DIGITS Release 19.05
The NVIDIA application of DIGITS, release 19.05, is available on NGC.
Contents of the DIGITS container
This application contains the complete source of the version of DIGITS in
/opt/digits. It is pre-built and installed into the
/usr/local/python/ directory in the application.
Two DIGITS containers are available for this release based on the deep learning frameworks. Tag
19.05-tensorflow includes the TensorFlow framework only. Tag
19.05-caffe includes the Caffe only.
The container also includes the following:
- Ubuntu 16.04 including Python 2.7
- NVIDIA CUDA 10.1 Update 1 including cuBLAS 10.1 Update 1
- NVIDIA cuDNN 7.6.0
- NVIDIA NCCL 2.4.6 (optimized for NVLink™ )
- OpenMPI 3.1.3
- NVCaffe 0.17.3
- TensorFlow 1.13.1
- TensorRT 5.1.5
Release 19.05 is based on CUDA 10.1 Update 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 19.05 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 DIGITS container image version includes the following key features and enhancements.
- NVIDIA DIGITS application version 19.05 is based on DIGITS version 6.1.1
- Latest version of NVIDIA CUDA 10.1 Update 1 including cuBLAS 10.1 Update 1
- Latest version of NVIDIA cuDNN 7.6.0
- Latest version of TensorRT 5.1.5
- Ubuntu 16.04 with April 2019 updates
DIGITS is not designed to be run as an exposed external web service.
- The DIGITS container with
19.05-tensorflowtag may not run training tasks properly in vGPU environment when vGPU memory is 2GB or smaller.
- When creating datasets with S3 feature (Use S3), using the S3 zone other than the default may not work properly.