atdata 0.2.2b1

A loose federation of distributed, typed datasets

atdata

A loose federation of distributed, typed datasets built on WebDataset.

atdata provides a type-safe, composable framework for working with large-scale datasets. It combines the efficiency of WebDataset's tar-based storage with Python's type system and functional programming patterns.

Features

• Typed Samples - Define dataset schemas using Python dataclasses with automatic msgpack serialization
• Schema-free Exploration - Load datasets without defining a schema first using DictSample
• Lens Transformations - Bidirectional, composable transformations between different dataset views
• Automatic Batching - Smart batch aggregation with numpy array stacking
• WebDataset Integration - Efficient storage and streaming for large-scale datasets
• Flexible Data Sources - Stream from local files, HTTP URLs, or S3-compatible storage
• HuggingFace-style API - load_dataset() with path resolution and split handling
• Local & Atmosphere Storage - Index datasets locally with Redis or publish to ATProto network

Installation

pip install atdata

Requires Python 3.12 or later.

Quick Start

Loading Datasets

The primary way to load datasets is with load_dataset():

from atdata import load_dataset

# Load without specifying a type - returns Dataset[DictSample]
ds = load_dataset("path/to/data.tar", split="train")

# Explore the data
for sample in ds.ordered():
    print(sample.keys())      # See available fields
    print(sample["text"])     # Dict-style access
    print(sample.label)       # Attribute access
    break

Defining Typed Schemas

Once you understand your data, define a typed schema with @packable:

import atdata
from numpy.typing import NDArray

@atdata.packable
class ImageSample:
    image: NDArray
    label: str
    metadata: dict

Loading with Types

# Load with explicit type
ds = load_dataset("path/to/data-{000000..000009}.tar", ImageSample, split="train")

# Or convert from DictSample
ds = load_dataset("path/to/data.tar", split="train").as_type(ImageSample)

# Iterate over samples
for sample in ds.ordered():
    print(f"Label: {sample.label}, Image shape: {sample.image.shape}")

# Iterate with shuffling and batching
for batch in ds.shuffled(batch_size=32):
    # batch.image is automatically stacked into shape (32, ...)
    # batch.label is a list of 32 labels
    process_batch(batch.image, batch.label)

Lens Transformations

Define reusable transformations between sample types:

@atdata.packable
class ProcessedSample:
    features: NDArray
    label: str

@atdata.lens
def preprocess(sample: ImageSample) -> ProcessedSample:
    features = extract_features(sample.image)
    return ProcessedSample(features=features, label=sample.label)

# Apply lens to view dataset as ProcessedSample
processed_ds = dataset.as_type(ProcessedSample)

for sample in processed_ds.ordered(batch_size=None):
    # sample is now a ProcessedSample
    print(sample.features.shape)

Core Concepts

DictSample

The default sample type for schema-free exploration. Provides both attribute and dict-style access:

ds = load_dataset("data.tar", split="train")

for sample in ds.ordered():
    # Dict-style access
    print(sample["field_name"])

    # Attribute access
    print(sample.field_name)

    # Introspection
    print(sample.keys())
    print(sample.to_dict())

PackableSample

Base class for typed, serializable samples. Fields annotated as NDArray are automatically handled:

@atdata.packable
class MySample:
    array_field: NDArray      # Automatically serialized
    optional_array: NDArray | None
    regular_field: str

Every @packable class automatically registers a lens from DictSample, enabling seamless conversion via .as_type().

Lens

Bidirectional transformations with getter/putter semantics:

@atdata.lens
def my_lens(source: SourceType) -> ViewType:
    # Transform source -> view
    return ViewType(...)

@my_lens.putter
def my_lens_put(view: ViewType, source: SourceType) -> SourceType:
    # Transform view -> source
    return SourceType(...)

Data Sources

Datasets support multiple backends via the DataSource protocol:

# String URLs (most common) - automatically wrapped in URLSource
dataset = atdata.Dataset[ImageSample]("data-{000000..000009}.tar")

# S3 with authentication (private buckets, Cloudflare R2, MinIO)
source = atdata.S3Source(
    bucket="my-bucket",
    keys=["data-000000.tar", "data-000001.tar"],
    endpoint="https://my-account.r2.cloudflarestorage.com",
    access_key="...",
    secret_key="...",
)
dataset = atdata.Dataset[ImageSample](source)

Dataset URLs

Uses WebDataset brace expansion for sharded datasets:

• Single file: "data/dataset-000000.tar"
• Multiple shards: "data/dataset-{000000..000099}.tar"
• Multiple patterns: "data/{train,val}/dataset-{000000..000009}.tar"

HuggingFace-style API

Load datasets with a familiar interface:

from atdata import load_dataset

# Load without type for exploration (returns Dataset[DictSample])
ds = load_dataset("./data/train-*.tar", split="train")

# Load with explicit type
ds = load_dataset("./data/train-*.tar", ImageSample, split="train")

# Load from S3 with brace notation
ds = load_dataset("s3://bucket/data-{000000..000099}.tar", ImageSample, split="train")

# Load all splits (returns DatasetDict)
ds_dict = load_dataset("./data", ImageSample)
train_ds = ds_dict["train"]
test_ds = ds_dict["test"]

# Convert DictSample to typed schema
ds = load_dataset("./data/train.tar", split="train").as_type(ImageSample)

Development

Setup

# Install uv if not already available
python -m pip install uv

# Install dependencies
uv sync

Testing

# Run all tests with coverage
uv run pytest

# Run specific test file
uv run pytest tests/test_dataset.py

# Run single test
uv run pytest tests/test_lens.py::test_lens

Building

uv build

Contributing

Contributions are welcome! This project is in beta, so the API may still evolve.

License

This project is licensed under the Mozilla Public License 2.0. See LICENSE for details.