Install and Setup — DGL 2.2.1 documentation

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. We recommend installing DGL by conda or pip. Check out the instructions on the Get Started page. Note For Windows users: you will need to install Visual C++ 2015 Redistributable. Download the source files from GitHub. (Optional) Clone the repository first, and then run the following: Install the system packages for building the shared library. For Debian and Ubuntu users, run: For Fedora/RHEL/CentOS users, run: To create a Conda environment for CPU development, run: To create a Conda environment for GPU development, run: To further configure the conda environment, run the following command for more details:

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. We recommend installing DGL by conda or pip. Check out the instructions on the Get Started page. Note For Windows users: you will need to install Visual C++ 2015 Redistributable. Download the source files from GitHu