Track and Visualize Experiments (intermediate)

.. _logging_intermediate:

############################################## Track and Visualize Experiments (intermediate) ############################################## Audience: Users who want to track more complex outputs and use third-party experiment managers.

Track audio and other artifacts

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

.. code-block:: python

from lightning.pytorch import loggers as pl_loggers

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

then access the logger's API directly

.. code-block:: python

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

.. include:: supported_exp_managers.rst

Track hyperparameters

To track hyperparameters, first call save_hyperparameters from the LightningModule init:

.. code-block:: python

class MyLightningModule(LightningModule):
    def __init__(self, learning_rate, another_parameter, *args, **kwargs):
        super().__init__()
        self.save_hyperparameters()

If your logger supports tracked hyperparameters, the hyperparameters will automatically show up on the logger dashboard.

.. TODO:: show tracked hyperparameters.

Track model topology

Multiple loggers support visualizing the model topology. Here's an example that tracks the model topology using Tensorboard.

.. code-block:: python

def any_lightning_module_function_or_hook(self):
    tensorboard_logger = self.logger

    prototype_array = torch.Tensor(32, 1, 28, 27)
    tensorboard_logger.log_graph(model=self, input_array=prototype_array)

.. TODO:: show tensorboard topology.