litdata
The Deep Learning framework to train, deploy, and ship AI products Lightning fast.
Decision gist · record as of 2026-08-14
Yes, if you train PyTorch models on datasets larger than local storage or consisting of many small cloud files. The library is actively maintained, has no known vulnerabilities, and low install friction. The permissive Apache-2.0 license poses no restrictions. Start with StreamingRawDataset for immediate benefit; use the optimize workflow when I/O becomes the bottleneck. Not necessary for small local datasets that fit in memory or on disk.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10 and torch installed.
- For cloud paths (S3, GCS, Azure), appropriate cloud credentials and boto3 must be configured.
- Low friction; pure Python wheel with no compiled dependencies.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most production and research contexts.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 611 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 233,560 downloads/mo, #9,037 on PyPI
Alternatives
Verify before relying
pip install litdata
from litdata import StreamingRawDataset
from torch.utils.data import DataLoader
dataset = StreamingRawDataset("s3://my-bucket/images/")
loader = DataLoader(dataset, batch_size=32)
for batch in loader:
pass # train_step(batch)- Whether the claimed 20x training speedup is measured under specific dataset/hardware conditions and how generalizable that figure is.
- Whether uvloop (optional on Linux/macOS) provides meaningful performance gains for typical workloads.
- Whether polars is required for keyed lookup and in-place patches (description cuts off mid-sentence).
What it is and what it does
LitData is a data pipeline library for PyTorch that addresses the bottleneck of slow data loading during model training. It provides two main workflows: StreamingRawDataset for streaming raw files directly from cloud or local storage with no preprocessing, and an optimize-then-stream pattern that chunks datasets into efficient binary formats for maximum throughput. The library handles asynchronous batched downloads with built-in retries, supports S3, GCS, Azure, and local paths through a unified interface, and integrates with PyTorch's DataLoader.
The package is built on torch, torchvision, and lightning-utilities, with additional cloud and file I/O support via boto3, requests, obstore, and tifffile. It targets developers working with large datasets that either don't fit locally, consist of millions of small files, or stream slowly from the cloud. The optimize step is a one-time preprocessing task that transforms raw data into chunked binary format; the streaming step then loads that data on-demand during training without requiring full local copies.
Use it for
- Stream image datasets from S3 during training without downloading the entire dataset to local disk.
- Preprocess and optimize a large dataset once, then train multiple models against the optimized version from cloud storage.
- Load datasets consisting of millions of small files (e.g., individual images or audio clips) efficiently via batched async downloads.
- Resume training from a checkpoint while continuing to stream data, with the index cached locally for fast restarts.
- Distribute data processing across workers before optimization to create embeddings or apply transformations at scale.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you train PyTorch models on datasets larger than local storage or consisting of many small cloud files.
The library is actively maintained, has no known vulnerabilities, and low install friction. The permissive Apache-2.0 license poses no restrictions. Start with StreamingRawDataset for immediate benefit; use the optimize workflow when I/O becomes the bottleneck. Not necessary for small local datasets that fit in memory or on disk.
Install
litdata on PyPI
Before you install
Low friction; pure Python wheel with no compiled dependencies. Active maintenance (last commit 2026-08-14) and recent release cycle. Requires torch and torchvision as runtime dependencies, which are typically already present in PyTorch projects.
Requires Python >=3.10 and torch installed. For cloud paths (S3, GCS, Azure), appropriate cloud credentials and boto3 must be configured.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most production and research contexts.
Quickstart
pip install litdata
from litdata import StreamingRawDataset
from torch.utils.data import DataLoader
dataset = StreamingRawDataset("s3://my-bucket/images/")
loader = DataLoader(dataset, batch_size=32)
for batch in loader:
pass # train_step(batch)Verify before relying
- Whether the claimed 20x training speedup is measured under specific dataset/hardware conditions and how generalizable that figure is.
- Whether uvloop (optional on Linux/macOS) provides meaningful performance gains for typical workloads.
- Whether polars is required for keyed lookup and in-place patches (description cuts off mid-sentence).
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagestorchtorchvisionlightning-utilitiesfilelocknumpyboto3requeststifffileobstore |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 233,560 / month, #9,037 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: ConsoleIntended Audience :: DevelopersNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Information Analysis |
Evidence: litdata-0.2.68-py3-none-any.whl
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