:orphan:
.. _debugging_basic:
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Debug your model (basic)
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**Audience**: Users who want to learn the basics of debugging models.
.. raw:: html
<video width="50%" max-width="400px" controls
poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/yt_thumbs/thumb_debugging.png"
src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/yt/Trainer+flags+7-+debugging_1.mp4"></video>
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How does Lightning help me debug ?
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The Lightning Trainer has *a lot* of arguments devoted to maximizing your debugging productivity.
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Set a breakpoint
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A breakpoint stops your code execution so you can inspect variables, etc... and allow your code to execute one line at a time.
.. code:: python
def function_to_debug():
x = 2
# set breakpoint
import pdb
pdb.set_trace()
y = x**2
In this example, the code will stop before executing the ``y = x**2`` line.
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Run all your model code once quickly
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If you've ever trained a model for days only to crash during validation or testing then this trainer argument is about to become your best friend.
The :paramref:`~lightning.pytorch.trainer.trainer.Trainer.fast_dev_run` argument in the trainer runs 5 batch of training, validation, test and prediction data through your trainer to see if there are any bugs:
.. code:: python
Trainer(fast_dev_run=True)
To change how many batches to use, change the argument to an integer. Here we run 7 batches of each:
.. code:: python
Trainer(fast_dev_run=7)
.. note::
This argument will disable tuner, checkpoint callbacks, early stopping callbacks,
loggers and logger callbacks like :class:`~lightning.pytorch.callbacks.lr_monitor.LearningRateMonitor` and
:class:`~lightning.pytorch.callbacks.device_stats_monitor.DeviceStatsMonitor`.
----
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Shorten the epoch length
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Sometimes it's helpful to only use a fraction of your training, val, test, or predict data (or a set number of batches).
For example, you can use 20% of the training set and 1% of the validation set.
On larger datasets like Imagenet, this can help you debug or test a few things faster than waiting for a full epoch.
.. testcode::
# use only 10% of training data and 1% of val data
trainer = Trainer(limit_train_batches=0.1, limit_val_batches=0.01)
# use 10 batches of train and 5 batches of val
trainer = Trainer(limit_train_batches=10, limit_val_batches=5)
----
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Run a Sanity Check
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Lightning runs **2** steps of validation in the beginning of training.
This avoids crashing in the validation loop sometime deep into a lengthy training loop.
(See: :paramref:`~lightning.pytorch.trainer.trainer.Trainer.num_sanity_val_steps`
argument of :class:`~lightning.pytorch.trainer.trainer.Trainer`)
.. testcode::
trainer = Trainer(num_sanity_val_steps=2)
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Print LightningModule weights summary
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Whenever the ``.fit()`` function gets called, the Trainer will print the weights summary for the LightningModule.
.. code:: python
trainer.fit(...)
this generate a table like:
.. code-block:: text
| Name | Type | Params
----------------------------------
0 | net | Sequential | 132 K
1 | net.0 | Linear | 131 K
2 | net.1 | BatchNorm1d | 1.0 K
To add the child modules to the summary add a :class:`~lightning.pytorch.callbacks.model_summary.ModelSummary`:
.. testcode::
from lightning.pytorch.callbacks import ModelSummary
trainer = Trainer(callbacks=[ModelSummary(max_depth=-1)])
To print the model summary if ``.fit()`` is not called:
.. code-block:: python
from lightning.pytorch.utilities.model_summary import ModelSummary
model = LitModel()
summary = ModelSummary(model, max_depth=-1)
print(summary)
To turn off the autosummary use:
.. code:: python
Trainer(enable_model_summary=False)
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Print input output layer dimensions
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Another debugging tool is to display the intermediate input- and output sizes of all your layers by setting the
``example_input_array`` attribute in your LightningModule.
.. code-block:: python
class LitModel(LightningModule):
def __init__(self, *args, **kwargs):
self.example_input_array = torch.Tensor(32, 1, 28, 28)
With the input array, the summary table will include the input and output layer dimensions:
.. code-block:: text
| Name | Type | Params | In sizes | Out sizes
--------------------------------------------------------------
0 | net | Sequential | 132 K | [10, 256] | [10, 512]
1 | net.0 | Linear | 131 K | [10, 256] | [10, 512]
2 | net.1 | BatchNorm1d | 1.0 K | [10, 512] | [10, 512]
when you call ``.fit()`` on the Trainer. This can help you find bugs in the composition of your layers.