Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/data-processing-ray-data
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or
📊 Stats
Stars29,852
Forks3,189
LanguagePython
LicenseMIT
📦 Ships with claude-code-templates
</> SKILL.md
data-processing-ray-data.SKILL.md
---name: ray-data
description: Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Data Processing, Ray Data, Distributed Computing, ML Pipelines, Batch Inference, ETL, Scalable, Ray, PyTorch, TensorFlow]
dependencies: [ray[data], pyarrow, pandas]
---# Ray Data - Scalable ML Data Processing
Distributed data processing library for ML and AI workloads.
## When to use Ray Data
**Use Ray Data when:**
- Processing large datasets (>100GB) for ML training
- Need distributed data preprocessing across cluster
- Building batch inference pipelines
- Loading multi-modal data (images, audio, video)
- Scaling data processing from laptop to cluster
**Key features**:
- **Streaming execution**: Process data larger than memory
- **GPU support**: Accelerate transforms with GPUs
- **Framework integration**: PyTorch, TensorFlow, HuggingFace
