ray

Convert Python workloads to Ray by decorating functions with @ray.remote, replacing sequential calls with .remote() to create futures, and using ray.get() to retrieve results. Ray automatically distributes tasks across your cluster, handling parallelization and scaling without rewriting core logic.

Use Ray's high-level libraries for specific workloads: Ray Data for batch processing and ETL, Ray Serve for online model serving endpoints, Ray Train for distributed training, and Ray Tune for hyperparameter optimization. They abstract complexity and provide optimized patterns; Ray Core is lower-level for custom distributed tasks.

Debug Ray apps using logging with ray.util.logging, the Ray Dashboard for real-time monitoring, and ray.get() with try-except blocks to catch task failures. Enable task profiling and tracing to diagnose performance bottlenecks and pinpoint where errors occur across your cluster.

Yes. Ray Data handles large-scale batch processing and ETL through distributed pipelines. Ray Serve deploys batch inference endpoints, and Ray Train supports distributed inference workloads. Ray automatically manages resource allocation and parallelization across your infrastructure.

Start with conservative defaults: set concurrency limits to match your cluster resources, choose batch_size based on memory constraints, and specify GPU usage explicitly to avoid overallocation. Ray provides safe, scalable configurations you can tune after profiling production workloads.

Analyze your code structure and dependencies first to identify parallelizable tasks and Ray patterns that fit. Most dependencies work unchanged; Ray runs your functions as-is. Only refactor if dependencies block distribution or require inter-task communication Ray doesn't support.