PyTorch Release 18.02

The NVIDIA container image of PyTorch, release 18.02, is available.

PyTorch container image version 18.02 is based on PyTorch 0.3.0.

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 9.0.176 including:
  • CUDA^® Basic Linear Algebra Subroutines library™ (cuBLAS) 9.0.282 Patch 2 which is installed by default
  • cuBLAS 9.0.234 Patch 1 as a debian file. Installing Patch 1 by issuing the dpkg -i /opt/cuda-cublas-9-0_9.0.234-1_amd64.deb command is the workaround for the known issue described below.
• NVIDIA CUDA^® Deep Neural Network library™ (cuDNN) 7.0.5
• NCCL 2.1.2 (optimized for NVLink™ )

Driver Requirements

Release 18.02 is based on CUDA 9, which requires NVIDIA Driver release 384.xx.

Key Features and Enhancements

This PyTorch release includes the following key features and enhancements.

• Improved multi-GPU performance on image networks shown in /opt/pytorch/examples/imagenet. You can run this example for multi-GPU by issuing the python -m multiproc main.py command.
• Latest version of cuBLAS
• Ubuntu 16.04 with January 2018 updates

Known Issues

cuBLAS 9.0.282 regresses RNN seq2seq FP16 performance for a small subset of input sizes. This issue should be fixed in the next update. As a workaround, install cuBLAS 9.0.234 Patch 1 by issuing the dpkg -i /opt/cuda-cublas-9-0_9.0.234-1_amd64.deb command.