251 lines
6.4 KiB
ReStructuredText
251 lines
6.4 KiB
ReStructuredText
.. testsetup:: *
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.trainer.trainer import Trainer
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from pytorch_lightning import loggers as pl_loggers
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.. role:: hidden
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:class: hidden-section
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Loggers
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===========
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Lightning supports the most popular logging frameworks (TensorBoard, Comet, etc...).
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To use a logger, simply pass it into the :class:`~pytorch_lightning.trainer.trainer.Trainer`.
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Lightning uses TensorBoard by default.
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.. testcode::
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from pytorch_lightning import loggers as pl_loggers
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tb_logger = pl_loggers.TensorBoardLogger('logs/')
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trainer = Trainer(logger=tb_logger)
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Choose from any of the others such as MLflow, Comet, Neptune, WandB, ...
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.. testcode::
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comet_logger = pl_loggers.CometLogger(save_dir='logs/')
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trainer = Trainer(logger=comet_logger)
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To use multiple loggers, simply pass in a ``list`` or ``tuple`` of loggers ...
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.. testcode::
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tb_logger = pl_loggers.TensorBoardLogger('logs/')
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comet_logger = pl_loggers.CometLogger(save_dir='logs/')
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trainer = Trainer(logger=[tb_logger, comet_logger])
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.. note::
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All loggers log by default to `os.getcwd()`. To change the path without creating a logger set
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`Trainer(default_root_dir='/your/path/to/save/checkpoints')`
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----------
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Logging from a LightningModule
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------------------------------
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Use the Result objects to log from any lightning module.
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Training loop logging
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^^^^^^^^^^^^^^^^^^^^^
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To log in the training loop use the :class:`TrainResult`.
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.. code-block:: python
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def training_step(self, batch, batch_idx):
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loss = ...
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result = pl.TrainResult(minimize=loss)
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result.log('train_loss', loss)
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return result
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The `Result` object is simply a dictionary that gives you added methods like `log` and `write`
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and automatically detaches tensors (except for the minimize value).
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.. code-block:: python
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result = pl.TrainResult(minimize=loss)
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result.log('train_loss', loss)
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print(result)
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{'train_loss': tensor([0.2262])}
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The `TrainResult` can log at two places in the training, on each step (`TrainResult(on_step=True)`) and
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the aggregate at the end of the epoch (`TrainResult(on_epoch=True)`).
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.. code-block:: python
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for epoch in epochs:
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epoch_outs = []
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for batch in train_dataloader():
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# ......
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out = training_step(batch)
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# < ----------- log (on_step=True)
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epoch_outs.append(out)
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# < -------------- log (on_epoch=True)
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auto_reduce_log(epoch_outs)
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Validation loop logging
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^^^^^^^^^^^^^^^^^^^^^^^
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To log in the training loop use the :class:`EvalResult`.
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.. code-block:: python
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def validation_step(self, batch, batch_idx):
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loss = ...
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result = pl.EvalResult()
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result.log('val_loss', loss)
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return result
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The `EvalResult` object is simply a dictionary that gives you added methods like `log` and `write`
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and automatically detaches tensors (except for the minimize value).
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.. code-block:: python
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result = pl.EvalResult()
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result.log('val_loss', loss)
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print(result)
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{'val_loss': tensor([0.2262])}
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The `EvalResult` can log at two places in the validation loop, on each step (`EvalResult(on_step=True)`) and
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the aggregate at the end of the epoch (`EvalResult(on_epoch=True)`).
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.. code-block:: python
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def run_val_loop():
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epoch_outs = []
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for batch in val_dataloader():
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out = validation_step(batch)
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# < ----------- log (on_step=True)
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epoch_outs.append(out)
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# < -------------- log (on_epoch=True)
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auto_reduce_log(epoch_outs)
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Test loop logging
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^^^^^^^^^^^^^^^^^
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See the previous section.
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Manual logging
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^^^^^^^^^^^^^^
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For certain things like histograms, text, images, etc... you may need to use the logger object directly.
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.. code-block:: python
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def training_step(...):
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...
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# the logger you used (in this case tensorboard)
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tensorboard = self.logger.experiment
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tensorboard.add_histogram(...)
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tensorboard.add_figure(...)
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This also applies to Callbacks
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----------
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Logging from a Callback
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-----------------------
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To log from a callback, access the logger object directly
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.. code-block:: python
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class MyCallback(Callback):
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def on_train_epoch_end(self, trainer, pl_module):
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tensorboard = pl_module.logger.experiment
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tensorboard.add_histogram(...)
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tensorboard.add_figure(...)
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----------
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Make a Custom Logger
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--------------------
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You can implement your own logger by writing a class that inherits from
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:class:`LightningLoggerBase`. Use the :func:`~pytorch_lightning.loggers.base.rank_zero_only`
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decorator to make sure that only the first process in DDP training logs data.
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.. testcode::
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from pytorch_lightning.utilities import rank_zero_only
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from pytorch_lightning.loggers import LightningLoggerBase
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class MyLogger(LightningLoggerBase):
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@rank_zero_only
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def log_hyperparams(self, params):
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# params is an argparse.Namespace
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# your code to record hyperparameters goes here
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pass
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@rank_zero_only
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def log_metrics(self, metrics, step):
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# metrics is a dictionary of metric names and values
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# your code to record metrics goes here
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pass
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def save(self):
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# Optional. Any code necessary to save logger data goes here
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pass
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@rank_zero_only
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def finalize(self, status):
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# Optional. Any code that needs to be run after training
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# finishes goes here
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pass
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If you write a logger that may be useful to others, please send
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a pull request to add it to Lighting!
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----------
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Supported Loggers
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-----------------
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The following are loggers we support
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Comet
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^^^^^
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.. autoclass:: pytorch_lightning.loggers.comet.CometLogger
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:noindex:
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CSVLogger
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^^^^^^^^^
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.. autoclass:: pytorch_lightning.loggers.csv_logs.CSVLogger
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:noindex:
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MLFlow
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^^^^^^
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.. autoclass:: pytorch_lightning.loggers.mlflow.MLFlowLogger
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:noindex:
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Neptune
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^^^^^^^
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.. autoclass:: pytorch_lightning.loggers.neptune.NeptuneLogger
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:noindex:
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Tensorboard
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^^^^^^^^^^^^
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.. autoclass:: pytorch_lightning.loggers.tensorboard.TensorBoardLogger
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:noindex:
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Test-tube
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^^^^^^^^^
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.. autoclass:: pytorch_lightning.loggers.test_tube.TestTubeLogger
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:noindex:
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Weights and Biases
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^^^^^^^^^^^^^^^^^^
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.. autoclass:: pytorch_lightning.loggers.wandb.WandbLogger
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:noindex:
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