Release 21.03

The container image for NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, release 21.03, 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 recently released Apache MXNet version 1.8.0. It is pre-built and installed to the Python path.

Driver Requirements

Release 21.03 is based on NVIDIA CUDA 11.2.1, which requires NVIDIA Driver release 460.32.03 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). 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 21.03 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 21.03 is based on 1.8.0. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF).
  • An expanded functionality InstanceNorm operator that adds fp64 and fp16 data I/O capability to the existing fp32 support. Also an experimental InstanceNormV2 operator with additional parameters: axis (for NHWC support), act_type (for integrated relu) and xbuf_group/xbuf_ptr (for multi-GPU operation).
  • Improved the performance of the softmax, log_softmax and softmin operators, especially for cases with small or odd number of elements per row.
  • Improved the performance of the BatchNorm operator when used with fp16 and axis=-1 options.
  • Improved performance of multiple operators when operating on large input.
  • Latest version of NVIDIA CUDA 11.2.1 including cuBLAS 11.4.1.1026
  • Latest version of NVIDIA cuDNN 8.1.1
  • Latest version of Horovod 0.21.2
  • Latest version of DALI 0.31.0
  • Ubuntu 20.04 with February 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.

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.

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

Known Issues

  • 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.