Manage Experiments¶

To track other artifacts, such as histograms or model topology graphs first select one of the many experiment managers (loggers) supported by Lightning

from pytorch_lightning import loggers as pl_loggers

tensorboard = pl_loggers.TensorBoardLogger()
trainer = Trainer(logger=tensorboard)

then access the logger’s API directly

def training_step(self):
    tensorboard = self.logger.experiment
    tensorboard.add_image()
    tensorboard.add_histogram(...)
    tensorboard.add_figure(...)

Comet.ml¶

To use Comet.ml first install the comet package:

pip install comet-ml

Configure the logger and pass it to the Trainer:

from pytorch_lightning.loggers import CometLogger

comet_logger = CometLogger(api_key="YOUR_COMET_API_KEY")
trainer = Trainer(logger=comet_logger)

Access the comet logger from any function (except the LightningModule init) to use its API for tracking advanced artifacts

class LitModel(LightningModule):
    def any_lightning_module_function_or_hook(self):
        comet = self.logger.experiment
        fake_images = torch.Tensor(32, 3, 28, 28)
        comet.add_image("generated_images", fake_images, 0)

Here’s the full documentation for the CometLogger.

MLflow¶

To use MLflow first install the MLflow package:

pip install mlflow

Configure the logger and pass it to the Trainer:

from pytorch_lightning.loggers import MLFlowLogger

mlf_logger = MLFlowLogger(experiment_name="lightning_logs", tracking_uri="file:./ml-runs")
trainer = Trainer(logger=mlf_logger)

Access the mlflow logger from any function (except the LightningModule init) to use its API for tracking advanced artifacts

class LitModel(LightningModule):
    def any_lightning_module_function_or_hook(self):
        mlf_logger = self.logger.experiment
        fake_images = torch.Tensor(32, 3, 28, 28)
        mlf_logger.add_image("generated_images", fake_images, 0)

Here’s the full documentation for the MLFlowLogger.

Neptune.ai¶

To use Neptune.ai first install the neptune package:

pip install neptune-client

or with conda:

conda install -c conda-forge neptune-client

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="common/pytorch-lightning-integration",  # format "<WORKSPACE/PROJECT>"
)
trainer = Trainer(logger=neptune_logger)

Access the neptune logger from any function (except the LightningModule init) to use its API for tracking advanced artifacts

class LitModel(LightningModule):
    def any_lightning_module_function_or_hook(self):
        neptune_logger = self.logger.experiment["your/metadata/structure"]
        neptune_logger.log(metadata)

Here’s the full documentation for the NeptuneLogger.

Tensorboard¶

TensorBoard can be installed with:

pip install tensorboard

Configure the logger and pass it to the Trainer:

from pytorch_lightning.loggers import TensorBoardLogger

logger = TensorBoardLogger()
trainer = Trainer(logger=logger)

Access the tensorboard logger from any function (except the LightningModule init) to use its API for tracking advanced artifacts

class LitModel(LightningModule):
    def any_lightning_module_function_or_hook(self):
        tensorboard_logger = self.logger.experiment
        fake_images = torch.Tensor(32, 3, 28, 28)
        tensorboard_logger.add_image("generated_images", fake_images, 0)

Here’s the full documentation for the TensorBoardLogger.

Weights and Biases¶

To use Weights and Biases (wandb) first install the wandb package:

pip install wandb

Configure the logger and pass it to the Trainer:

from pytorch_lightning.loggers import WandbLogger

wandb_logger = WandbLogger(project="MNIST", log_model="all")
trainer = Trainer(logger=wandb_logger)

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

Access the wandb logger from any function (except the LightningModule init) to use its API for tracking advanced artifacts

class MyModule(LightningModule):
    def any_lightning_module_function_or_hook(self):
        wandb_logger = self.logger.experiment
        fake_images = torch.Tensor(32, 3, 28, 28)

        # Option 1
        wandb_logger.log({"generated_images": [wandb.Image(fake_images, caption="...")]})

        # Option 2 for specifically logging images
        wandb_logger.log_image(key="generated_images", images=[fake_images])

Here’s the full documentation for the WandbLogger. Demo in Google Colab with hyperparameter search and model logging.

Use multiple exp managers¶

To use multiple experiment managers at the same time, pass a list to the logger Trainer argument.

from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger

logger1 = TensorBoardLogger()
logger2 = WandbLogger()
trainer = Trainer(logger=[logger1, logger2])

Access all loggers from any function (except the LightningModule init) to use their APIs for tracking advanced artifacts

class MyModule(LightningModule):
    def any_lightning_module_function_or_hook(self):
        tensorboard_logger = self.loggers.experiment[0]
        wandb_logger = self.loggers.experiment[1]

        fake_images = torch.Tensor(32, 3, 28, 28)

        tensorboard_logger.add_image("generated_images", fake_images, 0)
        wandb_logger.add_image("generated_images", fake_images, 0)