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 :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
----------------------
If ``conda`` is not yet installed, get either `miniconda <https://conda.io/miniconda.html>`_ or
the full `anaconda <https://www.anaconda.com/download/>`_.

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.

.. code:: bash

   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``.

.. code:: bash

   pip install dgl

For CUDA builds, run one of the following commands and specify the CUDA version.

.. code:: bash

   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
   pip install dgl-cu102     # For CUDA 10.2 Build

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

.. code:: bash

   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
   pip install --pre dgl-cu102     # For CUDA 10.2 Build


.. _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

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
   .. code:: bash

      mkdir build
      cd build
      cmake ..
      make -j4
- CUDA build
   .. code:: bash

      mkdir build
      cd build
      cmake -DUSE_CUDA=ON ..
      make -j4

Finally, install the Python binding.

.. code:: bash

   cd ../python
   python setup.py install

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 <https://brew.sh/>`_.

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 ..
   make -j4
   cd ../python
   python setup.py install

Windows
```````

The Windows source build is tested with CMake and MinGW/GCC.  We highly recommend
using CMake and GCC from `conda installations <https://conda.io/miniconda.html>`_.  To
get started, run the following:

.. code:: bash

   conda install cmake m2w64-gcc m2w64-make

Build the shared library and install the Python binding.

.. code::

   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

You can also build DGL with MSBuild.  With `MS Build Tools <https://go.microsoft.com/fwlink/?linkid=840931>`_
and `CMake on Windows <https://cmake.org/download/>`_ installed, run the following
in VS2017 x64 Native tools command prompt.

.. code::

   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