Install and Setup ================= System requirements ------------------- DGL works with the following operating systems: * Ubuntu 20.04+ * CentOS 8+ * RHEL 8+ * macOS X * Windows 10 DGL requires Python version 3.7, 3.8, 3.9, 3.10, 3.11. DGL supports multiple tensor libraries as backends, e.g., PyTorch, MXNet. For requirements on backends and how to select one, see :ref:`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 or Pip ------------------------- 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 `_. .. _install-from-source: Install from source ------------------- Download the source files from GitHub. .. code:: bash git clone --recurse-submodules https://github.com/dmlc/dgl.git (Optional) Clone the repository first, and then run the following: .. code:: bash git submodule update --init --recursive Linux ````` Install the system packages for building the shared library. For Debian and Ubuntu users, run: .. code:: bash sudo apt-get update sudo apt-get install -y build-essential python3-dev make cmake For Fedora/RHEL/CentOS users, run: .. code:: bash sudo yum install -y gcc-c++ python3-devel make cmake To create a Conda environment for CPU development, run: .. code:: bash bash script/create_dev_conda_env.sh -c To create a Conda environment for GPU development, run: .. code:: bash bash script/create_dev_conda_env.sh -g 11.7 To further configure the conda environment, run the following command for more details: .. code:: bash bash script/create_dev_conda_env.sh -h To build the shared library for CPU development, run: .. code:: bash bash script/build_dgl.sh -c To build the shared library for GPU development, run: .. code:: bash bash script/build_dgl.sh -g To further build the shared library, run the following command for more details: .. code:: bash bash script/build_dgl.sh -h Finally, install the Python binding. .. code:: bash cd python python setup.py install # Build Cython extension python setup.py build_ext --inplace macOS ````` 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: .. code:: bash 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. .. code:: bash brew install cmake Go to root directory of the DGL repository, build a shared library, and install the Python binding for DGL. .. code:: bash mkdir build cd build cmake -DUSE_OPENMP=off -DUSE_LIBXSMM=OFF .. make -j4 cd ../python python setup.py install # Build Cython extension python setup.py build_ext --inplace Windows ``````` You can build DGL with MSBuild. With `MS Build Tools `_ and `CMake on Windows `_ installed, run the following in VS2019 x64 Native tools command prompt. * CPU only build:: MD build CD build cmake -DCMAKE_CXX_FLAGS="/DDGL_EXPORTS" -DCMAKE_CONFIGURATION_TYPES="Release" -DDMLC_FORCE_SHARED_CRT=ON .. -G "Visual Studio 16 2019" msbuild dgl.sln /m CD ..\python python setup.py install * CUDA build:: MD build CD build cmake -DCMAKE_CXX_FLAGS="/DDGL_EXPORTS" -DCMAKE_CONFIGURATION_TYPES="Release" -DDMLC_FORCE_SHARED_CRT=ON -DUSE_CUDA=ON .. -G "Visual Studio 16 2019" msbuild dgl.sln /m CD ..\python python setup.py install .. _backends: Working with different backends ------------------------------- DGL supports PyTorch, MXNet and Tensorflow backends. DGL will choose the backend on the following options (high priority to low priority) * Use the ``DGLBACKEND`` environment variable: - You can use ``DGLBACKEND=[BACKEND] python gcn.py ...`` to specify the backend - Or ``export DGLBACKEND=[BACKEND]`` to set the global environment variable * Modify the ``config.json`` file under "~/.dgl": - You can use ``python -m dgl.backend.set_default_backend [BACKEND]`` to set the default backend Currently BACKEND can be chosen from mxnet, pytorch, tensorflow. PyTorch backend ``````````````` Export ``DGLBACKEND`` as ``pytorch`` to specify PyTorch backend. The required PyTorch version is 1.12.0 or later. See `pytorch.org `_ for installation instructions. MXNet backend ````````````` Export ``DGLBACKEND`` as ``mxnet`` to specify MXNet backend. The required MXNet version is 1.6 or later. See `mxnet.apache.org `_ for installation instructions. MXNet uses uint32 as the default data type for integer tensors, which only supports graph of size smaller than 2^32. To enable large graph training, *build* MXNet with ``USE_INT64_TENSOR_SIZE=1`` flag. See `this FAQ `_ for more information. MXNet 1.5 and later has an option to enable Numpy shape mode for ``NDArray`` objects, some DGL models need this mode to be enabled to run correctly. However, this mode may not compatible with pretrained model parameters with this mode disabled, e.g. pretrained models from GluonCV and GluonNLP. By setting ``DGL_MXNET_SET_NP_SHAPE``, users can switch this mode on or off. Tensorflow backend `````````````````` Export ``DGLBACKEND`` as ``tensorflow`` to specify Tensorflow backend. The required Tensorflow version is 2.3.0 or later. See `tensorflow.org `_ for installation instructions. In addition, DGL will set ``TF_FORCE_GPU_ALLOW_GROWTH`` to ``true`` to prevent Tensorflow take over the whole GPU memory: