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:
- 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) 7.4.1
- NCCL 2.3.7 (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)
- OpenMPI 3.1.2
- Horovod 0.15.1
- TensorRT 5.0.2
- DALI 0.5.0 Beta
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.
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
- 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
- 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=1will experience issues, which will be resolved in a subsequent release. Issue tracked on https://github.com/apache/incubator-mxnet/issue/13341.
- The default setting of the environment variable
MXNET_GPU_COPY_NTHREADS=1in 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.