70 lines
2.0 KiB
ReStructuredText
70 lines
2.0 KiB
ReStructuredText
.. _logging_intermediate:
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##############################################
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Track and Visualize Experiments (intermediate)
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##############################################
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**Audience:** Users who want to track more complex outputs and use third-party experiment managers.
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----
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*******************************
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Track audio and other artifacts
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*******************************
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To track other artifacts, such as histograms or model topology graphs first select one of the many loggers supported by Lightning
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.. code-block:: python
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from pytorch_lightning import loggers as pl_loggers
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tensorboard = pl_loggers.TensorBoardLogger(save_dir="")
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trainer = Trainer(logger=tensorboard)
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then access the logger's API directly
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.. code-block:: python
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def training_step(self):
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tensorboard = self.logger.experiment
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tensorboard.add_image()
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tensorboard.add_histogram(...)
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tensorboard.add_figure(...)
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----
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.. include:: supported_exp_managers.rst
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----
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*********************
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Track hyperparameters
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*********************
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To track hyperparameters, first call *save_hyperparameters* from the LightningModule init:
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.. code-block:: python
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class MyLightningModule(LightningModule):
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def __init__(self, learning_rate, another_parameter, *args, **kwargs):
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super().__init__()
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self.save_hyperparameters()
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If your logger supports tracked hyperparameters, the hyperparameters will automatically show up on the logger dashboard.
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TODO: show tracked hyperparameters.
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----
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********************
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Track model topology
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********************
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Multiple loggers support visualizing the model topology. Here's an example that tracks the model topology using Tensorboard.
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.. code-block:: python
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def any_lightning_module_function_or_hook(self):
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tensorboard_logger = self.logger.experiment
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prototype_array = torch.Tensor(32, 1, 28, 27)
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tensorboard_logger.log_graph(model=self, input_array=prototype_array)
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TODO: show tensorboard topology.
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