Release 18.12

The container image for NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, release 18.12, is available.

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 18.12 is based on CUDA 10, which requires NVIDIA Driver release 410.xx. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384. For more information, see CUDA Compatibility and Upgrades.

GPU Requirements

Release 18.12 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.
  • NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet container image version 18.12 is based on 1.3.0, with all upstream changes from the Apache MXNet main branch up to and including PR 13069.
  • Improved handling of float32 datatype in examples/image-classification/train_imagenet_runner.
  • Enabled NVIDIA Tools Extension SDK (NVTX) instrumentation.
  • Improved speed of metrics computation during training, especially in the case of using TopKAccuracy metric.
  • Latest version of DALI 0.5.0 Beta.
  • Ubuntu 16.04 with November 2018 updates

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 on
  • 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.