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.
The container also includes the following:
- Ubuntu 16.04 including Python 3.6 environment
- NVIDIA CUDA 10.0.130 including CUDA® Basic Linear Algebra Subroutines library™ (cuBLAS) 10.0.130
- NVIDIA CUDA® Deep Neural Network library™ (cuDNN) 7.4.0
- NCCL 2.3.6 (optimized for NVLink™ )
- OpenMPI 3.1.2
- TensorRT 5.0.0 RC
- DALI 0.4 Beta
- Tensor Core Optimized Examples:
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
- PyTorch container image version 18.10 is based on PyTorch v0.4.1+ with up-to-date features from the PyTorch v1.0 preview (main 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
- Improved functionality of the
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
- An implementation of ResNet50. The ResNet50 v1.5 model is a modified version of the original ResNet50 v1 model.
- An implementation of GNMT v2. The GNMT v2 model is similar to the one discussed in the Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation paper.
There are no new issues in this release.