Best practice for saving logits/activation values of model in PyTorch Lightning

• lightning-hydra-template

  9 3,742 5.1 Python

  PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡

I've been trying to learn PyTorch Lightning and Hydra in order to use/create my own custom deep learning template (e.g. like this) as it would greatly help with my research workflow. A lot of the work I do requires me to analyse metrics based on the logits/activations of the model.

• hydra

  14 8,304 5.9 Python

  Hydra is a framework for elegantly configuring complex applications (by facebookresearch)

  

I've been trying to learn PyTorch Lightning and Hydra in order to use/create my own custom deep learning template (e.g. like this) as it would greatly help with my research workflow. A lot of the work I do requires me to analyse metrics based on the logits/activations of the model.

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• pytorch-lightning

  9 27,176 9.9 Python

  Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.

  

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.

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