pytorch-lightning/docs/Trainer/Logging.md

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Display metrics in progress bar

# DEFAULT
trainer = Trainer(progress_bar=True)

---

Process position

When running multiple models on the same machine we want to decide which progress bar to use. Lightning will stack progress bars according to this value.

# DEFAULT
trainer = Trainer(process_position=0)

# if this is the second model on the node, show the second progress bar below
trainer = Trainer(process_position=1)

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Print which gradients are nan

This option prints a list of tensors with nan gradients.

# DEFAULT
trainer = Trainer(print_nan_grads=False)

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Save a snapshot of all hyperparameters

Whenever you call .save() on the test-tube experiment it logs all the hyperparameters in current use. Give lightning a test-tube Experiment object to automate this for you.

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Log metric row every k batches

Every k batches lightning will make an entry in the metrics log

# DEFAULT (ie: save a .csv log file every 100 batches)
trainer = Trainer(add_log_row_interval=10)

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Write logs file to csv every k batches

Every k batches, lightning will write the new logs to disk

# DEFAULT (ie: save a .csv log file every 100 batches)
trainer = Trainer(log_save_interval=100)