# Release 21.05

Release 21.05 (PDF)

This NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, container release is intended for use on the NVIDIA Ampere Architecture A100 GPU, as well as on previous generation GPUs like V100 and T4, and with the latest CUDA 11 and cuDNN 8 libraries. The container image is available on NGC.

## Contents of the Optimized Deep Learning Framework container

This container image contains the complete source of the NVIDIA Optimized Deep Learning Framework, which is based upon Apache MXNet version 1.8.0. It is pre-built and installed to the Python path. The container also includes the following:

## Driver Requirements

Release 21.05 is based on NVIDIA CUDA 11.3.0, which requires NVIDIA Driver release 465.19.01 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, you may use NVIDIA driver release 418.40 (or later R418), 440.33 (or later R440), 450.51 (or later R450), or 460.27 (or later R460). 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 and NVIDIA CUDA and Drivers Support.

## GPU Requirements

Release 21.05 supports CUDA compute capability 6.0 and higher. This corresponds to GPUs in the NVIDIA Pascal, Volta, Turing, and Ampere Architecture GPU 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.

• NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet container image version 21.05 is based on 1.8.0. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF).
• A new Gluon operator mxnet.gluon.nn.SpatialParallelConv3D was added that implements 3D convolution of features-last batchsize-1 inputs spatially distributed across multiple GPUs--each one of n GPUs holds an input of shape [1, D, H, W, C] and the convolution is performed as if it were done on the full [1, n*D, H, W, C] input tensor.
• A new Gluon operator mxnet.gluon.nn.SpatialParallelSplit was added that implements a spatial 1:n split of a tensor across multiple GPUs. For n GPUs, the output for an input of shape [1, n*D, H, W, C] is [1,D,H,W,C], where each GPU holds a different part of the input.
• A new Gluon operator mxnet.gluon.nn.SpatialParallelAllgather was added that implements a n:1 spatial allgather of a tensor from multiple GPUs. For n GPUs, the output for an input of shape [1, D, H, W, C] is [1, n*D, H, W, C], so that every GPU holds the full tensor.
• Ubuntu 20.04 with April 2021 updates

## Announcements

• Deep learning framework containers 19.11 and later include experimental support for Singularity v3.0.

## NVIDIA Optimized Deep Learning Framework Container Versions

The following table shows what versions of Ubuntu, CUDA, Apache MXNet, and TensorRT are supported in each of the NVIDIA containers for the Optimized Deep Learning Framework. For older container versions, refer to the Frameworks Support Matrix.

Container Version Ubuntu CUDA Toolkit Apache MXNet TensorRT
21.05 20.04 NVIDIA CUDA 11.3.0 1.8.0 TensorRT 7.2.3.4
21.04
21.03 NVIDIA CUDA 11.2.1 TensorRT 7.2.2.3
21.02 NVIDIA CUDA 11.2.0 1.8.0.rc2 7.2.2.3+cuda11.1.0.024
20.12 NVIDIA CUDA 11.1.1 1.8.0.rc0 TensorRT 7.2.2
20.11

18.04

NVIDIA CUDA 11.1.0 TensorRT 7.2.1
20.10 1.7.0
20.09 NVIDIA CUDA 11.0.3 TensorRT 7.1.3
20.08 1.6.0
20.07 NVIDIA CUDA 11.0.194
20.06 NVIDIA CUDA 11.0.167 TensorRT 7.1.2
20.03 NVIDIA CUDA 10.2.89 TensorRT 7.0.0
20.02 1.6.0.rc2

20.01

1.5.1 commit c98184806 from September 4, 2019

19.12

19.11

TensorRT 6.0.1
19.10 NVIDIA CUDA 10.1.243
19.09 1.5.0 commit 006486af3 from August 28, 2019
19.08 1.5.0 commit 75a9e187d from June 27, 2019 TensorRT 5.1.5

## Tensor Core Examples

The tensor core examples provided in GitHub and NVIDIA GPU Cloud (NGC) focus on achieving the best performance and convergence from NVIDIA Volta tensor cores by using the latest deep learning example networks and model scripts for training. Each example model trains with mixed precision Tensor Cores on Volta and Turing, 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 an increasing number of layers and parameters, which slows down training. Fortunately, new generations of training hardware as well as software optimizations make training these new models a feasible task.

Most of the hardware and software training optimization opportunities involve exploiting lower precision like FP16 in order to utilize the 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 are needed to ensure proper model training.

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 how to get started with mixed precision training using AMP for Apache MXNet, using by example the SSD network from GluonCV.

• 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.