FitLoop
class pytorch_lightning.loops. FitLoop ( min_epochs = 0 , max_epochs = None ) [source]
Bases: pytorch_lightning.loops.loop.Loop
[None
]
This Loop iterates over the epochs to run the training.
Parameters:
min_epochs (int
) – The minimum number of epochs
max_epochs (Optional
[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
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