#GPU Training

DIGITS is a deep learning GPU training system supporting Caffe, Torch, and TensorFlow frameworks. Its web interface simplifies model training, while a platform for feature requests encourages community-driven development. Comprehensive documentation and multi-platform installation guides enhance accessibility, with Docker containers for streamlined deployment. Offering tutorials and advanced examples, DIGITS accommodates both beginners and advanced users. Community engagement is facilitated through forums and user groups, promoting collaborative troubleshooting and feature enhancements.

Developed by NVIDIA and NERSC, Makani is a PyTorch-based library optimized for parallel training of ML-driven weather and climate models across multiple GPUs. By integrating advanced features like automatic mixed precision and spatial model parallelism, Makani boosts the efficiency of training large models, such as the FourCastNet with Spherical Fourier Neural Operators, while reducing memory overhead. It offers extensive configuration options and supports large dataset inference, positioning itself as a vital resource for contemporary weather and climate research.

Facilitate deep learning model training on Linux with TensorFlow 2 and CUDA 11. Benefit from TensorBoard monitoring and a built-in Inference REST API for model testing. Compatible with CPU and multi-GPU setups, it provides flexibility for various hardware. Use BMW-Labeltool-lite for data annotation and pre-trained network weights from the COCO dataset to enhance training efficiency. Setup requires Ubuntu 18.04, NVIDIA drivers, Docker CE, and NVIDIA Docker 2, all streamlined by automated scripts.