# PyTorch Release 19.08

PyTorch Release 19.08 (PDF)

The NVIDIA container image for PyTorch, release 19.08, is available on NGC.

## Contents of the PyTorch container

This container image contains the complete source of the version of PyTorch in /opt/pytorch. It is pre-built and installed in Conda default environment (/opt/conda/lib/python3.6/site-packages/torch/) in the container image. The container also includes the following:

## Driver Requirements

Release 19.08 is based on NVIDIA CUDA 10.1.243, which requires NVIDIA Driver release 418.87. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.

## GPU Requirements

Release 19.08 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.

## 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. Each example model trains with mixed precision Tensor Cores on Volta, therefore you can get results much faster than training without tensor cores. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. This container includes the following tensor core examples.

## Automatic Mixed Precision (AMP)

NVIDIA’s Automatic Mixed Precision (AMP) for PyTorch is available in this container through a preinstalled release of Apex. AMP enables users to try mixed precision training by adding only 3 lines of Python to an existing FP32 (default) script. Amp will choose an optimal set of operations to cast to FP16. FP16 operations require 2X reduced memory bandwidth (resulting in a 2X speedup for bandwidth-bound operations like most pointwise ops) and 2X reduced memory storage for intermediates (reducing the overall memory consumption of your model). Additionally, GEMMs and convolutions with FP16 inputs can run on Tensor Cores, which provide an 8X increase in computational throughput over FP32 arithmetic.

Comprehensive guidance and examples demonstrating AMP for PyTorch can be found in the documentation.

## Known Issues

• Performance of Mask R-CNN in FP32 precision is up to 20% slower in the 19.07 container compared to the previous release. For best performance on Mask R-CNN, it is recommended to use automatic mixed precision training. This can easily be done using the float16 option with the MaskRCNN example included in this container.
• Due to recent changes on batch norm multiplier initialization (PyTorch commit: c60465873c5cf8f1a36da39f7875224d4c48d7ca), all batch norm multiplier is initialized as constant 1, instead of uniformly distributed between 0 and 1, as it was previously. This has caused accuracy issue for our TACOTRON2 model. If similar accuracy regression is observed during an update from 19.06 to 19.08, we recommend to re-initialize the batch norm multiplier using uniformed distribution. This could be done by passing your model to the following function:
Copy
Copied!

def init_bn(module):
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
if module.affine:
module.weight.data.uniform_()
for child in module.children():
init_bn(child)