wehub-resource-sync
220 lines
6.3 KiB
ReStructuredText
220 lines
6.3 KiB
ReStructuredText
Install and Setup
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=================
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System requirements
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-------------------
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DGL works with the following operating systems:
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* Ubuntu 20.04+
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* CentOS 8+ (Although gcc 9 is needed)
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* RHEL 8+
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* macOS X
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* Windows 10
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DGL requires Python version 3.7, 3.8, 3.9, 3.10, 3.11.
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DGL supports multiple tensor libraries as backends, e.g., PyTorch, MXNet. For requirements on backends and how to select one, see :ref:`backends`.
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Starting at version 0.3, DGL is separated into CPU and CUDA builds. The builds share the
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same Python package name. If you install DGL with a CUDA 9 build after you install the
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CPU build, then the CPU build is overwritten.
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Install from Conda or Pip
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-------------------------
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We recommend installing DGL by ``conda`` or ``pip``.
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Check out the instructions on the `Get Started page <https://www.dgl.ai/pages/start.html>`_.
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.. note::
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For Windows users: you will need to install `Visual C++ 2015 Redistributable <https://www.microsoft.com/en-us/download/details.aspx?id=48145>`_.
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.. _install-from-source:
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Install from source
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-------------------
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Download the source files from GitHub.
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.. code:: bash
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git clone --recurse-submodules https://github.com/dmlc/dgl.git
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(Optional) Clone the repository first, and then run the following:
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.. code:: bash
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git submodule update --init --recursive
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Linux
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`````
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Install the system packages for building the shared library. For Debian and Ubuntu
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users, run:
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.. code:: bash
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sudo apt-get update
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sudo apt-get install -y build-essential python3-dev make cmake
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For Fedora/RHEL/CentOS users, run:
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.. code:: bash
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sudo yum install -y gcc-c++ python3-devel make cmake
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To create a Conda environment for CPU development, run:
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.. code:: bash
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bash script/create_dev_conda_env.sh -c
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To create a Conda environment for GPU development, run:
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.. code:: bash
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bash script/create_dev_conda_env.sh -g 11.7
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To further configure the conda environment, run the following command for more details:
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.. code:: bash
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bash script/create_dev_conda_env.sh -h
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To build the shared library for CPU development, run:
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.. code:: bash
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bash script/build_dgl.sh -c
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To build the shared library for GPU development, run:
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.. code:: bash
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bash script/build_dgl.sh -g
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To further build the shared library, run the following command for more details:
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.. code:: bash
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bash script/build_dgl.sh -h
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Finally, install the Python binding.
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.. code:: bash
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cd python
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python setup.py install
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# Build Cython extension
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python setup.py build_ext --inplace
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macOS
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`````
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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.
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Tools like clang and GNU Make are packaged in **Command Line Tools** for macOS. To
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install, run the following:
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.. code:: bash
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xcode-select --install
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To install other needed packages like cmake, we recommend first installing
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**Homebrew**, which is a popular package manager for macOS. To learn more, see the `Homebrew website <https://brew.sh/>`_.
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After you install Homebrew, install cmake.
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.. code:: bash
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brew install cmake
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Go to root directory of the DGL repository, build a shared library, and
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install the Python binding for DGL.
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.. code:: bash
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mkdir build
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cd build
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cmake -DUSE_OPENMP=off -DUSE_LIBXSMM=OFF ..
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make -j4
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cd ../python
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python setup.py install
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# Build Cython extension
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python setup.py build_ext --inplace
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Windows
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```````
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You can build DGL with MSBuild. With `MS Build Tools <https://go.microsoft.com/fwlink/?linkid=840931>`_
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and `CMake on Windows <https://cmake.org/download/>`_ installed, run the following
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in VS2019 x64 Native tools command prompt.
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* CPU only build::
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MD build
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CD build
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cmake -DCMAKE_CXX_FLAGS="/DDGL_EXPORTS" -DCMAKE_CONFIGURATION_TYPES="Release" -DDMLC_FORCE_SHARED_CRT=ON .. -G "Visual Studio 16 2019"
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msbuild dgl.sln /m
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CD ..\python
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python setup.py install
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* CUDA build::
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MD build
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CD build
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cmake -DCMAKE_CXX_FLAGS="/DDGL_EXPORTS" -DCMAKE_CONFIGURATION_TYPES="Release" -DDMLC_FORCE_SHARED_CRT=ON -DUSE_CUDA=ON .. -G "Visual Studio 16 2019"
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msbuild dgl.sln /m
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CD ..\python
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python setup.py install
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.. _backends:
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Working with different backends
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-------------------------------
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DGL supports PyTorch, MXNet and Tensorflow backends.
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DGL will choose the backend on the following options (high priority to low priority)
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* Use the ``DGLBACKEND`` environment variable:
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- You can use ``DGLBACKEND=[BACKEND] python gcn.py ...`` to specify the backend
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- Or ``export DGLBACKEND=[BACKEND]`` to set the global environment variable
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* Modify the ``config.json`` file under "~/.dgl":
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- You can use ``python -m dgl.backend.set_default_backend [BACKEND]`` to set the default backend
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Currently BACKEND can be chosen from mxnet, pytorch, tensorflow.
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PyTorch backend
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```````````````
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Export ``DGLBACKEND`` as ``pytorch`` to specify PyTorch backend. The required PyTorch
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version is 1.12.0 or later. See `pytorch.org <https://pytorch.org>`_ for installation instructions.
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MXNet backend
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`````````````
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Export ``DGLBACKEND`` as ``mxnet`` to specify MXNet backend. The required MXNet version is
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1.6 or later. See `mxnet.apache.org <https://mxnet.apache.org/get_started>`_ for installation
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instructions.
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MXNet uses uint32 as the default data type for integer tensors, which only supports graph of
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size smaller than 2^32. To enable large graph training, *build* MXNet with ``USE_INT64_TENSOR_SIZE=1``
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flag. See `this FAQ <https://mxnet.apache.org/api/faq/large_tensor_support>`_ for more information.
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MXNet 1.5 and later has an option to enable Numpy shape mode for ``NDArray`` objects, some DGL models
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need this mode to be enabled to run correctly. However, this mode may not compatible with pretrained
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model parameters with this mode disabled, e.g. pretrained models from GluonCV and GluonNLP.
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By setting ``DGL_MXNET_SET_NP_SHAPE``, users can switch this mode on or off.
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Tensorflow backend
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``````````````````
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Export ``DGLBACKEND`` as ``tensorflow`` to specify Tensorflow backend. The required Tensorflow
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version is 2.3.0 or later. See `tensorflow.org <https://www.tensorflow.org/install>`_ for installation
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instructions. In addition, DGL will set ``TF_FORCE_GPU_ALLOW_GROWTH`` to ``true`` to prevent Tensorflow take over the whole GPU memory:
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