Debug your model (basic)¶
Audience: Users who want to learn the basics of debugging models.
How does Lightning help me debug ?¶
The Lightning Trainer has a lot of arguments devoted to maximizing your debugging productivity.
Set a breakpoint¶
A breakpoint stops your code execution so you can inspect variables, etc… and allow your code to execute one line at a time.
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.
Run all your model code once quickly¶
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 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:
trainer = 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:
trainer = Trainer(fast_dev_run=7)
Note
This argument will disable tuner, checkpoint callbacks, early stopping callbacks,
loggers and logger callbacks like LearningRateMonitor and
DeviceStatsMonitor.
Shorten the epoch length¶
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.
# 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)
Run a Sanity Check¶
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: num_sanity_val_steps
argument of Trainer)
trainer = Trainer(num_sanity_val_steps=2)
Print LightningModule weights summary¶
Whenever the .fit() function gets called, the Trainer will print the weights summary for the LightningModule.
trainer.fit(...)
this generate a table like:
| Name | Type | Params | Mode
-------------------------------------------
0 | net | Sequential | 132 K | train
1 | net.0 | Linear | 131 K | train
2 | net.1 | BatchNorm1d | 1.0 K | train
To add the child modules to the summary add a ModelSummary:
from lightning.pytorch.callbacks import ModelSummary
trainer = Trainer(callbacks=[ModelSummary(max_depth=-1)])
To print the model summary if .fit() is not called:
from lightning.pytorch.utilities.model_summary import ModelSummary
model = LitModel()
summary = ModelSummary(model, max_depth=-1)
print(summary)
To turn off the autosummary use:
trainer = Trainer(enable_model_summary=False)
Print input output layer dimensions¶
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.
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:
| Name | Type | Params | Mode | In sizes | Out sizes
----------------------------------------------------------------------
0 | net | Sequential | 132 K | train | [10, 256] | [10, 512]
1 | net.0 | Linear | 131 K | train | [10, 256] | [10, 512]
2 | net.1 | BatchNorm1d | 1.0 K | train | [10, 512] | [10, 512]
when you call .fit() on the Trainer. This can help you find bugs in the composition of your layers.
