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NVIDIA Deep Learning Examples
SE(3)-Transformers for PyTorch
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NVIDIA Deep Learning Examples
NVIDIA Deep Learning Examples
SE(3)-Transformers for PyTorch

A Graph Neural Network using a variant of self-attention for 3D points and graphs processing.

Changelog

August 2022:

  • Slight performance improvements
  • Upgraded base container

November 2021:

  • Improved low memory mode to give further 6x memory savings
  • Disabled W&B logging by default
  • Fixed persistent workers when using one data loading process

October 2021:

  • Updated README performance tables
  • Fixed shape mismatch when using partially fused TFNs per output degree
  • Fixed shape mismatch when using partially fused TFNs per input degree with edge degrees > 0

September 2021:

  • Moved to new location (from PyTorch/DrugDiscovery to DGLPyTorch/DrugDiscovery)
  • Fixed multi-GPUs training script

August 2021

  • Initial release

Known issues

If you encounter OSError: [Errno 12] Cannot allocate memory during the Dataloader iterator creation (more precisely during the fork(), this is most likely due to the use of the --precompute_bases flag. If you cannot add more RAM or Swap to your machine, it is recommended to turn off bases precomputation by removing the --precompute_bases flag or using --precompute_bases false.