Ray Data Agent 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 distributed ETL pipelines.

3k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

1480

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/OpenRaiser/NanoResearch --skill ray-data

The instruction itself

31 sections, as written by the author

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 (<1GB) on single machine
Dask: Tabular data, SQL-like operations
Spark: Enterprise ETL, SQL queries

Quick start

Installation

pip install -U 'ray[data]'

Load and transform data

import ray

# Read Parquet files
ds = ray.data.read_parquet("s3://bucket/data/*.parquet")

# Transform data (lazy execution)
ds = ds.map_batches(lambda batch: {"processed": batch["text"].str.lower()})

# Consume data
for batch in ds.iter_batches(batch_size=100):
    print(batch)

Integration with Ray Train

import ray
from ray.train import ScalingConfig
from ray.train.torch import TorchTrainer

# Create dataset
train_ds = ray.data.read_parquet("s3://bucket/train/*.parquet")

def train_func(config):
    # Access dataset in training
    train_ds = ray.train.get_dataset_shard("train")

    for epoch in range(10):
        for batch in train_ds.iter_batches(batch_size=32):
            # Train on batch
            pass

# Train with Ray
trainer = TorchTrainer(
    train_func,
    datasets={"train": train_ds},
    scaling_config=ScalingConfig(num_workers=4, use_gpu=True)
)
trainer.fit()

Reading data

From cloud storage

import ray

# Parquet (recommended for ML)
ds = ray.data.read_parquet("s3://bucket/data/*.parquet")

# CSV
ds = ray.data.read_csv("s3://bucket/data/*.csv")

# JSON
ds = ray.data.read_json("gs://bucket/data/*.json")

# Images
ds = ray.data.read_images("s3://bucket/images/")

From Python objects

# From list
ds = ray.data.from_items([{"id": i, "value": i * 2} for i in range(1000)])

# From range
ds = ray.data.range(1000000)  # Synthetic data

# From pandas
import pandas as pd
df = pd.DataFrame({"col1": [1, 2, 3], "col2": [4, 5, 6]})
ds = ray.data.from_pandas(df)

Transformations

Map batches (vectorized)

# Batch transformation (fast)
def process_batch(batch):
    batch["doubled"] = batch["value"] * 2
    return batch

ds = ds.map_batches(process_batch, batch_size=1000)

Row transformations

# Row-by-row (slower)
def process_row(row):
    row["squared"] = row["value"] ** 2
    return row

ds = ds.map(process_row)

Filter

# Filter rows
ds = ds.filter(lambda row: row["value"] > 100)

Group by and aggregate

# 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

# 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

# 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

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

Batch size tuning

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

Streaming execution

# 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

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

# 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

# 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

# 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

|--------|------|-------|----------|

| 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 - Map, filter, groupby operations
Integration Guide - 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

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How to use it

Copy the folder

Take openraiser/ray-data from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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Install what it needs

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