lightning/pytorch_lightning/utilities/debugging.py

79 lines
1.9 KiB
Python

"""
These flags are useful to help debug a model.
Fast dev run
------------
This flag is meant for debugging a full train/val/test loop.
It'll activate callbacks, everything but only with 1 training and 1 validation batch.
Use this to debug a full run of your program quickly
.. code-block:: python
# DEFAULT
trainer = Trainer(fast_dev_run=False)
Inspect gradient norms
----------------------
Looking at grad norms can help you figure out where training might be going wrong.
.. code-block:: python
# DEFAULT (-1 doesn't track norms)
trainer = Trainer(track_grad_norm=-1)
# track the LP norm (P=2 here)
trainer = Trainer(track_grad_norm=2)
Make model overfit on subset of data
------------------------------------
A useful debugging trick is to make your model overfit a tiny fraction of the data.
setting `overfit_pct > 0` will overwrite train_percent_check, val_percent_check, test_percent_check
.. code-block:: python
# DEFAULT don't overfit (ie: normal training)
trainer = Trainer(overfit_pct=0.0)
# overfit on 1% of data
trainer = Trainer(overfit_pct=0.01)
Print the parameter count by layer
----------------------------------
By default lightning prints a list of parameters *and submodules* when it starts training.
.. code-block:: python
# DEFAULT print a full list of all submodules and their parameters.
trainer = Trainer(weights_summary='full')
# only print the top-level modules (i.e. the children of LightningModule).
trainer = Trainer(weights_summary='top')
Print which gradients are nan
-----------------------------
This option prints a list of tensors with nan gradients::
# DEFAULT
trainer = Trainer(print_nan_grads=False)
Log GPU usage
-------------
Lightning automatically logs gpu usage to the test tube logs.
It'll only do it at the metric logging interval, so it doesn't slow down training.
"""
class MisconfigurationException(Exception):
pass