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https://github.com/wassname/pytorch-lightning.git
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22a7264e9a
* ignore in setup * show report * abs imports * abstract pass * cover loggers * doctest trains * locals * pass * revert tensorboard * use tensorboardX * revert tensorboardX * fix trains * Add TrainsLogger.set_credentials (#1179) * Add TrainsLogger.set_credentials to control trains server configuration and authentication from code. Sync trains package version. Fix CI Trains tests * Add global TrainsLogger set_bypass_mode (#1187) * Add global TrainsLogger set_bypass_mode skips all external communication Co-authored-by: bmartinn <> * rm some no-cov Co-authored-by: Martin.B <51887611+bmartinn@users.noreply.github.com>
134 lines
3.9 KiB
Python
134 lines
3.9 KiB
Python
"""
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Lightning supports most popular logging frameworks (Tensorboard, comet, weights and biases, etc...).
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To use a logger, simply pass it into the trainer. To use multiple loggers, simply pass in a ``list``
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or ``tuple`` of loggers.
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.. code-block:: python
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from pytorch_lightning import loggers
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# lightning uses tensorboard by default
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tb_logger = loggers.TensorBoardLogger()
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trainer = Trainer(logger=tb_logger)
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# or choose from any of the others such as MLFlow, Comet, Neptune, Wandb
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comet_logger = loggers.CometLogger()
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trainer = Trainer(logger=comet_logger)
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# or pass a list
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tb_logger = loggers.TensorBoardLogger()
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comet_logger = loggers.CometLogger()
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trainer = Trainer(logger=[tb_logger, comet_logger])
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.. note:: All loggers log by default to ``os.getcwd()``. To change the path without creating a logger set
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``Trainer(default_save_path='/your/path/to/save/checkpoints')``
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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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``LightningLoggerBase``. Use the ``rank_zero_only`` decorator to make sure that
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only the first process in DDP training logs data.
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.. code-block:: python
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from pytorch_lightning.loggers import LightningLoggerBase, rank_zero_only
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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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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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Using loggers
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-------------
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Call the logger anywhere except ``__init__`` in your LightningModule by doing:
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.. code-block:: python
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def train_step(...):
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# example
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self.logger.experiment.whatever_method_summary_writer_supports(...)
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def any_lightning_module_function_or_hook(...):
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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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Supported Loggers
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-----------------
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"""
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from os import environ
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from pytorch_lightning.loggers.base import LightningLoggerBase, LoggerCollection, rank_zero_only
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from pytorch_lightning.loggers.tensorboard import TensorBoardLogger
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__all__ = ['TensorBoardLogger']
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try:
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# needed to prevent ImportError and duplicated logs.
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environ["COMET_DISABLE_AUTO_LOGGING"] = "1"
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from pytorch_lightning.loggers.comet import CometLogger
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except ImportError: # pragma: no-cover
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del environ["COMET_DISABLE_AUTO_LOGGING"] # pragma: no-cover
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else:
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__all__.append('CometLogger')
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try:
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from pytorch_lightning.loggers.mlflow import MLFlowLogger
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except ImportError: # pragma: no-cover
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pass # pragma: no-cover
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else:
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__all__.append('MLFlowLogger')
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try:
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from pytorch_lightning.loggers.neptune import NeptuneLogger
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except ImportError: # pragma: no-cover
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pass # pragma: no-cover
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else:
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__all__.append('NeptuneLogger')
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try:
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from pytorch_lightning.loggers.test_tube import TestTubeLogger
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except ImportError: # pragma: no-cover
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pass # pragma: no-cover
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else:
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__all__.append('TestTubeLogger')
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try:
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from pytorch_lightning.loggers.wandb import WandbLogger
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except ImportError: # pragma: no-cover
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pass # pragma: no-cover
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else:
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__all__.append('WandbLogger')
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try:
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from pytorch_lightning.loggers.trains import TrainsLogger
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except ImportError: # pragma: no-cover
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pass # pragma: no-cover
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else:
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__all__.append('TrainsLogger')
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