Ray

Use it when

• scale Python tasks, training, tuning, or batch inference across machines
• parallelize a workload that outgrows one process or GPU

What it solves

Not the fit when

• cluster operations can outweigh benefits for small jobs

Install

pip install -U ray

Invoke

Annotate tasks or actors with `@ray.remote`, initialize the runtime, and use Ray Data Train or Tune when their lifecycle matches the workload.

Alternatives

No reviewed alternatives recorded yet.