MXNet Release 19.02

The NVIDIA container image for MXNet, release 19.02, is available.

Contents of MXNet

This container image contains the complete source of the version of MXNet in /opt/mxnet. It is pre-built and installed to the Python path.

The container also includes the following:

Driver Requirements

Release 19.02 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 19.02 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 MXNet release includes the following key features and enhancements.
  • MXNet container image version 19.02 is based on 1.4.0.rc2.
  • Latest version of DALI 0.6.1 Beta
  • Added Jupyter and JupyterLab software in our packaged container.
  • Latest version of jupyter_client 5.2.4
  • Latest version of jupyter_core 4.4.0
  • Added an image classification example in Gluon.
  • Multiple enhancements to Gluon training speed with models hybridized with static_alloc=True setting.
  • Added Python bindings for NVTX and CUDA profiler in the mxnet.cuda_utils package.
  • Ubuntu 16.04 with January 2019 updates

Tensor Core Examples

These examples focus on achieving the best performance and convergence from NVIDIA Volta Tensor Cores by using the latest deep learning example networks for training. This container includes the following Tensor Core examples.

Known Issues

  • The 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.
  • If using or upgrading to a 3-part-version driver, for example, a driver that takes the format of xxx.yy.zz, you will receive a Failed to detect NVIDIA driver version. message. This is due to a known bug in the entry point script's parsing of 3-part driver versions. This message is non-fatal and can be ignored. This will be fixed in the 19.04 release.