ray

What problem does it solve?

This Skill helps software engineers translate Python workloads into scalable Ray-based workflows, simplifying distributed processing, debugging, and performance tuning.

Core Features & Use Cases

• Code conversion guidance: Convert sequential Python tasks into Ray Data pipelines, Ray Serve endpoints, or Ray Train workloads.
• Debugging guidance: Diagnose distributed Ray apps with best practices, logging, tracing, and error handling.
• Performance optimization: Suggest patterns to improve throughput and resource utilization, including when to prefer high-level Ray libraries over Ray Core.

Quick Start

Install Ray with: pip install ray Initialize Ray in your Python script: import ray; ray.init() Convert a Python function to a Ray task: @ray.remote def work(x): return x*x futures = [work.remote(i) for i in range(4)] print(ray.get(futures))