When using the Lightning’s built-in LR finder:

# Create a Tuner
tuner = Tuner(trainer)

# finds learning rate automatically
# sets hparams.lr or hparams.learning_rate to that learning rate
tuner.lr_find(model)

a lot of checkpoint lr_find_XXX.ckpt are created in the running directory which creates clutter. How can I make sure that these checkpoint are not created? Or keep them in a dedicated directory?

>Solution :

As it is defined in the lr_finder.py as:

# Save initial model, that is loaded after learning rate is found
ckpt_path = os.path.join(trainer.default_root_dir, f".lr_find_{uuid.uuid4()}.ckpt")
trainer.save_checkpoint(ckpt_path)

the initial model is saved with the checkpoint you are mentioning lr_find_XXX.ckpt to the directory trainer.default_root_dir. If no default directory is defined during the initialization of the trainer, current working directory will be assigned as the default_root_dir. After finding the ideal learning rate lr_find restores the initial model from the checkpoint and removes the checkpoint.

# Restore initial state of model
trainer._checkpoint_connector.restore(ckpt_path)
trainer.strategy.remove_checkpoint(ckpt_path)

You are probably stopping the program before the checkpoint is restored and removed so you have two options:

1. Wait for the ideal learning rate to be found so that the checkpoint is removed
2. Change the default_root_dir: Trainer(default_root_dir='./NAME_OF_THE_DIR') but be aware that this is also the directory that the lightning logs are saved to.