Scale distributed data processing for ML workloads with streaming and batch pipelines.
npx skills add https://github.com/Tnemo65/template --skill ray-data-tnemo65
Please help me install this Agent Skill. Skill: ray-data Source: https://github.com/Tnemo65/template/tree/main/.cursor/skills/03-data-processing/ray-data Command: npx skills add https://github.com/Tnemo65/template --skill ray-data-tnemo65
Scalable, distributed data processing for ML workloads, enabling both streaming and batch pipelines across single machines to large clusters.
Run a small Ray Data pipeline to read Parquet data, apply a map_batch transformation, and write the results back to Parquet.
High-intent search queries and answers about installing and using this skill.
You can scale distributed data processing for ML pipelines across single machines to large clusters using Ray Data, which supports both streaming and batch execution for CPU and GPU workloads.
Yes, Ray Data supports flexible data I/O for Parquet, CSV, JSON, and images, allowing you to read Parquet data, apply map_batch transformations, and write results back for large-scale preprocessing.
Ray Data integrates directly with Ray Train, PyTorch, and TensorFlow to streamline workflows, enabling efficient multi-modal data loading and reproducibility across ML experiments.
Processing streaming and batch data for ML workloads is handled by Ray Data, satisfying requirements for streaming execution, GPU acceleration, and framework integration across distributed environments.
No, you do not need a large cluster; Ray Data enables scalable data processing and feature engineering across single machines to large clusters, allowing you to start small and scale when needed.
Use Ray Data when you need to scale ML data pipelines with streaming execution, GPU acceleration, and native integration with Ray Train, PyTorch, or TensorFlow across distributed clusters.
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