87 lines
2.5 KiB
Markdown
87 lines
2.5 KiB
Markdown
The lightning training loop handles everything except the actual computations of your model. To decide what will happen in your training loop, define the [training_step function](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#training_step).
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Below are all the things lightning automates for you in the training loop.
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---
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#### Accumulated gradients
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Accumulated gradients runs K small batches of size N before doing a backwards pass. The effect is a large effective batch size of size KxN.
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``` {.python}
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# DEFAULT (ie: no accumulated grads)
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trainer = Trainer(accumulate_grad_batches=1)
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```
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---
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#### Force training for min or max epochs
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It can be useful to force training for a minimum number of epochs or limit to a max number
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``` {.python}
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# DEFAULT
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trainer = Trainer(min_nb_epochs=1, max_nb_epochs=1000)
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```
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---
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#### Early stopping
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The trainer already sets up default early stopping for you.
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To modify this behavior, pass in your own EarlyStopping callback.
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``` {.python}
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from pytorch_lightning.callbacks import EarlyStopping
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# DEFAULTS used by Trainer
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early_stop_callback = EarlyStopping(
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monitor='val_loss',
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min_delta=0.00,
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patience=3,
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verbose=False,
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mode='min'
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)
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trainer = Trainer(early_stop_callback=early_stop_callback)
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```
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---
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#### Force disable early stop
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Use this to turn off early stopping and run training to the [max_epoch](#force-training-for-min-or-max-epochs)
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``` {.python}
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# DEFAULT
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trainer = Trainer(enable_early_stop=True)
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```
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---
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#### Gradient Clipping
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Gradient clipping may be enabled to avoid exploding gradients.
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Specifically, this will [clip the gradient norm computed over all model parameters *together*](https://pytorch.org/docs/stable/nn.html#torch.nn.utils.clip_grad_norm_).
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``` {.python}
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# DEFAULT (ie: don't clip)
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trainer = Trainer(gradient_clip_val=0)
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# clip gradients with norm above 0.5
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trainer = Trainer(gradient_clip_val=0.5)
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```
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---
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#### Inspect gradient norms
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Looking at grad norms can help you figure out where training might be going wrong.
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``` {.python}
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# DEFAULT (-1 doesn't track norms)
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trainer = Trainer(track_grad_norm=-1)
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# track the LP norm (P=2 here)
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trainer = Trainer(track_grad_norm=2)
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```
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---
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#### Set how much of the training set to check
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If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag.
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train_percent_check will be overwritten by overfit_pct if `overfit_pct > 0`
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``` {.python}
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# DEFAULT
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trainer = Trainer(train_percent_check=1.0)
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# check 10% only
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trainer = Trainer(train_percent_check=0.1)
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```
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