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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

    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:


To change how many batches to use, change the argument to an integer. Here we run 7 batches of each:



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)

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