pytorch-lightning VS awesome

It's very easy to get started, right in your Terminal, no fees! No credit card at all.

And there are cloud providers like https://replicate.com/ and https://lightning.ai/ that will let you use your LLM via an API key just like you did with OpenAI if you need that.

You don't need OpenAI - nobody does.

https://see.stanford.edu/Course/CS229 https://lightning.ai/ https://www.youtube.com/watch?v=00s9ireCnCw&t=57s https://towardsdatascience.com/

I've been wondering on what is the recommended method of saving logits/activations using PyTorch Lightning. I've looked at Callbacks, Loggers and ModelHooks but none of the use-cases seem to be for this kind of activity (even if I were to create my own custom variants of each utility). The ModelCheckpoint Callback in its utility makes me feel like custom Callbacks would be the way to go but I'm not quite sure. This closed GitHub issue does address my issue to some extent.

Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase , lucidrains, Stable Diffusion, Lightning and Hugging Face. Thanks for open-sourcing!

For lightweight use, pytorch-lightning is too heavy, and its source code will be very difficult for beginners to read, at least for me.

For the A100 GPU, theoretical performance is the same for FP16/BF16 and both rely on the same number of bits, meaning memory should be the same. However since it's quite newly added to PyTorch, performance seems to still be dependent on underlying operators used (pytorch lightning debugging in progress here).

In the days before "google it" was a synonym for "find it", we had different curated link sites, and even pyhsical magazines with hand-curated lists of links that people interested in a certain topic might find interesting. This still exists today in some forms, for example the "awesome lists" that you see for some programming topics, for example https://github.com/sindresorhus/awesome .

Just like there was a time when 90%-99% of all email traffic was viagra spam, I imagine in the future most of the internet by volume will be AI-generated trash, and those in the know will still circulate lists of where the other 1% can be found.

An even brighter scenario is that someone, maybe a kid tinkering in their garage, figures out how to make a search engine that finds the good stuff, doesn't immediately die to AI bot farms' SEO efforts, and is financially viable.

2. Awesome Lists

Software Engineering Blogs

He appears to be the original creator of the “Awesome X” repo: https://github.com/sindresorhus/awesome

Awesome React

I'm just going to leave this here: awesome git repo

You don't need any particular requirement to consult issues section on GitHub. If you need a place to follow along this post, my chosen repository for today's blog post is Awesome.

I have an awesome list GitHub repository that needs a few icons & a banner made. I was wondering if any students in graphic design would be willing to commission a few for me? I'm willing to pay either hourly, or by the project and can pay cash or venmo. Note that the art will end up as CC0, so you'd essentially be waiving any right to the artwork.

yah, come to think of it in the curated space, this reminds me of that awesome X family of github pages. Looks like someone compiled a bunch of them here https://github.com/sindresorhus/awesome#databases. I have found those to be highly valuable treasure troves pregnant with rich and relevant information.

10. Awesome