Ray Train: Production-ready distributed deep learning

Ray Train is a library built on top of the Ray ecosystem that simplifies distributed deep learning. Currently in stable beta in Ray 1.9, Ray Train offers the following features:

• Scales to multi-GPU and multi-node training with zero code changes

• Runs seamlessly on any cloud (AWS, GCP, Azure, Kubernetes, or on-prem)

• Supports PyTorch, TensorFlow, and Horovod

• Distributed data shuffling and loading with Ray Datasets

• Distributed hyperparameter tuning with Ray Tune

• Built-in loggers for TensorBoard and MLflow

In this webinar, we'll talk through some of the challenges in large-scale computer vision ML training, and show a demo of Ray Train in action.

LinkResources

• Slides >>

• Webinar Q&A >>

• GitHub: Ray Train demos >>

• Ray Train Docs >>

• How Ray Train parallelizes data and distributes computation >>