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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A Data Streaming Library for Efficient Neural Network Training
| Date | Stars |
|---|---|
| 2026-07-24 | 1536 |
| 2026-07-25 | 1536 |
| 2026-07-28 | 1536 |
| 2026-07-30 | 1536 |
| 2026-07-31 | 1539 |
| 2026-08-02 | 1540 |
| 2026-08-03 | 1541 |
| 2026-08-04 | 1542 |
| 2026-08-05 | 1543 |
| 2026-08-06 | 1543 |
Today
— stars today
This week
+7 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.46%/day
<br />
<p align="center">
<a href="https://github.com/mosaicml/streaming#gh-light-mode-only" class="only-light">
<img src="./docs/source/_static/images/streaming-logo-light-mode.png" width="50%"/>
</a>
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The below tag is being used to remove the dark mode image on pypi website.-->
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<a href="https://github.com/mosaicml/streaming#gh-dark-mode-only" class="only-dark">
<img src="./docs/source/_static/images/streaming-logo-dark-mode.png" width="50%"/>
</a>
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</p>
<h2><p align="center">Fast, accurate streaming of training data from cloud storage</p></h2>
<h4><p align='center'>
<a href="https://www.mosaicml.com">[Website]</a>
- <a href="https://docs.mosaicml.com/projects/streaming/en/latest/getting_started/quick_start.html">[Quick Start]</a>
- <a href="https://streaming.docs.mosaicml.com/">[Docs]
- <a href="https://www.databricks.com/company/careers/open-positions?department=Mosaic%20AI&location=all">[We're Hiring!]</a>
</p></h4>
<p align="center">
<a href="https://pypi.org/project/mosaicml-streaming/">
<img alt="PyPi Version" src="https://img.shields.io/pypi/pyversions/mosaicml-streaming">
</a>
<a href="https://pypi.org/project/mosaicml-streaming/">
<img alt="PyPi Package Version" src="https://img.shields.io/pypi/v/mosaicml-streaming">
</a>
<a href="https://github.com/mosaicml/streaming/actions?query=workflow%3ATest">
<img alt="Unit test" src="https://github.com/mosaicml/streaming/actions/workflows/pytest.yaml/badge.svg">
</a>
<a href="https://pepy.tech/project/mosaicml-streaming/">
<img alt="PyPi Downloads" src="https://static.pepy.tech/personalized-badge/mosaicml-streaming?period=month&units=international_system&left_color=grey&right_color=blue&left_text=Downloads/month">
</a>
<a href="https://streaming.docs.mosaicml.com">
<img alt="Documentation" src="https://readthedocs.org/projects/streaming/badge/?version=stable">
</a>
<a href="https://dub.sh/mcomm">
<img alt="Chat @ Slack" src="https://img.shields.io/badge/slack-chat-2eb67d.svg?logo=slack">
</a>
<a href="https://github.com/mosaicml/streaming/blob/main/LICENSE">
<img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-green.svg?logo=slack">
</a>
<a href="https://gurubase.io/g/streaming">
<img alt="License" src="https://img.shields.io/badge/Gurubase-Ask%20Streaming%20Guru-006BFF">
</a>
</p>
<br />
# 👋 Welcome
We built StreamingDataset to make training on large datasets from cloud storage as fast, cheap, and scalable as possible.
It’s specially designed for multi-node, distributed training for large models—maximizing correctness guarantees, performance, and ease of use. Now, you can efficiently train anywhere, independent of your training data location. Just stream in the data you need, when you need it. To learn more about why we built StreamingDataset, read our [announcement blog](https://www.mosaicml.com/blog/mosaicml-streamingdataset).
StreamingDataset is compatible with any data type, including **images, text, video, and multimodal data**.
With support for major cloud storage providers ([AWS](https://aws.amazon.com/s3/), [OCI](https://www.oracle.com/cloud/storage/object-storage/), [GCS](https://cloud.google.com/storage), [Azure](https://azure.microsoft.com/en-us/products/storage/blobs), [Databricks](https://docs.databricks.com/en/storage/index.html), and any S3 compatible object store such as [Cloudflare R2](https://www.cloudflare.com/products/r2/), [Coreweave](https://docs.coreweave.com/storage/object-storage), [Backblaze b2](https://www.backblaze.com/b2/cloud-storage.html), etc. ) and designed as a drop-in replacement for your PyTorch [IterableDataset](https://pytorch.org/docs/sExcerpt of 15,959 characters
Read on GitHub
201
Karan Jariwala · MosaicML · United States
128
110
32
23
9
Brian
9
Daniel King
8
Aaron Gokaslan · @facebookresearch · United States
6
Scott Stevenson
6
Sean Owen · @databricks · United States
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MosaicML · United States
3
3
3
2
Mihir Patel
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Hanlin Tang · MosaicML · United States
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Vincent Chen
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4ec596e792529110, topic:deep-learning, topic:neural-network, topic:pytorch