FitLoop¶

class pytorch_lightning.loops.FitLoop(min_epochs=0, max_epochs=1000)[source]¶

Bases: pytorch_lightning.loops.base.Loop[None]

This Loop iterates over the epochs to run the training.

Parameters

min_epochs¶ (int) – The minimum number of epochs
max_epochs¶ (int) – The maximum number of epochs, can be set -1 to turn this limit off

advance()[source]¶

Runs one whole epoch.

Return type: None

connect(epoch_loop)[source]¶

Connects a training epoch loop to this fit loop.

Return type: None

on_advance_end()[source]¶

Hook to be called each time after advance is called.

Return type: None

on_advance_start()[source]¶

Prepares the dataloader for training and calls the hooks on_epoch_start and on_train_epoch_start

Return type: None

on_run_end()[source]¶

Calls the on_train_end hook.

Return type: None

on_run_start()[source]¶

Calls the on_train_start hook.

Return type: None

reset()[source]¶

Resets the internal state of this loop.

Return type: None

teardown()[source]¶

Use to release memory etc.

Return type: None

property batch_idx: int¶

Returns the current batch index (within this epoch)

Return type: int

property done: bool¶

Evaluates when to leave the loop.

Return type: bool

property max_steps: int¶

Returns the maximum number of steps to run.

Return type: int

property min_steps: Optional[int]¶

Returns the minimum number of steps to run.

Return type: Optional[int]

property restarting: bool¶

Whether the state of this loop was reloaded and it needs to restart.

Return type: bool

property running_loss: pytorch_lightning.trainer.supporters.TensorRunningAccum¶

Returns the running loss.

Return type: TensorRunningAccum

property skip: bool¶

Whether we should skip the training and immediately return from the call to run().

Return type: bool

property split_idx: int¶

Returns the index of the current batch split (within the current batch) for bptt.

Return type: int

property total_batch_idx: int¶

Returns the current batch index (across epochs)

Return type: int