Foundations Atlas

ML dev tool that saves you up to 8x in cloud GPU costs

3 followers

ML dev tool that saves you up to 8x in cloud GPU costs

3 followers

Atlas allows you to run, track & evaluate machine learning experiments on your infrastructure. Currently used by 700+ data scientists, Atlas helps run experiments concurrently & supports the usage of preemptible/spot instances, saving you up to 8x in GPU cost

Launch tags:Productivity•Developer Tools•Artificial Intelligence

Launch Team

Render Sandboxes: a computer for every agentGive every agent its own machine. Starts in under a second

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Foundations Atlas

Maker

Hello friends 👋🏽, PM for Foundations here. Thank you so much @robjama for hunting us and for the support 😍😻 We started building Atlas a few years ago for ourselves when we were working with some of the largest teams to build and deploy machine learning models for enterprise. We’ve also built some crazy things on the side, which you may have seen around the interwebs (DeepFake Joe Rogan anyone?). Through our work, we’ve encountered firsthand the problems that limit ML teams impact: 1. ML teams spend a lot of time dealing with infrastructure-related work 2. Ad-hoc experiment management chews up a lot of time and limits reproducibility 3. GPU's are expensive❗️ 💰 (2.48$/hr for a NVIDIA V100??!!) 4. Teams within organizations don't usually have visibility on all data science projects and have a difficult time collaborating 5. Existing tools aren’t flexible and require dramatic changes to workflow All of this makes it difficult to be truly productive with ML. Atlas aims to solve these 5 problems. - ⚡️ Atlas sits on top of your infrastructure and has a multi-node, multi-GPU job orchestrator run & schedule jobs remotely with 1 command. - Use < 3 lines of code to record and reproduce your experiments, alongside other features like tagging jobs, recording data artifacts, and more. - 🌎 Collaborate across your organization with Atlas Projects, project-specific discussion forums, multi-tenancy, and user access controls. - 🤑 Save on GPU costs with auto-scaling of clusters and the ability to use pre-emptive/spot instances with Atlas's built-in retry mechanism. Design principles: Atlas was built to be augmentations of existing workflows as opposed to replacements, our team is obsessed with API and developer UX design, so we paid as much attention to the terminal and SDK as we did with the UI! Since the launch of our community edition 4 weeks ago, Atlas has been downloaded by over 700+ data scientists! ❤️ We are here to get your feedback so we can continue to make Atlas better. Thank you again for your support and we hope you will find value in our work. ---------- Download the free version of Foundations Atlas Join our community Slack & chat with our Engineers!

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7yr ago

Interesting! The biggest challenge I have is compute cost (my models take hours/days to train). But spot instances are a hit and miss for me - I hate having to rerun my job all over again.. Can your "pre-emptive/spot instances with Atlas's built-in retry mechanism" help work around that?

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7yr ago

I didn't try yet, but it sounds very promising. GPUs are getting more expensive, and if it really does work, then it'll save us alot of money. And a cool GUI as well.

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7yr ago

Foundations Atlas

Maker

@engin_oner Thank you Engin!

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7yr ago

@engin_oner Tesekkurler Engin. Great feedback!

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7yr ago

I'm an instructor at the University of Toronto (world leading research in AI and ML, with one of our profs winning the Alan Turing Prize in Comp Sci last year). Dessa has been partnering with the program we are running at the university to teach students how to build startups (this year in Health Care AI!). Their experience and will to teach/help has been exceptionally helpful, and their Atlas product is providing a real leg up during our product talks with the student groups. Dessa deserves tons of attention just for their willingness to help educate and dedication, let alone their amazing product. Give it a go! This is fantastic, thanks team <3

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7yr ago

Foundations Atlas

Maker

@jules2689 Julian! What a fun time working with all of you folks at UofT. Loved having being able to contribute to your course. Thank you very much for the kind words!!

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7yr ago

This is exciting!! As a finance person in Dessa, I've been amazed by my fellow Dessans's passion and dedication when building this product! Also everyone has been so patient to educate me about what is a Docker and what is RAPIDS and why it's important that Atlas allows you to use customized Docker images = D I'm here to learn more!!!

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7yr ago

Foundations Atlas

Maker

@hellen_hou Your hard work is always appreciated Hellen, you have been a great contributor to the product itself.

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7yr ago

Cannabiscope

Data scientists and machine learning experts should not be bogged down in MLops (devops). So kudos for that. The missing part here is how to deploy the models into real-time applications.

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7yr ago

Foundations Atlas

Maker

@hakosam Thank you for your feedback Sam! You are absolutely right about that. Building model is the first step, deploying them and managing them after deployment are also critical. We have features that address these challenges as well in our products. But they are not part of the Atlas Community Edition. Would love to get your feedback on these features as well. Let me follow up offline.

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7yr ago

Foundations Atlas

Maker

Hey folks! Thank you all for the support and love! As a reminder, you can chant 1-1 with our Engineers and myself using the Dessa @slackhq channel. Use this to get help on Atlas, feature requests and have bug discussions. We also occasionally post articles we love and find interesting! Here is the invitation link: https://tinyurl.com/dessaslack

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7yr ago

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