Solvedpytorch lightning Cyclic learning rate finder as a part of Trainer

๐Ÿš€ Feature

Learning rate finder to plot lr vs loss relationship for Trainer and find a good starting learning rate.


Cyclical Learning Rates for Training Neural Networks by Leslie N. Smith documents how to find a good learning rate for training with CyclicLR scheduler.


Adding a methods to the Trainer class:

  • find() : Runs the CLR finder and plots the graph in Logger.
30 Answers

โœ”๏ธAccepted Answer

I actually did a bit of research into this and implemented this at work. It's actually very easy.

fastai's implementation just does a small run while tracking learning rate and loss, and then prints out the chart. They also have an option for finding the 'optimal' learning rate, but it's different for every use-case so even in the course they look at the graph and do it intuitively.

The easiest way to implement this with lightning that I can think of:

  1. is to use a learning rate scheduler that steps through the learning rate range you'd like to explore.
  2. Do a short run (1 epoch) using that learning rate scheduler. Make a model and Trainer and run fit().
  3. Use tensorboard or w&b or anything you want to graph loss vs learning rate (fast ai prints matplotlib graph). Or write some code to find the 'optimal' learning rate using the emitted logs.
  4. Choose your learning rate
  5. Plug in that number into a new Trainer/Model instance (remember to set the old one to .cpu()). If you used this technique you'll probably want to use another scheduler.
  6. Run as you want.

Regarding using, I don't think it would be possible to just use it, as the user would need to have a model as well. Maybe there is an adapter we can provide in the future.

I suggested this feature cause it requires a few components to work like the optimizer, dataloader and the model

Regarding the optimizer, dataloader, model, I think we don't need any improvements there as you can do everything with Trainer already. BUT, we currently do not have the ability to .step() the learning rate scheduler every iteration, so that is the main blocker.

You can easily work around this by keeping a reference to the scheduler and stepping yourself, but lightning could also add this functionality.

TLDR: work needed for this:

  • Ability to step LR schedulers every iteration
  • Make sure LR is logged everytime it changes in logging (it might already be)

Other Answers:

IMHO it doesn't make any sense to force the user to install fastai only to use a subfeature that can (and should) be present in lighting. Lighting should replace other libraries like fastai.

For example, I don't like fastai, the code base is not great and the doc is terrible, I would like to avoid installing it again on my machine to just use one feature.

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