81 lines
2.3 KiB
ReStructuredText
81 lines
2.3 KiB
ReStructuredText
.. _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 pytorch_lightning 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 multiple metrics in the same chart
|
|
****************************************
|
|
If your logger supports plotting multiple metrics on the same chart, pass in a dictionary to *self.log*.
|
|
|
|
.. code-block:: python
|
|
|
|
self.log("performance", {"acc": acc, "recall": recall})
|
|
|
|
----
|
|
|
|
*********************
|
|
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.experiment
|
|
|
|
prototype_array = torch.Tensor(32, 1, 28, 27)
|
|
tensorboard_logger.log_graph(model=self, input_array=prototype_array)
|
|
|
|
TODO: show tensorboard topology.
|