PyTorch Release 18.12

The NVIDIA container image for PyTorch, release 18.12, 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 18.12 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.

GPU Requirements

Release 18.12 supports CUDA compute capability 6.0 and higher. This corresponds to GPUs in the Pascal, Volta, and Turing families. Specifically, for a list of GPUs that this compute capability corresponds to, see CUDA GPUs. For additional support details, see Deep Learning Frameworks Support Matrix.

Key Features and Enhancements

This PyTorch release includes the following key features and enhancements.
  • PyTorch container image version 18.12 is based on PyTorch v0.4.1+ with up-to-date features from the PyTorch v1.0 preview (master branch up to PR 12303). PyTorch 0.4.1+ is released and included with this container.
  • Performance improvement for PyTorch’s native batch normalization.
  • Mixed precision SoftMax enabling FP16 inputs, FP32 computations and FP32 outputs.
  • Latest version of DALI 0.5.0 Beta.
  • Ubuntu 16.04 with November 2018 updates

Tensor Core Examples

Known Issues

Persistent batch normalization kernels have been disabled due to a known bug during validation. Batch normalization provides correct results and work as expected from users, however, this may cause up to 10% regression in time to solution performance on networks using batch normalization.