_OPENMP=off ..
make -j4
cd ../python
python install


The Windows source build is tested with CMake and MinGW/GCC. We highly recommend using CMake and GCC from conda installations. To get started, run the following:

conda install cmake m2w64-gcc m2w64-make

Build the shared library and install the Python binding.

md build
cd build
cd ..\python
python install

You can also build 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 install