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Ray production readiness
Apply operational gates to research and application workloads.
Ray production readiness
A production Ray application is more than a working notebook. It needs resource intent, dependency control, observability, failure handling, and operational ownership.
Readiness dimensions
| Area | Question |
|---|---|
| Resources | Are CPU, GPU, and memory needs explicit? |
| Data | Can workers read inputs directly? |
| Failure | What retries or checkpoints exist? |
| Observability | Which metrics indicate progress and saturation? |
| Release | Can the team roll back code and model versions? |
Resource annotations
@ray.remote(num_cpus=4, num_gpus=1)
def gpu_transform(batch):
return run_model(batch)Operating principle
Make the cluster behavior legible. If a task needs a GPU, say so. If a pipeline depends on object storage throughput, measure it. If a Serve deployment owns user traffic, define health and rollback expectations.