# Ray Data

> 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.

- **Type:** Skill
- **Install:** `agentstack add skill-orchestra-research-ai-research-skills-ray-data`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Orchestra-Research](https://agentstack.voostack.com/s/orchestra-research)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Orchestra-Research](https://github.com/Orchestra-Research)
- **Source:** https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/05-data-processing/ray-data
- **Website:** http://orchestra-research.com

## Install

```sh
agentstack add skill-orchestra-research-ai-research-skills-ray-data
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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
- **Multi-modal**: Images, Parquet, CSV, JSON, audio, video

**Use alternatives instead**:
- **Pandas**: Small data ( 100)
```

### Group by and aggregate

```python
# Group by column
ds = ds.groupby("category").count()

# Custom aggregation
ds = ds.groupby("category").map_groups(lambda group: {"sum": group["value"].sum()})
```

## GPU-accelerated transforms

```python
# Use GPU for preprocessing
def preprocess_images_gpu(batch):
    import torch
    images = torch.tensor(batch["image"]).cuda()
    # GPU preprocessing
    processed = images * 255
    return {"processed": processed.cpu().numpy()}

ds = ds.map_batches(
    preprocess_images_gpu,
    batch_size=64,
    num_gpus=1  # Request GPU
)
```

## Writing data

```python
# Write to Parquet
ds.write_parquet("s3://bucket/output/")

# Write to CSV
ds.write_csv("output/")

# Write to JSON
ds.write_json("output/")
```

## Performance optimization

### Repartition

```python
# Control parallelism
ds = ds.repartition(100)  # 100 blocks for 100-core cluster
```

### Batch size tuning

```python
# Larger batches = faster vectorized ops
ds.map_batches(process_fn, batch_size=10000)  # vs batch_size=100
```

### Streaming execution

```python
# Process data larger than memory
ds = ray.data.read_parquet("s3://huge-dataset/")
for batch in ds.iter_batches(batch_size=1000):
    process(batch)  # Streamed, not loaded to memory
```

## Common patterns

### Batch inference

```python
import ray

# Load model
def load_model():
    # Load once per worker
    return MyModel()

# Inference function
class BatchInference:
    def __init__(self):
        self.model = load_model()

    def __call__(self, batch):
        predictions = self.model(batch["input"])
        return {"prediction": predictions}

# Run distributed inference
ds = ray.data.read_parquet("s3://data/")
predictions = ds.map_batches(BatchInference, batch_size=32, num_gpus=1)
predictions.write_parquet("s3://output/")
```

### Data preprocessing pipeline

```python
# Multi-step pipeline
ds = (
    ray.data.read_parquet("s3://raw/")
    .map_batches(clean_data)
    .map_batches(tokenize)
    .map_batches(augment)
    .write_parquet("s3://processed/")
)
```

## Integration with ML frameworks

### PyTorch

```python
# Convert to PyTorch
torch_ds = ds.to_torch(label_column="label", batch_size=32)

for batch in torch_ds:
    # batch is dict with tensors
    inputs, labels = batch["features"], batch["label"]
```

### TensorFlow

```python
# Convert to TensorFlow
tf_ds = ds.to_tf(feature_columns=["image"], label_column="label", batch_size=32)

for features, labels in tf_ds:
    # Train model
    pass
```

## Supported data formats

| Format | Read | Write | Use Case |
|--------|------|-------|----------|
| Parquet | ✅ | ✅ | ML data (recommended) |
| CSV | ✅ | ✅ | Tabular data |
| JSON | ✅ | ✅ | Semi-structured |
| Images | ✅ | ❌ | Computer vision |
| NumPy | ✅ | ✅ | Arrays |
| Pandas | ✅ | ❌ | DataFrames |

## Performance benchmarks

**Scaling** (processing 100GB data):
- 1 node (16 cores): ~30 minutes
- 4 nodes (64 cores): ~8 minutes
- 16 nodes (256 cores): ~2 minutes

**GPU acceleration** (image preprocessing):
- CPU only: 1,000 images/sec
- 1 GPU: 5,000 images/sec
- 4 GPUs: 18,000 images/sec

## Use cases

**Production deployments**:
- **Pinterest**: Last-mile data processing for model training
- **ByteDance**: Scaling offline inference with multi-modal LLMs
- **Spotify**: ML platform for batch inference

## References

- **[Transformations Guide](references/transformations.md)** - Map, filter, groupby operations
- **[Integration Guide](references/integration.md)** - Ray Train, PyTorch, TensorFlow

## Resources

- **Docs**: https://docs.ray.io/en/latest/data/data.html
- **GitHub**: https://github.com/ray-project/ray ⭐ 36,000+
- **Version**: Ray 2.40.0+
- **Examples**: https://docs.ray.io/en/latest/data/examples/overview.html

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Orchestra-Research](https://github.com/Orchestra-Research)
- **Source:** [Orchestra-Research/AI-Research-SKILLs](https://github.com/Orchestra-Research/AI-Research-SKILLs)
- **License:** MIT
- **Homepage:** http://orchestra-research.com

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-orchestra-research-ai-research-skills-ray-data
- Seller: https://agentstack.voostack.com/s/orchestra-research
- Browse the marketplace: https://agentstack.voostack.com/browse

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