shitty_gaze_mouse_controller VS pytorch-lightning

original project: https://github.com/SubhranshuSharma/shitty_gaze_mouse_controller

So decided to use cheat engine to find a pointer chain that consistently resolves to ram address of coordinates of an html text element that js moves based on inference results.

original project: https://github.com/SubhranshuSharma/shitty_gaze_mouse_contro...

Notice the cursor goes left when eye goes left, right for right, at the camera for up, down for down, left eyebrow up for enable/disable, left eye blink for double click QUICK START- And before anything the main.py file takes photos of you, run main.py and raise your left eyebrow,then make your left eye look up then down then left then right then close our eye everytime u have to press s to save image anything else to retake photo https://github.com/SubhranshuSharma/shitty_gaze_mouse_cotroller

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).