Fix RayServeException

✅ Solution

RayServeException usually arises from configuration mismatches between your deployment definition and the cluster's actual state, often stemming from incorrect resource requests, missing libraries, or incompatible Ray versions. Resolve this by carefully inspecting your deployment's `@serve.deployment` decorator parameters (num_replicas, ray_options) against available cluster resources, ensuring all dependencies are installed on all nodes, and aligning Ray versions across your client and cluster. Validate resource availability using `ray status` before deploying.