Track and Visualize Experiments

Basic

Learn how to track and visualize metrics, images and text.

basic

Intermediate

Enable third-party experiment managers with advanced visualizations.

intermediate

Advanced

Optimize model speed with advanced self.log arguments and cloud logging.

advanced

Expert

Make your own progress-bar or integrate a new experiment manager.

expert

LightningModule.log API

Dig into the LightningModule.log API in depth

MLflow Logger

The MLflow logger in PyTorch Lightning now includes a checkpoint_path_prefix parameter. This parameter allows you to prefix the checkpoint artifact’s path when logging checkpoints as artifacts.

Example usage:

import lightning as L
from lightning.pytorch.loggers import MLFlowLogger

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

# Your LightningModule definition
class LitModel(L.LightningModule):
    def training_step(self, batch, batch_idx):
        # example
        self.logger.experiment.whatever_ml_flow_supports(...)

    def any_lightning_module_function_or_hook(self):
        self.logger.experiment.whatever_ml_flow_supports(...)

# Train your model
model = LitModel()
trainer.fit(model)