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Add Aim integration to traingenerator #10
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Hey @SGevorg, let's use the existing image classification w/ PyTorch template as an example here. As far as I can see, using Aim would just mean adding aim.set_params({"lr": lr, "batch_size": batch_size, "num_epochs": num_epochs}, name="hparams") around line 85 and adding aim.track(metrics["loss"], name="loss", subset=name, epoch=epoch)
aim.track(metrics["accuracy"], name="accuracy", subset=name, epoch=epoch) around line 220 (+ adding import and pip install of course). Questions:
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@jrieke thanks for the instructions. re |
Hi @jrieke , All correct, thanks for your effort!
Would be happy to help in case of more questions. Do you want me to open a PR? This is an awesome project! |
Hi @gorarakelyan, yes, feel free to open a PR if you have time, otherwise, I'll do it later (not that much work anyway) and will send it to you for testing. Make sure to add Aim as an option in the sidebar here. You can also update the scikit-learn template if you like. Best, Johannes |
Great, thanks for the instructions, @jrieke. I will work on the PR and ping you once it is ready. |
@gorarakelyan Sorry for the slight delay. I only changed 2 small things on your fork (hope that was OK; I added aim to the Thanks again for the contribution ❤️ Feel free to also add it to the scikit-learn template if you like. Btw I noticed 2 minor issues with aim when running on my machine:
Nothing major but thought I'll mention it in case you want to look into it :) |
Awesome! 🎉 Thanks a lot for the review and for reporting issues. |
Thanks for the great work.
It would be great to add Aim to train generator as an experiment logger option.
Aim is the most advanced open source experiment comparison tool available at the moment.
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