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.. testsetup:: *

    from pytorch_lightning.trainer.trainer import Trainer
    from pytorch_lightning.core.lightning import LightningModule

Loggers

Lightning supports the most popular logging frameworks (TensorBoard, Comet, Neptune, etc...). TensorBoard is used by default, but you can pass to the :class:`~pytorch_lightning.trainer.trainer.Trainer` any combination of the following loggers.

Note

All loggers log by default to os.getcwd(). To change the path without creating a logger set Trainer(default_root_dir='/your/path/to/save/checkpoints')

Read more about :doc:`logging <../extensions/logging>` options.

To log arbitrary artifacts like images or audio samples use the trainer.log_dir property to resolve the path.

def training_step(self, batch, batch_idx):
    img = ...
    log_image(img, self.trainer.log_dir)

Comet.ml

Comet.ml is a third-party logger. To use :class:`~pytorch_lightning.loggers.CometLogger` as your logger do the following. First, install the package:

pip install comet-ml

Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:

.. testcode::

    import os
    from pytorch_lightning.loggers import CometLogger

    comet_logger = CometLogger(
        api_key=os.environ.get("COMET_API_KEY"),
        workspace=os.environ.get("COMET_WORKSPACE"),  # Optional
        save_dir=".",  # Optional
        project_name="default_project",  # Optional
        rest_api_key=os.environ.get("COMET_REST_API_KEY"),  # Optional
        experiment_name="default",  # Optional
    )
    trainer = Trainer(logger=comet_logger)

The :class:`~pytorch_lightning.loggers.CometLogger` is available anywhere except __init__ in your :class:`~pytorch_lightning.core.lightning.LightningModule`.

.. testcode::

    class MyModule(LightningModule):
        def any_lightning_module_function_or_hook(self):
            some_img = fake_image()
            self.logger.experiment.add_image("generated_images", some_img, 0)


.. seealso::
    :class:`~pytorch_lightning.loggers.CometLogger` docs.

MLflow

MLflow is a third-party logger. To use :class:`~pytorch_lightning.loggers.MLFlowLogger` as your logger do the following. First, install the package:

pip install mlflow

Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:

from pytorch_lightning.loggers import MLFlowLogger

mlf_logger = MLFlowLogger(experiment_name="default", tracking_uri="file:./ml-runs")
trainer = Trainer(logger=mlf_logger)
.. seealso::
    :class:`~pytorch_lightning.loggers.MLFlowLogger` docs.

Neptune.ai

Neptune.ai is a third-party logger. To use :class:`~pytorch_lightning.loggers.NeptuneLogger` as your logger do the following. First, install the package:

pip install neptune-client

or with conda:

conda install -c conda-forge neptune-client

Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:

from pytorch_lightning.loggers import NeptuneLogger

neptune_logger = NeptuneLogger(
    api_key="ANONYMOUS",  # replace with your own
    project="common/pytorch-lightning-integration",  # format "<WORKSPACE/PROJECT>"
    tags=["training", "resnet"],  # optional
)
trainer = Trainer(logger=neptune_logger)

The :class:`~pytorch_lightning.loggers.NeptuneLogger` is available anywhere except __init__ in your :class:`~pytorch_lightning.core.lightning.LightningModule`.

class MyModule(LightningModule):
    def any_lightning_module_function_or_hook(self):
        # generic recipe for logging custom metadata (neptune specific)
        metadata = ...
        self.logger.experiment["your/metadata/structure"].log(metadata)

Note that syntax: self.logger.experiment["your/metadata/structure"].log(metadata) is specific to Neptune and it extends logger capabilities. Specifically, it allows you to log various types of metadata like scores, files, images, interactive visuals, CSVs, etc. Refer to the Neptune docs for more detailed explanations.

You can always use regular logger methods: log_metrics() and log_hyperparams() as these are also supported.

.. seealso::
    :class:`~pytorch_lightning.loggers.NeptuneLogger` docs.

    Logger `user guide <https://docs.neptune.ai/integrations-and-supported-tools/model-training/pytorch-lightning>`_.

Tensorboard

To use TensorBoard as your logger do the following.

.. testcode::

    from pytorch_lightning.loggers import TensorBoardLogger

    logger = TensorBoardLogger("tb_logs", name="my_model")
    trainer = Trainer(logger=logger)

The :class:`~pytorch_lightning.loggers.TensorBoardLogger` is available anywhere except __init__ in your :class:`~pytorch_lightning.core.lightning.LightningModule`.

.. testcode::

    class MyModule(LightningModule):
        def any_lightning_module_function_or_hook(self):
            some_img = fake_image()
            self.logger.experiment.add_image("generated_images", some_img, 0)


.. seealso::
    :class:`~pytorch_lightning.loggers.TensorBoardLogger` docs.

Test Tube

Test Tube is a TensorBoard logger but with nicer file structure. To use :class:`~pytorch_lightning.loggers.TestTubeLogger` as your logger do the following. First, install the package:

pip install test_tube

Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:

from pytorch_lightning.loggers import TestTubeLogger

logger = TestTubeLogger("tb_logs", name="my_model")
trainer = Trainer(logger=logger)

The :class:`~pytorch_lightning.loggers.TestTubeLogger` is available anywhere except __init__ in your :class:`~pytorch_lightning.core.lightning.LightningModule`.

.. testcode::

    class MyModule(LightningModule):
        def any_lightning_module_function_or_hook(self):
            some_img = fake_image()
            self.logger.experiment.add_image("generated_images", some_img, 0)


.. seealso::
    :class:`~pytorch_lightning.loggers.TestTubeLogger` docs.

Weights and Biases

Weights and Biases is a third-party logger. To use :class:`~pytorch_lightning.loggers.WandbLogger` as your logger do the following. First, install the package:

pip install wandb

Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:

from pytorch_lightning.loggers import WandbLogger

# instrument experiment with W&B
wandb_logger = WandbLogger(project="MNIST", log_model="all")
trainer = Trainer(logger=wandb_logger)

# log gradients and model topology
wandb_logger.watch(model)

The :class:`~pytorch_lightning.loggers.WandbLogger` is available anywhere except __init__ in your :class:`~pytorch_lightning.core.lightning.LightningModule`.

class MyModule(LightningModule):
    def any_lightning_module_function_or_hook(self):
        some_img = fake_image()
        self.log({"generated_images": [wandb.Image(some_img, caption="...")]})
.. seealso::
    - :class:`~pytorch_lightning.loggers.WandbLogger` docs.
    - `W&B Documentation <https://docs.wandb.ai/integrations/lightning>`__
    - `Demo in Google Colab <http://wandb.me/lightning>`__ with hyperparameter search and model logging

Multiple Loggers

Lightning supports the use of multiple loggers, just pass a list to the :class:`~pytorch_lightning.trainer.trainer.Trainer`.

from pytorch_lightning.loggers import TensorBoardLogger, TestTubeLogger

logger1 = TensorBoardLogger("tb_logs", name="my_model")
logger2 = TestTubeLogger("tb_logs", name="my_model")
trainer = Trainer(logger=[logger1, logger2])

The loggers are available as a list anywhere except __init__ in your :class:`~pytorch_lightning.core.lightning.LightningModule`.

.. testcode::

    class MyModule(LightningModule):
        def any_lightning_module_function_or_hook(self):
            some_img = fake_image()
            # Option 1
            self.logger.experiment[0].add_image("generated_images", some_img, 0)
            # Option 2
            self.logger[0].experiment.add_image("generated_images", some_img, 0)