Distributed deep learning with Ray Train is now in Beta
Blog post from Anyscale
Ray Train is an easy-to-use library for distributed deep learning that aims to improve developer velocity, be production-ready, and come with built-in features. It simplifies the APIs of its ML ecosystem as it heads towards Ray 2.0. The library addresses the gap between prototyping and production model training by providing a framework that can bring the best of both worlds together - extremely fast iteration while making it really easy to scale on different cluster environments. Ray Train is designed for developer productivity, allowing developers to iterate quickly and easily integrate with third-party libraries. It provides features such as distributed data loading, hyperparameter tuning, built-in loggers, and support for PyTorch, TensorFlow, and Horovod. The library also offers a TrainingCallback interface that can be used to process intermediate results, making it easy to incorporate tools and utilities. Ray Train is open-source and flexible, allowing developers to leverage the open-source data ecosystem and integrate with various libraries and frameworks.
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