Release 20.03

The container image for NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, release 20.03, 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:

• Ubuntu 18.04 including Python 3.6
• NVIDIA CUDA 10.2.89 including cuBLAS 10.2.2.89
• NVIDIA cuDNN 7.6.5
• NVIDIA NCCL 2.6.3 (optimized for NVLink™ )
• ONNX exporter 0.1 for CNN classification models
  Note:

  The ONNX exporter is being continuously improved. You can try the latest changes by pulling from the main branch.

• Amazon Labs Sockeye sequence-to-sequence framework 1.18.99 (for machine translation)
• MLNX_OFED
• OpenMPI 3.1.4
• Horovod 0.19.0
• Nsight Compute 2019.5.0
• Nsight Systems 2020.1.1
• GluonCV Toolkit 0.5
• GluonNLP Toolkit 0.8.1
• TensorRT 7.0.0
• DALI 0.19.0
• Tensor Core optimized example:
  • ResNet-50 v1.5
• Jupyter and JupyterLab:
  • Jupyter Client 6.0.0
  • Jupyter Core 4.6.1
  • Jupyter Notebook 6.0.3
  • JupyterLab 1.2.6
  • JupyterLab Server 1.0.6
  • Jupyter-TensorBoard

Driver Requirements

Release 20.03 is based on NVIDIA CUDA 10.2.89, which requires NVIDIA Driver release 440.33.01. However, if you are running on Tesla (for example, T4 or any other Tesla board), you may use NVIDIA driver release 396, 384.111+, 410, 418.xx or 440.30. 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 20.03 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 NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet release includes the following key features and enhancements.

• NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet container image version 20.03 is based on Apache MXNet 1.6.0
• Increased performance of BatchNorm operator when the bn_group parameter is set to more than 1 and enabled grouping for 8 and 16 GPUs in a single node
• Increased performance of the ArgSort operator
• Exposed cudnn control parameters such as cudnn_algo_fwd, previously only available in mx.sym.Convolution to Gluon’s gluon.nn.Conv[1D,2D,3D]
• Increased performance of the elementwise operations such as elementwise_add, most notably for FP16 precision
• The latest version of DALI 0.19.0
• Ubuntu 18.04 with February 2020 updates

Announcements

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

NVIDIA MXNet 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.

• The ResNet50 v1.5 model is a slightly modified version of the original ResNet50 v1 model that trains to a greater accuracy. This model script is available on GitHub as well as NVIDIA GPU Cloud (NGC).

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 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.

• There is a known issue in the BERT QA demo, where for some values of sequence length the inference may fail with CUDA Driver: operation failed due to a previous error during captureerror. To test those values of sequence length, change the line export MXNET_EXPERIMENTAL_ENABLE_CUDA_GRAPH=1 to export MXNET_EXPERIMENTAL_ENABLE_CUDA_GRAPH=0 in the test_bert_inference script inside the demo directory.