A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.
-
Updated
Feb 9, 2026 - Python
A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.
Minimal sharded dataset loaders, decoders, and utils for multi-modal document, image, and text datasets.
Unofficial DynaDUSt3R reimplementation trained on Stereo4D (research only).
[CVPR 2021: Oral] In this work, we show that high frequency Fourier spectrum decay discrepancies are not inherent characteristics for existing CNN-based generative models.
DALLE-tools provided useful dataset utilities to improve you workflow with WebDatasets.
High-level API for tar-based dataset
This repo is the official released code of FoPro (AAAI-2023)
A sample subset of the NIH Chest X-ray Dataset. At only 2.4% of the size of the original dataset, it allows creating an accurate classifier using the Augmented Chest X-Ray repository.
Scripts to collect data from CARLA and save them as Webdataset
High-Throughput PyTorch Sequential Data Loaders for GPU Starvation Reduction
Stream WebDataset shards straight from Backblaze B2 object storage into PyTorch training — no local staging disk. A FastAPI + Next.js sample app that packs media into .tar shards, writes them to B2, and streams them back as an IterableDataset with live throughput and a distributed worker/node shard split.
Bulk-download image-text datasets into Backblaze B2 as reproducible WebDataset tar shards with img2dataset — no local staging disk. Sample app (FastAPI + Next.js) that streams shards to S3-compatible object storage, validates download yield, and streams them back for PyTorch/JAX training.
Web project, using SparQL on dbpedia and wikidata for mental disorder search feature
Systematic VLA training optimization on 2× RTX 3090. WebDataset + FlashAttention-2 + FSDP → 3.3× throughput, 26% VRAM reduction. Profiler traces and W&B report linked. Reproducible in one command.
Add a description, image, and links to the webdataset topic page so that developers can more easily learn about it.
To associate your repository with the webdataset topic, visit your repo's landing page and select "manage topics."