# Release 19.05

Release 19.05 (PDF)

The container image for NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, release 19.05, is available on NGC.

## Contents of the Optimized Deep Learning Framework container

This container image contains the complete source of the version of NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet in /opt/mxnet. It is pre-built and installed to the Python path. The container also includes the following:

## Driver Requirements

Release 19.05 is based on CUDA 10.1 Update 1, which requires NVIDIA Driver release 418.xx. 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 19.05 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 Optimized Deep Learning Framework release includes the following key features and enhancements.

## NormalizedConvolution Operator

NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet 19.05 includes a new operator (NormalizedConvolution) to improve training speeds of CNN's like ResNet-50. The NormalizedConvolution operator combines the functions of BatchNorm and Convolution into one operator to reduce data transfers to and from GPU global memory. For more information regarding its use through the ResNet-50 sample model script, see ./example/image-classification/symbols/resnet-v1b-normconv-fl.py.

NormalizedConvolution is supported by new APIs of cuDNN v7.6, however, its Python API is experimental until it becomes incorporated into upstream Apache MXNet.

## 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)

Training deep learning networks is a very computationally intensive task. Novel model architectures tend to have increasing number of layers and parameters, which slows down training. Fortunately, new generations of training hardware as well as software optimizations, make it a feasible task.

However, where most of the (both hardware and software) optimization opportunities exists is in exploiting lower precision (like FP16) to, for example, utilize tensor cores available on new Volta and Turing GPUs. While training in FP16 showed great success in image classification tasks, other more complicated neural networks typically stayed in FP32 due to difficulties in applying the FP16 training guidelines.

That is where AMP (Automatic Mixed Precision) comes into play. It automatically applies the guidelines of FP16 training, using FP16 precision where it provides the most benefit, while conservatively keeping in full FP32 precision operations unsafe to do in FP16.

The NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet AMP tutorial, located in /opt/mxnet/nvidia-examples/AMP/AMP_tutorial.md inside this container, shows you how to get started with mixed precision training using AMP for Apache MXNet. As an example of a network we will use SSD network from GluonCV.

• The Apache MXNet KVStore GPU peer-to-peer communication tree discovery, as of release 18.09, is not compatible with DGX-1V. Only users that set the environment variable MXNET_KVSTORE_USETREE=1 will experience issues, which will be resolved in a subsequent release. Issue tracked under 13341.
• The default setting of the environment variable MXNET_GPU_COPY_NTHREADS=1 in the container may not be optimal for all networks. Networks with a high ratio of parameters and computation, like AlexNet, may achieve greater multi-GPU training speeds with the setting MXNET_GPU_COPY_NTHREADS=2. Users are encouraged to try this setting for their own use case.