Release 18.10 - NVIDIA Docs

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Release 18.10 (PDF)

The container image of NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, release 18.10, 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:

Ubuntu 16.04 including Python 3.5
NVIDIA CUDA 10.0.130 including CUDA^® Basic Linear Algebra Subroutines library™ (cuBLAS) 10.0.130
NVIDIA CUDA^® Deep Neural Network library™ (cuDNN) 7.4.0
NCCL 2.3.6 (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 1.18.28 (for machine translation)
OpenMPI 3.1.2
TensorRT 5.0.0 RC
DALI 0.4 Beta

Driver Requirements

Release 18.10 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.

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 18.10 is based on 1.3.0, with all upstream changes from the Apache MXNet main branch up to and including PR 12537.
Latest version of NCCL 2.3.6.
Latest version of DALI 0.4 Beta.
Added support for OpenMPI 3.1.2
The known issue in the prior release regarding the variable maximum GPU global memory usage has been fixed. You should now see lower and stable global memory usage from run to run, and across GPUs in multi-GPU training.
Ubuntu 16.04 with September 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.
Apache MXNet ResNet50 regresses in FP32 performance. This issue should be fixed in a later release.