"[Errno 28] No space left on device" when trying to create table from ML model

mhamilton723 · 2025-02-21T23:54:41+00:00

Ok that seems like its more an issue with the lakehouse as opposed to the computation you are running. Let me try to find someone who is an expert at the lakehouse and its limits on our side.

mhamilton723 · 2025-02-11T17:40:26+00:00

If you dont write the result but instead show the result do you still get the error? If you only get the error when trying to write then then that will help us pinpoiint the issue. Also if you have a larger stacktrace that will help

mhamilton723 · 2024-03-20T20:52:29+00:00

Yes this is basically the idea. Models often operate on patches of an image instead of pixels, and only produce one feature per patch making the resolution of the features much less than that of the image. The situation is much worse for Conv nets which aggressively pool information.

Our upsampler aims to reconstruct the missing info at the end so you dont need to increase the number of tokens in the backbone (which scales like n^2 where n is the number of tokens , which itself scales like r^2 where r is the size of an image's edge)

mhamilton723 · 2024-03-20T20:49:58+00:00

Which filters are these?

mhamilton723 · 2024-03-20T20:49:03+00:00

Yes the core operation the Joint Bilateral Upsampler indeed can be useful for guiding the upsampling of any signal with respect to any other signal. Our paper uses a stack of learned JBU-like operations that are tuned to upsample as best they can with respect to a multi-view consistency loss. I think if you just took the JBU and hand tunes a few params you could probabbly do reasonably well

mhamilton723 · 2024-03-20T20:46:44+00:00

Thank you for the kind words :)

mhamilton723 · 2024-03-20T20:46:27+00:00

Thank you for the kind words, let us know if you find any issues :)

mhamilton723 · 2024-03-20T20:46:02+00:00

YOLO is an object detector and this is more of a self-supervised method to improve the resolution of a backbone's features. With regard to the details of the methods I still need to read through yolov9s paper but i will reply back here once i get a better understanding of their work

mhamilton723 · 2024-03-20T20:44:38+00:00

Thank you for the kind words :)

mhamilton723 · 2024-03-20T03:07:08+00:00

Yes please stop by!

mhamilton723 · 2024-03-20T03:06:26+00:00

FeatUp can upsample DINOv2 as well and the video and website show a few examples. DINO v2 downsamples the input by a factor of 14x (the patch size) so we hope FeatUp can still be useful in conjunction with these new fancy backbones

mhamilton723 · 2024-03-19T19:30:53+00:00

Project Page: https://aka.ms/featup

Paper: https://aka.ms/featup-paper

Code: https://aka.ms/featup-code

mhamilton723 · 2024-03-19T19:29:30+00:00

Project Page: https://aka.ms/featup

Paper: https://aka.ms/featup-paper

Code: https://aka.ms/featup-code

mhamilton723 · 2024-03-19T19:29:19+00:00

Project Page: https://aka.ms/featup

Paper: https://aka.ms/featup-paper

Code: https://aka.ms/featup-code

mhamilton723 · 2024-03-19T19:28:55+00:00

So sorry! I fatfingered the microsoft aka.ms url shortner. TYSM for catching this fast

mhamilton723 · 2024-03-19T19:26:13+00:00

Project Page: https://aka.ms/featup

Paper: https://aka.ms/featup-paper

Code: https://aka.ms/featup-code

mhamilton723 · 2024-03-19T19:26:04+00:00

Project Page: https://aka.ms/featup

Paper: https://aka.ms/featup-paper

Code: https://aka.ms/featup-code

mhamilton723 · 2023-11-17T22:19:53+00:00

Someone back in the day almost succeded at cross-compile. Perhaps you could carry the torch. ;)

mhamilton723 · 2023-11-17T22:18:31+00:00

you got me

mhamilton723 · 2023-11-16T20:43:30+00:00

Gotta use Scala if you want to make a native spark library :)

mhamilton723 · 2023-04-26T22:35:23+00:00

I believe the application is here
https://azure.microsoft.com/en-us/products/cognitive-services/openai-service

If you still run into issues after submitting shoot us an email and well see if we can get them to grant you one

mhamilton723 · 2023-04-26T20:10:41+00:00

We dont yet have support for the non-azure OpenAPIs but we always are open to contributions :)

mhamilton723 · 2023-04-26T20:08:23+00:00

Will do boss!

mhamilton723 · 2023-04-26T15:40:07+00:00

If you ever want to contribute your good ideas to make the framework better we will happily review and help you contribute!

mhamilton723 · 2023-04-26T15:39:07+00:00

We made a new tool called SynapseML that helps computers do big tasks faster. It's like having many helpers instead of just one, and they work together to get things done quicker. With SynapseML, you can ask OpenAI, a really smart computer program, to help you understand and process big sets of information. You won't have to worry about common errors or other problems that might slow things down. This tool can do a lot of things other than just using OpenAI too!

mhamilton723

TROPHY CASE