Reference trainer properties in docs (#16969)
This commit is contained in:
Carlos Mocholí
GitHub
parent
8074219f02
commit
1835cc0bb2
|
|
@ -1177,27 +1177,38 @@ Properties
|
|||
callback_metrics
|
||||
****************
|
||||
|
||||
The metrics available to callbacks. These are automatically set when you log via `self.log`
|
||||
The metrics available to callbacks.
|
||||
|
||||
.. code-block:: python
|
||||
This includes metrics logged via :meth:`~lightning.pytorch.core.module.LightningModule.log`.
|
||||
|
||||
..code-block:: python
|
||||
|
||||
def training_step(self, batch, batch_idx):
|
||||
self.log("a_val", 2)
|
||||
|
||||
self.log("a_val", 2.0)
|
||||
|
||||
callback_metrics = trainer.callback_metrics
|
||||
assert callback_metrics["a_val"] == 2
|
||||
assert callback_metrics["a_val"] == 2.0
|
||||
|
||||
logged_metrics
|
||||
**************
|
||||
|
||||
The metrics sent to the loggers.
|
||||
|
||||
This includes metrics logged via :meth:`~lightning.pytorch.core.module.LightningModule.log` with the
|
||||
:paramref:`~lightning.pytorch.core.module.LightningModule.log.logger` argument set.
|
||||
|
||||
progress_bar_metrics
|
||||
********************
|
||||
|
||||
The metrics sent to the progress bar.
|
||||
|
||||
This includes metrics logged via :meth:`~lightning.pytorch.core.module.LightningModule.log` with the
|
||||
:paramref:`~lightning.pytorch.core.module.LightningModule.log.prog_bar` argument set.
|
||||
|
||||
current_epoch
|
||||
*************
|
||||
|
||||
The number of epochs run.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
if trainer.current_epoch >= 10:
|
||||
...
|
||||
|
||||
The current epoch, updated after the epoch end hooks are run.
|
||||
|
||||
datamodule
|
||||
**********
|
||||
|
|
@ -1211,64 +1222,33 @@ The current datamodule, which is used by the trainer.
|
|||
is_last_batch
|
||||
*************
|
||||
|
||||
Whether trainer is executing last batch in the current epoch.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
if trainer.is_last_batch:
|
||||
...
|
||||
Whether trainer is executing the last batch.
|
||||
|
||||
global_step
|
||||
***********
|
||||
|
||||
The number of optimizer steps taken (does not reset each epoch).
|
||||
|
||||
This includes multiple optimizers (if enabled).
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
if trainer.global_step >= 100:
|
||||
...
|
||||
|
||||
logger
|
||||
*******
|
||||
|
||||
The current logger being used. Here's an example using tensorboard
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
logger = trainer.logger
|
||||
tensorboard = logger.experiment
|
||||
|
||||
The first :class:`~lightning.pytorch.loggers.logger.Logger` being used.
|
||||
|
||||
loggers
|
||||
********
|
||||
|
||||
The list of loggers currently being used by the Trainer.
|
||||
The list of class:`~lightning.pytorch.loggers.logger.Logger` used.
|
||||
|
||||
.. code-block:: python
|
||||
..code-block:: python
|
||||
|
||||
# List of Logger objects
|
||||
loggers = trainer.loggers
|
||||
for logger in loggers:
|
||||
for logger in trainer.loggers:
|
||||
logger.log_metrics({"foo": 1.0})
|
||||
|
||||
|
||||
logged_metrics
|
||||
**************
|
||||
|
||||
The metrics sent to the logger (visualizer).
