.. _backends:

Working with different backends
===============================

DGL supports PyTorch, MXNet and Tensorflow backends. To change them, set the ``DGLBACKEND``
environcment variable. The default backend is PyTorch.

PyTorch backend
---------------

Export ``DGLBACKEND`` as ``pytorch`` to specify PyTorch backend. The required PyTorch
version is 0.4.1 or later. See `pytorch.org <https://pytorch.org>`_ for installation instructions.

MXNet backend
-------------

Export ``DGLBACKEND`` as ``mxnet`` to specify MXNet backend. The required MXNet version is
1.5 or later. See `mxnet.apache.org <https://mxnet.apache.org/get_started>`_ 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 <https://mxnet.apache.org/api/faq/large_tensor_support>`_ 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 2.0 or later. See `tensorflow.org <https://www.tensorflow.org/install>`_ for installation
instructions. In addition, Tensorflow backend requires ``tfdlpack`` package installed as follows and set ``TF_FORCE_GPU_ALLOW_GROWTH`` to ``true`` to prevent Tensorflow take over the whole GPU memory:

.. code:: bash

   pip install tfdlpack  # when using tensorflow cpu version


or

.. code:: bash

   pip install tfdlpack-gpu  # when using tensorflow gpu version
   export TF_FORCE_GPU_ALLOW_GROWTH=true # and add this to your .bashrc/.zshrc file if needed