dstack

A unified orchestration layer for heterogeneous AI compute. It standardizes how to manage compute and run training and inference on GPU clouds, Kubernetes, VMs, or bare-metal clusters.

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2.3KGitHub stars

REPOSITORY AT A GLANCE

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What it is

A unified orchestration layer for heterogeneous AI compute. It standardizes how to manage compute and run training and inference on GPU clouds, Kubernetes, VMs, or bare-metal clusters.

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Where it is used

No documented product use has been verified yet.

What people are saying

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 6:01:08 PM UTC.

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Mentions over time

HN mentions by year17 dated mentions

Observed mentions: 2020-08-12 13:14:20 UTC to 2025-03-03 21:57:08 UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	2	1	1
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	0	0	0
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	4	2	2
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	1	0	1
2024	2024-01-01 00:00:00 UTC	2025-01-01 00:00:00 UTC	8	1	7
2025	2025-01-01 00:00:00 UTC	2026-01-01 00:00:00 UTC	2	0	2

What Hacker News says

2 sources

peterschmidt·Aug 12, 2020

UPDATE: https://github.com/dstackai/dstack-react is outdated and was fully merged some time ago into the main repository https://github.com/dstackai/dstack. So we will delete https://github.com/dstackai/dstack-react within a week.

In Show HN: dstack – an open-source tool to build data applications easily

kaudinya·Aug 12, 2020Submission

Show HN: dstack – an open-source tool to build data applications easily

Dear HN, I am Riwaj, the cofounder of dstack.ai (https://github.com/dstackai). A few months ago, we built an online service that allows users to publish data visualizations from Python or R. The idea was to build a tool that did not require additional programming or front-end development for publishing data visualizations. Such a code can be invoked from either Jupyter notebook, RMarkdown, Python, or R scripts. Once the data is pushed, it can be accessed via a browser. Open-sourcing dstack: During our customer discovery phase, we realized that dstack.ai should integrate a lot more open source data science frameworks than we integrated ourselves. For example, as a user, I want to push a matplotlib plot, a Tensorflow model, a plotly chart, a pandas dataframe, and I expect the presentation layer to fully-support it. Supporting all types of

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artifacts and providing all the tools to work with them solely seems to be a very challenging task. With this, we open-sourced the framework. Now you can build dstack locally, and run it on your servers, or in a cloud of your choice if that’s needed. More details on the project, how to use it, and the source code of the server can be found at the https://github.com/dstackai/dstack repo. The client packages for Python and R are available at the https://github.com/dstackai/dstack-py and https://github.com/dstackai/dstack-r correspondingly. What’s next: User callbacks- so that application shows not just pre-calculated visualizations but also can fetch data from a store and process it in real-time. ML models- so that data scientists can publish a stack which binds together a pre-calculated ML model and user parameters Use cases- Support specific use cases that help data scientists to build data science models into data applications as fast as possible. We would be happy to get your feedback on the open-source framework and also get your opinion on what kind of use cases can be built on top of the framework? Thank you.

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