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) - DGLBACKEND environment

  • You can use DGLBACKEND=[BACKEND] python gcn.py … to specify the backend

  • Or export DGLBACKEND=[BACKEND] to set the global environment variable

  • 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 1.1.0 or later. See pytorch.org for installation instructions.

MXNet backend

Export DGLBACKEND as mxnet to specify MXNet backend. The required MXNet version is 1.5 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 2.2.0 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:

pip install "tensorflow>=2.2.0rc1"  # when using tensorflow cpu version