141 lines
3.8 KiB
ReStructuredText
141 lines
3.8 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, Weights and Biases, 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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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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Using loggers
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-------------
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Call the logger anywhere except ``__init__`` in your
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:class:`~pytorch_lightning.core.lightning.LightningModule` by doing:
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.. testcode::
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class LitModel(LightningModule):
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def training_step(self, batch, batch_idx):
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# example
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self.logger.experiment.whatever_method_summary_writer_supports(...)
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# example if logger is a tensorboard logger
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self.logger.experiment.add_image('images', grid, 0)
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self.logger.experiment.add_graph(model, images)
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def any_lightning_module_function_or_hook(self):
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self.logger.experiment.add_histogram(...)
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Read more in the `Experiment Logging use case <./experiment_logging.html>`_.
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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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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: |