Debug your model (intermediate)¶

Audience: Users who want to debug their ML code

Why should I debug ML code?¶

Machine learning code requires debugging mathematical correctness, which is not something non-ML code has to deal with. Lightning implements a few best-practice techniques to give all users, expert level ML debugging abilities.

Overfit your model on a Subset of Data¶

A good debugging technique is to take a tiny portion of your data (say 2 samples per class), and try to get your model to overfit. If it can’t, it’s a sign it won’t work with large datasets.

(See: overfit_batches argument of Trainer)

# use only 1% of training data (and turn off validation)
trainer = Trainer(overfit_batches=0.01)

# similar, but with a fixed 10 batches
trainer = Trainer(overfit_batches=10)

When using this argument, the validation loop will be disabled. We will also replace the sampler in the training set to turn off shuffle for you.

Look-out for exploding gradients¶

One major problem that plagues models is exploding gradients. Gradient norm is one technique that can help keep gradients from exploding.

# the 2-norm
trainer = Trainer(track_grad_norm=2)

This will plot the 2-norm to your experiment manager. If you notice the norm is going up, there’s a good chance your gradients are/will explode.

One technique to stop exploding gradients is to clip the gradient

# DEFAULT (ie: don't clip)
trainer = Trainer(gradient_clip_val=0)

# clip gradients' global norm to <=0.5 using gradient_clip_algorithm='norm' by default
trainer = Trainer(gradient_clip_val=0.5)

# clip gradients' maximum magnitude to <=0.5
trainer = Trainer(gradient_clip_val=0.5, gradient_clip_algorithm="value")

Detect autograd anomalies¶

Lightning helps you detect anomalies in the PyTorh autograd engine via PyTorch’s built-in Anomaly Detection Context-manager.

Enable it via the detect_anomaly trainer argument:

trainer = Trainer(detect_anomaly=True)