Open-source framework for running Python work across many machines, from data processing to training to serving.
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About Ray
What it is
Ray is an open-source framework for distributed computing in Python. It runs training, data processing, hyperparameter search and model serving across a cluster of machines or accelerators, with libraries for each, and is maintained alongside Anyscale's managed version.
Why it's different
Its real contribution is that the distributed version of your code still looks like your code - you decorate functions rather than rewriting for a cluster framework - which is why so much AI infrastructure ends up built on it. That abstraction leaks under pressure: debugging a distributed failure is genuinely hard, and object store memory is the thing that bites first and least obviously. Running a cluster is an operational commitment, not a library import.
How people use it
Training that no longer fits on one machine. Batch inference over large datasets. Hyperparameter sweeps. Serving models behind an API. Get it working on a single machine first, because most Ray problems are ordinary Python problems that became difficult to see once they were distributed.
Written by the n3os team. We are not affiliated with Ray.
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