Release 18.07

The container image of NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, release 18.07, 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
  Note:

  Container image 18.07-py2 contains Python 2.7; 18.07-py3 contains Python 3.5.

• NVIDIA CUDA 9.0.176 (see Errata section and 2.1) including CUDA^® Basic Linear Algebra Subroutines library™ (cuBLAS) 9.0.425
• NVIDIA CUDA^® Deep Neural Network library™ (cuDNN) 7.1.4
• NCCL 2.2.13 (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 1.18.23 (for machine translation)
• TensorRT 4.0.1
• DALI 0.1 Beta

Driver Requirements

Release 18.07 is based on CUDA 9, which requires NVIDIA Driver release 384.xx.

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 18.07 is based on 1.2.0, with all upstream changes from the Apache MXNet main branch up to and including PR 11302.
• Added support for DALI 0.1 Beta.
• Latest version of CUDA^® Basic Linear Algebra Subroutines library™ (cuBLAS) 9.0.425.
• Ubuntu 16.04 with June 2018 updates

Announcements

Starting with the next major version of the CUDA release, we will no longer provide updated Python 2 containers and will only update Python 3 containers.

Known Issues

• Some of the unit tests available in /opt/mxnet/tests/python/{gpu,unittest}/*.py require the SciPy Python library. For those that want to run the unit tests, first install SciPy by pip install scipy. There is no longer a need to specifically request the 1.0 version of SciPy.
• The multi-threaded nature of Apache MXNet model execution may result in a variable maximum usage of GPU global memory. Users that experience sporadic out-of-GPU-memory errors should experiment with setting the environment variable MXNET_GPU_WORKER_NTHREADS=1 as a possible remedy. We anticipate the need for this experimentation will be removed in a subsequent release.