Release 22.03

This NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, container release is intended for use on the NVIDIA Ampere Architecture A100 GPU and on previous generation GPUs like V100 and T4, and with the latest NVIDIA CUDA® 11 and NVIDIA 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 1.9.0.rc6. It is prebuilt and installed to the Python path.

Driver Requirements

Release 22.03 is based on NVIDIA CUDA 11.6.1, which requires NVIDIA Driver release 470 or later. However, if you are running on Data Center GPUs (formerly Tesla), for example, T4, 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 specific drivers. For a complete list of supported drivers, see CUDA Application Compatibility. For more information, see CUDA Compatibility and Upgrades and NVIDIA CUDA and Drivers Support.

GPU Requirements

Release 22.03 supports CUDA compute capability 6.0 and later. This corresponds to GPUs in the NVIDIA Pascal, NVIDIA Volta™, NVIDIA Turing™, and NVIDIA 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 22.03 is based on 1.9.0rc6.

    Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF).

Announcements

  • Deep learning framework containers 19.11 and later include experimental support for Singularity v3.0.
  • The TensorCore example models are no longer provided in the core container (previously shipped in /workspace/nvidia-examples).

    The models can instead be obtained from Github or the NGC. Some python packages, included in previous containers to support these example models, have also been removed. Depending on their specific use cases, you might need to add some packages that were previously preinstalled.

Tensor Core Examples

The tensor core examples in GitHub and NGC focus on achieving the best performance and convergence from 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 NVIDIA Turing, so 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

Training deep learning networks is a 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 and software optimizations make training these new models a feasible task.

Most of the hardware and software training optimization opportunities involve exploiting lower precision, such as FP16, to use the Tensor Cores that are 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.

Automatic Mixed Precision (AMP) automatically applies the FP16 training guidelines by using FP16 precision where it provides the most benefit, while conservatively keeping in full FP32 precision operations unsafe to do in FP16.

In the container, the NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet AMP tutorial is located in the /opt/mxnet/nvidia-examples/AMP/AMP_tutorial.md directory, shows you how to get started with mixed precision training using AMP for Apache MXNet, by using the SSD network example from GluonCV.

For more information about AMP, see the Training With Mixed Precision Guide.

Known Issues

  • Segmentation faults are possible with low probability at the beginning of model training on A100.

    This issue will be fixed in a future release. To eliminate this issue, users are advised to set the MXNET_CUDNN_HEUR_MODE=0 environment variable before launching their training script.

  • During model training, the ‘nvJPEG error (10): <unknown error>’ warning message might be displayed.

    This message refers to an issue about parsing image metadata and can be safely ignored.