62 lines
1.7 KiB
Markdown
62 lines
1.7 KiB
Markdown
Lighting offers a few options for logging information about model, gpu usage, etc (via test-tube). It also offers printing options for training monitoring.
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#### Display metrics in progress bar
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``` {.python}
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# DEFAULT
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trainer = Trainer(progress_bar=True)
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```
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---
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#### Log metric row every k batches
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Every k batches lightning will make an entry in the metrics log
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``` {.python}
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# DEFAULT (ie: save a .csv log file every 10 batches)
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trainer = Trainer(add_log_row_interval=10)
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```
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---
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#### Process position
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When running multiple models on the same machine we want to decide which progress bar to use.
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Lightning will stack progress bars according to this value.
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``` {.python}
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# DEFAULT
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trainer = Trainer(process_position=0)
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# if this is the second model on the node, show the second progress bar below
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trainer = Trainer(process_position=1)
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```
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---
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#### Save a snapshot of all hyperparameters
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Whenever you call .save() on the test-tube experiment it logs all the hyperparameters in current use.
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Give lightning a test-tube Experiment object to automate this for you.
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``` {.python}
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from test-tube import Experiment
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exp = Experiment(...)
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Trainer(experiment=exp)
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```
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---
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#### Snapshot code for a training run
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Whenever you call .save() on the test-tube experiment it snapshows all code and pushes to a git tag.
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Give lightning a test-tube Experiment object to automate this for you.
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``` {.python}
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from test-tube import Experiment
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exp = Experiment(create_git_tag=True)
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Trainer(experiment=exp)
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```
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---
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#### Write logs file to csv every k batches
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Every k batches, lightning will write the new logs to disk
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``` {.python}
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# DEFAULT (ie: save a .csv log file every 100 batches)
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trainer = Trainer(log_save_interval=100)
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```
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