PyTorch Release 19.01

The NVIDIA container image for PyTorch, release 19.01, 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) 7.4.2
• NCCL 2.3.7 (optimized for NVLink™ )
• OpenMPI 3.1.3
• Caffe2
• TensorRT 5.0.2
• DALI 0.6 Beta
• Tensor Core optimized examples:
  • Neural Collaborative Filtering (NCF)
  • Transformer
  • ResNet50 v1.5
  • GNMT v2

Driver Requirements

Release 19.01 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 19.01 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 19.01 is based on PyTorch v1.0.0 with up-to-date features.
• Latest version of DALI 0.6 Beta
• Latest version of NVIDIA cuDNN 7.4.2
• Latest version of OpenMPI 3.1.3
• Added the Neural Collaborative Filtering (NCF) and TransformerTensor Core examples.
• Ubuntu 16.04 with December 2018 updates

Tensor Core Examples

These examples focus on achieving the best performance and convergence from NVIDIA Volta Tensor Cores by using the latest deep learning example networks for training. This container includes the following Tensor Core examples.

• An implementation of the Neural Collaborative Filtering (NCF) model. The NCF model focuses on providing recommendations, also known as collaborative filtering; with implicit feedback. The training data for this model should contain binary information about whether a user interacted with a specific item. NCF was first described by Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua in the Neural Collaborative Filtering paper.
• An implementation of the Transformer model architecture. The Transformer model is based on the optimized implementation in Facebook's Fairseq NLP Toolkit and is built on top of PyTorch. The original version in the Fairseq project was developed using Tensor Cores, which provides significant training speedup. Our implementation improves the performance and is tested on a DGX-1V 16GB.
• An implementation of the ResNet50 model. The ResNet50 v1.5 model is a modified version of the original ResNet50 v1 model.
• An implementation of the GNMT v2 model. 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.

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.
• If using or upgrading to a 3-part-version driver, for example, a driver that takes the format of xxx.yy.zz, you will receive a Failed to detect NVIDIA driver version. message. This is due to a known bug in the entry point script's parsing of 3-part driver versions. This message is non-fatal and can be ignored. This will be fixed in the 19.04 release.