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def training_step(self, batch, batch_idx):
|
||||
self.log("a_val", 2, logger=True)
|
||||
|
||||
|
||||
logged_metrics = trainer.logged_metrics
|
||||
assert logged_metrics["a_val"] == 2
|
||||
|
||||
log_dir
|
||||
*******
|
||||
|
||||
The directory for the current experiment. Use this to save images to, etc...
|
||||
|
||||
.. code-block:: python
|
||||
|
|
@ -1277,12 +1257,10 @@ The directory for the current experiment. Use this to save images to, etc...
|
|||
img = ...
|
||||
save_img(img, self.trainer.log_dir)
|
||||
|
||||
|
||||
|
||||
is_global_zero
|
||||
**************
|
||||
|
||||
Whether this process is the global zero in multi-node training
|
||||
Whether this process is the global zero in multi-node training.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
|
|
@ -1290,36 +1268,21 @@ Whether this process is the global zero in multi-node training
|
|||
if self.trainer.is_global_zero:
|
||||
print("in node 0, accelerator 0")
|
||||
|
||||
progress_bar_metrics
|
||||
********************
|
||||
|
||||
The metrics sent to the progress bar.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def training_step(self, batch, batch_idx):
|
||||
self.log("a_val", 2, prog_bar=True)
|
||||
|
||||
|
||||
progress_bar_metrics = trainer.progress_bar_metrics
|
||||
assert progress_bar_metrics["a_val"] == 2
|
||||
|
||||
|
||||
predict_dataloaders
|
||||
*******************
|
||||
|
||||
The current predict dataloaders of the trainer.
|
||||
Note that property returns a list of predict dataloaders.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
used_predict_dataloaders = trainer.predict_dataloaders
|
||||
|
||||
|
||||
estimated_stepping_batches
|
||||
**************************
|
||||
|
||||
Check out :meth:`~lightning.pytorch.trainer.trainer.Trainer.estimated_stepping_batches`.
|
||||
The estimated number of batches that will ``optimizer.step()`` during training.
|
||||
|
||||
This accounts for gradient accumulation and the current trainer configuration. This might sets up your training
|
||||
dataloader if hadn't been set up already.
|
||||
|
||||
..code-block:: python
|
||||
|
||||
def configure_optimizers(self):
|
||||
optimizer = ...
|
||||
stepping_batches = self.trainer.estimated_stepping_batches
|
||||
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=1e-3, total_steps=stepping_batches)
|
||||
return [optimizer], [scheduler]
|
||||
|
||||
state
|
||||
*****
|
||||
|
|
@ -1397,35 +1360,47 @@ both conditions are met. If any of these arguments is not set, it won't be consi
|
|||
trainer.fit(model)
|
||||
|
||||
|
||||
num_training_batches
|
||||
********************
|
||||
|
||||
The number of training batches that will be used during ``trainer.fit()``.
|
||||
|
||||
num_sanity_val_batches
|
||||
**********************
|
||||
|
||||
The number of validation batches that will be used during the sanity-checking part of ``trainer.fit()``.
|
||||
|
||||
num_val_batches
|
||||
***************
|
||||
|
||||
The number of validation batches that will be used during ``trainer.fit()`` or ``trainer.validate()``.
|
||||
|
||||
num_test_batches
|
||||
****************
|
||||
|
||||
The number of test batches that will be used during ``trainer.test()``.
|
||||
|
||||
num_predict_batches
|
||||
*******************
|
||||
|
||||
The number of prediction batches that will be used during ``trainer.predict()``.
|
||||
|
||||
train_dataloader
|
||||
****************
|
||||
|
||||
The current train dataloader of the trainer.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
used_train_dataloader = trainer.train_dataloader
|
||||
|
||||
|
||||
test_dataloaders
|
||||
****************
|
||||
|
||||
The current test dataloaders of the trainer.
|
||||
Note that property returns a list of test dataloaders.
|
||||
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
used_test_dataloaders = trainer.test_dataloaders
|
||||
The training dataloader(s) used during ``trainer.fit()``.
|
||||
|
||||
val_dataloaders
|
||||
***************
|
||||
|
||||
The validation dataloader(s) used during ``trainer.fit()`` or ``trainer.validate()``.
|
||||
|
||||
The current val dataloaders of the trainer.
|
||||
Note that property returns a list of val dataloaders.
|
||||
test_dataloaders
|
||||
****************
|
||||
|
||||
The test dataloader(s) used during ``trainer.test()``.
|
||||
|
||||
.. code-block:: python
|
||||
predict_dataloaders
|
||||
*******************
|
||||
|
||||
used_val_dataloaders = trainer.val_dataloaders
|
||||
The prediction dataloader(s) used during ``trainer.predict()``.
|
||||
|
|
|
|||
Loading…
Reference in New Issue