The container image of NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, release 18.09, 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 including CUDA® Basic Linear Algebra Subroutines library™ (cuBLAS) 10.0
- NVIDIA CUDA® Deep Neural Network library™ (cuDNN) 7.3.0
- NCCL 2.3.4 (optimized for NVLink™ )
- ONNX exporter 0.1 for CNN classification models
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)
- TensorRT 5.0.0 RC
- DALI 0.2 Beta
Release 18.09 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
- NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet container image version 18.09 is based on 1.3.0, with all upstream changes from the Apache MXNet main branch up to the creation point of the v1.3.x branch (PR 12301), plus all substantive cherry-picks from main that were included in the v1.3.0 release.
- The demonstrator of mixed precision ResNet-50 training using the
NHWCdata layout has been expanded to work now on the Turing architecture in addition to Volta.
- Latest version of cuDNN 7.3.0.
- Latest version of CUDA 10.0 which includes support for DGX-2, Turing, and Jetson Xavier.
- Latest version of cuBLAS 10.0.
- Latest version of NCCL 2.3.4.
- Latest version of TensorRT 5.0.0 RC.
- Latest version of DALI 0.2 Beta.
- Ubuntu 16.04 with August 2018 updates
The multi-threaded nature of Apache MXNet model execution may result in a variable maximum usage of GPU global memory, as discussed in earlier release notes. 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 our next release.