PyTorch Release 18.10

The NVIDIA container image of PyTorch, release 18.10, is available.

Contents of PyTorch

This container image contains the complete source of the version of PyTorch in /opt/pytorch. It is pre-built and installed in the pytorch-py3.6 Conda™ environment in the container image.

Driver Requirements

Release 18.10 is based on CUDA 10, which requires NVIDIA Driver release 410.xx. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384. For more information, see CUDA Compatibility and Upgrades.

Key Features and Enhancements

This PyTorch release includes the following key features and enhancements.
  • PyTorch container image version 18.10 is based on PyTorch v0.4.1+ with up-to-date features from the PyTorch v1.0 preview (master branch up to PR 11834). PyTorch 0.4.1+ is released and included with this container.
  • When possible PyTorch will now automatically use cuDNN persistent RNN’s providing improved speed for smaller RNN’s.
  • Improved multi-GPU performance in both PyTorch c10d and Apex’s DDP.
  • Faster weight norm with improved mixed-precision accuracy used through torch.nn.utils.weight_norm.
  • Improved functionality of the torch.jit.script and torch.jit.tracepreview features including better support for pointwise operations in fusion.
  • Added support for a C++ only API (new PyTorch 1.0 preview feature).
  • Dataloader may still throw a benign error when stopping iterations early, however, it is no longer preventing the process from ending.
  • Latest version of DALI 0.4 Beta.
  • Latest version of NCCL 2.3.6.
  • Added support for OpenMPI 3.1.2
  • Ubuntu 16.04 with September 2018 updates

Tensor Core Examples

Known Issues

There are no new issues in this release.