Handling files and packages on your cluster with Ray runtime environments

Ray's runtime environments feature provides a simple way to manage files and packages on a cluster, making it seamless to scale up to a cluster while allowing for rapid iterative development. A runtime environment is specified in Python and can be easily described with an example, which includes the working directory, pip packages, environment variables, and more. This allows users to update their runtime environment along with their code updates without having to restart their Ray cluster or rebuild any container image. The feature also supports caching of files and packages on the cluster for quick reuse, making it efficient for concurrent workloads with different package dependencies. Additionally, features are planned to improve the functionality of runtime environments, such as better support for Docker images and cross-language support.

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