Logging & Experiment Tracking
==============================

PyTorch Lightning integrates with all major experiment tracking platforms.

Built-in Loggers
----------------

CSVLogger
~~~~~~~~~

Writes metrics to a CSV file (default when no logger is specified):

.. code-block:: python

   from lightning.pytorch.loggers import CSVLogger

   logger = CSVLogger(save_dir="logs/", name="mnist_experiment")
   trainer = Trainer(logger=logger)

TensorBoardLogger
~~~~~~~~~~~~~~~~~

.. code-block:: python

   from lightning.pytorch.loggers import TensorBoardLogger

   logger = TensorBoardLogger(
       save_dir="tb_logs/",
       name="resnet50_imagenet",
       version="v1",
       log_graph=True,
   )

   # Launch TensorBoard
   # tensorboard --logdir tb_logs/

Weights & Biases
~~~~~~~~~~~~~~~~

.. code-block:: python

   from lightning.pytorch.loggers import WandbLogger

   logger = WandbLogger(
       project="image-classification",
       name="resnet50-run-42",
       tags=["resnet", "imagenet"],
       log_model="all",       # upload checkpoints to W&B Artifacts
   )

   trainer = Trainer(logger=logger, max_epochs=100)

MLflow
~~~~~~

.. code-block:: python

   from lightning.pytorch.loggers import MLFlowLogger

   logger = MLFlowLogger(
       experiment_name="lightning-demo",
       tracking_uri="http://mlflow-server:5000",
       run_name="experiment-001",
   )

Multiple Loggers
~~~~~~~~~~~~~~~~

.. code-block:: python

   from lightning.pytorch.loggers import WandbLogger, CSVLogger, TensorBoardLogger

   trainer = Trainer(
       logger=[
           WandbLogger(project="my-project"),
           CSVLogger(save_dir="logs/"),
           TensorBoardLogger(save_dir="tb_logs/"),
       ]
   )

Custom Logger
-------------

.. code-block:: python

   from lightning.pytorch.loggers import Logger
   from argparse import Namespace
   from typing import Dict, Optional, Union

   class MyCustomLogger(Logger):
       @property
       def name(self) -> str:
           return "MyCustomLogger"

       @property
       def version(self) -> Optional[Union[int, str]]:
           return "1.0"

       def log_hyperparams(self, params: Namespace, *args, **kwargs) -> None:
           print(f"Hyperparams: {vars(params)}")

       def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
           print(f"Step {step}: {metrics}")

       def save(self) -> None:
           pass

       def finalize(self, status: str) -> None:
           pass