The container image for NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet, release 19.03, is available on NGC.
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 10.1.105 including cuBLAS 10.1.105
- NVIDIA cuDNN 7.5.0
- NVIDIA NCCL 2.4.3 (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 1.18.61 (for machine translation)
- OpenMPI 3.1.3
- Horovod 0.13.11
- TensorRT 5.1.2
- DALI 0.7 Beta
- Tensor Core optimized example:
- Jupyter and JupyterLab:
Release 19.03 is based on CUDA 10.1, which requires NVIDIA Driver release 418.xx+. However, if you are running on Tesla (Tesla V100, Tesla P4, Tesla P40, or Tesla P100), you may use NVIDIA driver release 384.111+ or 410. The CUDA driver's compatibility package only supports particular drivers. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades.
Release 19.03 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
- NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet container image version 19.03 is based on Apache MXNet 1.4.0.
- Latest version of NVIDIA CUDA 10.1.105 including cuBLAS 10.1.105
- Latest version of NVIDIA cuDNN 7.5.0
- Latest version of NVIDIA NCCL 2.4.3
- Latest version of DALI 0.7 Beta
- Latest version of TensorRT 5.1.2
- Optimized NMS operator performance
- Ubuntu 16.04 with February 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. Each example model trains with mixed precision Tensor Cores on Volta, therefore you can get results much faster than training without tensor cores. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. This container includes the following tensor core examples.
- An implementation of the ResNet-50 model. The ResNet50 v1.5 model is a slightly modified version of the original ResNet50 v1 model that trains to a greater accuracy.
- 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 under 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.
- 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.