cheptsov

9 ms·

1. 121.▲
   by cheptsov 3y ago

   Hey everyone, I'm happy to release dstack Hub, an open-source tool that helps teams manage their ML workflows more effectively without vendor lock-in. dstack Hub extend dstack [1] with workflow scheduling capabilities and user manageme
2. 122.▲
   by cheptsov 4y ago

   Hey, the founder of dstack here. If I put it shortly, dstack allows you to define ML workflows as code and run them either locally or remotely (e.g. in a configured cloud). ML workflows here mean anything that you may want to do when you&#x
3. 123.▲
   by cheptsov 4y ago

   SEEKING CO-FOUNDER | Dev tools/MLOps | Tech co-founder | US/EU/Worldwide | Seed We aim at simplifying the MLOps stack. The first OSS version is out: https://github.com/dstackai/dstack . Plan to raise a se
4. 124.▲
   by cheptsov 4y ago

   I personally don’t think it’s a culture. I’d call it a marketing strategy. I can be wrong of course. Curious to hear what others think.
5. 125.▲
   by cheptsov 4y ago

   There is a lot of options: Large clouds (rather expensive): AWS, GCP, Azure - consider using spot instances that are cheaper Free GPU platforms: Colab, Kaggle ML platforms (cheap GPU): Jarvis Labs, Lambda Labs, TensorDoc, Genesis Cloud In c
6. 126.▲
   by cheptsov 4y ago

   An amazing idea! Applied as a speaker.
7. 127.▲
   by cheptsov 4y ago

   Would be great to have its supported by Conda. Anyone has an idea on when this might happen?
8. 128.▲
   Ask HN: What cloud GPU platforms do you use?

   1 points by cheptsov 4y ago|0 comments
9. 129.▲
   Ask HN: Recommend a simple and modern webinar platform

   1 points by cheptsov 4y ago|0 comments
10. 130.▲
    Anaconda introduces cloud-hosted Jupyter notebooks(anaconda.com)

    1 points by cheptsov 4y ago|0 comments
11. 131.▲
    by cheptsov 4y ago

    I’m listening to Superfans by Pat Flynn now. It is about practical approaches to growing a fanbase or a user community.
12. 132.▲
    by cheptsov 4y ago

    Just curious, what do you use for hosting the blog or landing page itself? For example, we use GitHub Pages for hosting the landing page. So for hosting images, we use GitHub itself and it mostly works. For the blog, we currently use Substa
13. 133.▲
    What's new in the upcoming JupyterLab 4(blog.jupyter.org)

    2 points by cheptsov 4y ago|0 comments
14. 134.▲
    by cheptsov 4y ago

    Sounds intriguing. Building beautiful docs is a super important and challenging tasks, especially for open-source dev tools! I currently use Mkdocs Material but I spent days customizing it for better appearance! Our docs: https:/&#x2F
15. 135.▲
    by cheptsov 4y ago

    Already saw it in Toolbox App and installed ;-) Gonna give it a try. Congrats on the launch, and kudos to the Fleet team!
16. 136.▲
    by cheptsov 4y ago

    Hey! Sorry for late reply. Yes, it does destroy the resources once finished. Also, it allows you to use interruptible (aka "spot") instances efficiently.
17. 137.▲
    by cheptsov 4y ago

    Yes, by default, it will copy the files. Or you could also tell dstack to use FUSE. As I said in the comment above, we think of providing a high-level Python API. THis API will allow to avoid copying the artifacts as an initial step. But of
18. 138.▲
    by cheptsov 4y ago

    Thank you! That's a great question. First and foremost, dstack treats artifacts are 1st class citizens. Here's the basic way of using them: A workflow may produce output files to a local folder. In your workflow declaration, you c
19. 139.▲
    by cheptsov 4y ago

    Thank you for the comment! Exactly, this is one of theses tasks, dstack is designed to help with. You basically write a simple Python script that does the job, tests that it works locally, and then run via dstack – asking for a more heavy m
20. 140.▲
    Show HN: Dstack – a command-line utility to provision infra for ML workflows(github.com)

    5 points by cheptsov 4y ago|8 comments
21. 141.▲
    Things That Kill MLOps(mlopsfluff.dstack.ai)

    2 points by cheptsov 4y ago|0 comments
22. 142.▲
    by cheptsov 5y ago

    Hi there! I’m one of the developers of dstack. In case you are curious about the story behind dstack, why we decide to build it and how we made certain design decision, please make sure to read our blog post: https://blog.dstack.
23. 143.▲
    Show HN: Dstack – Train models and manage data effortlessly(dstack.ai)

    17 points by cheptsov 5y ago|1 comments
24. 144.▲
    by cheptsov 6y ago

    Thank you so much. I totally understand what you mean. And this is exactly what we try to improve. Anyway, always happy to hear feedback and pass it over to the team.
25. 145.▲
    by cheptsov 6y ago

    Cool, I see the request! Will send you all the info the next week already.
26. 146.▲
    by cheptsov 6y ago

    Thank you! Will send all the info within a couple of days.
27. 147.▲
    by cheptsov 6y ago

    Julia is on the radar. I'd love some time in the future to include it into scope.
28. 148.▲
    by cheptsov 6y ago

    Our current approach to is very similar to the native Jupyter experience where notebook contains multiple separate cells. You can manipulate these cells with shortcuts or mouse. It looks very similar to Jupyter or JupyterLab. At the same ti
29. 149.▲
    by cheptsov 6y ago

    Hey, a PM of PyCharm here. Justin case someone will find it useful and relevant. Our team is currently working on a brand new IDE (based on PyCharm) for data scientists. A big part of what we're doing is convenient support for Jupyter
30. 150.▲
    AMA with PyCharm team from JetBrains on 9th July @ 16:00 UTC(reddit.com)

    3 points by cheptsov 6y ago|0 comments