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Loggers

Lightning supports the most popular logging frameworks (TensorBoard, Comet, etc…). TensorBoard is used by default, but you can pass to the Trainer any combintation 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 Logging options.

Comet.ml

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

pip install comet-ml

Then configure the logger and pass it to the Trainer:

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 CometLogger is available anywhere except __init__ in your LightningModule.

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)

See also

CometLogger docs.


MLflow

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

pip install mlflow

Then configure the logger and pass it to the Trainer:

from pytorch_lightning.loggers import MLFlowLogger
mlf_logger = MLFlowLogger(
    experiment_name="default",
    tracking_uri="file:./ml-runs"
)
trainer = Trainer(logger=mlf_logger)

See also

MLFlowLogger docs.


Neptune.ai

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

pip install neptune-client

Then configure the logger and pass it to the Trainer:

from pytorch_lightning.loggers import NeptuneLogger

neptune_logger = NeptuneLogger(
    api_key='ANONYMOUS',  # replace with your own
    project_name='shared/pytorch-lightning-integration',
    experiment_name='default',  # Optional,
    params={'max_epochs': 10},  # Optional,
    tags=['pytorch-lightning', 'mlp'],  # Optional,
)
trainer = Trainer(logger=neptune_logger)

The NeptuneLogger is available anywhere except __init__ in your LightningModule.

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)

See also

NeptuneLogger docs.


Tensorboard

To use TensorBoard as your logger do the following.

from pytorch_lightning.loggers import TensorBoardLogger
logger = TensorBoardLogger('tb_logs', name='my_model')
trainer = Trainer(logger=logger)

The TensorBoardLogger is available anywhere except __init__ in your LightningModule.

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)

See also

TensorBoardLogger docs.


Test Tube

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

pip install test_tube

Then configure the logger and pass it to the Trainer:

from pytorch_lightning.loggers import TestTubeLogger
logger = TestTubeLogger('tb_logs', name='my_model')
trainer = Trainer(logger=logger)

The TestTubeLogger is available anywhere except __init__ in your LightningModule.

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)

See also

TestTubeLogger docs.


Weights and Biases

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

pip install wandb

Then configure the logger and pass it to the Trainer:

from pytorch_lightning.loggers import WandbLogger
wandb_logger = WandbLogger(offline=True)
trainer = Trainer(logger=wandb_logger)

The WandbLogger is available anywhere except __init__ in your LightningModule.

class MyModule(LightningModule):
    def any_lightning_module_function_or_hook(self):
        some_img = fake_image()
        self.logger.experiment.log({
             "generated_images": [wandb.Image(some_img, caption="...")]
        })

See also

WandbLogger docs.


Multiple Loggers

Lightning supports the use of multiple loggers, just pass a list to the 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 LightningModule.

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)
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