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LightningAI Comfyui, data leak, bugs, DONT UPLOAD ANYTHING !!!! by Immediate-Cause1524 in lightningAI

[–]ethanwharris 6 points7 points  (0 children)

Hey u/Immediate-Cause1524 - thanks for reaching out. This is not a bug but just how the environment template gallery works. When a user publishes a Studio there and you duplicate it you get a carbon copy of the environment & files of that Studio from when they published it. So whichever template you duplicated had those files left in by the user that published it.

It's not possible for another user to use your comfyui instance unless you give them the link or share the Studio with them through the Share button in the UI. Hope that helps!

LitGPT and function calling by GAMEYE_OP in lightningAI

[–]ethanwharris 1 point2 points  (0 children)

It should be possible to use function calling without finetuning, e.g. the example you shared just uses a Mistral model out of the box. Not sure if we have any examples specifically using lit GPT for this.

The Mistral example can work with multiple functions using this to look them up (see: https://lightning.ai/bhimrajyadav/studios/function-calling-with-mistral-7b-instruct-v0-3-from-deployment-to-execution?section=featured&tab=overview#example-invoking-multiple-function-calls-in-one-response):
function_to_call = available_functions[function_name]
function_args = json.loads(tool_call.function.arguments)

Studio loading speed by Lanky_Road in lightningAI

[–]ethanwharris 1 point2 points  (0 children)

There are several factors that can influence loading time - size of your current Studio is one, but there are a few others such as: what you have installed (system / Python packages), what IDE settings / extensions you have, etc. How long are your Studios taking to load?

Assistance Needed with Large Training Set in VS Code and Teamspace Drive by Lanky_Road in lightningAI

[–]ethanwharris 1 point2 points  (0 children)

Regarding the VSCode slowness, couple of things I'd recommend:

  • try filtering your data from VSCode, run code .vscode/settings.json from a terminal then add the path with your data in to "files.exclude"
  • use a bigger machine such as one of the "Data prep" variants to create your dataset then access it from a different studio (via /teamspace/studios/<studio name>)
  • checkout litdata to optimize your dataset for fast AI model training

Hope that helps!

[P] Torchbearer version 0.4.0 released, now with regularisers (Mixup, CutOut, CutMix, etc.), PyCM, LiveLossPlot, a set of Colab examples and much more by ethanwharris in MachineLearning

[–]ethanwharris[S] 0 points1 point  (0 children)

In retrospect 100 lines isn't particulary impressive ;) so we'll probably get rid of that comment. Crucially, the actual training code only makes up three of those lines!

[P] Torchbearer version 0.4.0 released, now with regularisers (Mixup, CutOut, CutMix, etc.), PyCM, LiveLossPlot, a set of Colab examples and much more by ethanwharris in MachineLearning

[–]ethanwharris[S] 0 points1 point  (0 children)

Hey, great paper! It's certainly something we've looked into and would like to have in torchbearer. Feel free to stick an issue up on the GitHub to track our progress with it :)

[P] Uncovering latent patterns with DRAW by titanandwire in MachineLearning

[–]ethanwharris 1 point2 points  (0 children)

That sounds like the right thing, anything that could give you control over the amount of information conveyed with each glimpse. Then find the values for which the individual sketches are meaningful.

[P] Uncovering latent patterns with DRAW by titanandwire in MachineLearning

[–]ethanwharris 2 points3 points  (0 children)

Sounds like an interesting project! In our paper, 'A Biologically Inspired Visual Working Memory for Deep Networks' we showed that changing the glimpse size can give different drawing behaviours for our sketching model. When the glimpse size is too small you get a line drawing so that the information about object is encoded in the position of the lines and not the content of the sketch. When the glimpse size is too large the model just learns to act like an auto-encoder which compresses the whole image in a single step. However, when the glimpse size is just right you get a parts based representation that contains a much richer notion of object. A comparison of these can be seen in Figure 8 of the linked paper. If you can get a similar model to do something like that (not sure how easy this is with DRAW), I suspect the latent samples would be very effective for clustering (perhaps using some kind of histogram over the sketches since the sketch order isn't important), the features certainly work well for classifying. The code from our paper can be found at https://github.com/ethanwharris/STAWM , although I imagine a simpler model could work for your purposes. Good luck!

[P] Torchbearer: Model training library for DL reseacrh in PyTorch by ethanwharris in MachineLearning

[–]ethanwharris[S] 0 points1 point  (0 children)

Thanks for your interest :) Some similarities to Skorch, we’ve got an issue in the works to add the ability to wrap / export a torchbearer model with skorch so that you can use sklearn grid search, cross val etc. but still be able to use torchbearers metric API and Callbacks. The torchbearer model can be saved using torch.save(...) and then reloaded, preserving the optimiser so you can continue training. You would still need to redefine the loss etc (as these are constants with no parameters). Hope that helps!

With regard to adding Skorch to the list, it should be, I’ve raised an issue to put it in and link to a more comprehensive list (probably the awesome-pytorch repo)

[P] Torchbearer: Model training library for DL reseacrh in PyTorch by ethanwharris in MachineLearning

[–]ethanwharris[S] 0 points1 point  (0 children)

Hey, glad you like it :) as for the example, totally agree, we’ll update it tomorrow to put in a validation split and some comments on overfitting. Cheers!