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Scitator avatar Scitator commented on May 12, 2024

Hi,

Docs updates are in WIP mode for now, nevertheless the API is quite strainforward.
If you are using catalyst through notebook-interface, like here, all you need to specify are:
main_metric and minimize_metric. For example, in "Setup 4 - training with additional metrics" you can run

# model training
runner.train(
    model=model,
    criterion=criterion,
    optimizer=optimizer,
    scheduler=scheduler,
    loaders=loaders,
    callbacks=[
        PrecisionCallback(),
        EarlyStoppingCallback(patience=2, min_delta=0.01)
    ],
    logdir=logdir,
    n_epochs=n_epochs,
    main_metric="precision01", # "precision03" or "precision05"
    minimize_metric=False,
    verbose=True
)

and checkpointer will save model based on "precision01" (aka accuracy) metric.
By default, SupervisedRunner (and SupervisedExperiment) have checkpointer turned on during train stage.

If you are using config-interface, like here, all you need to do - specify state_params here: main_metric and minimize_metric again.

All the experiment checkpoints are located at {your_logdir}/checkpoints/*. By default, catalyst additionally saves best checkpoint (based on chosen metric) - best.pth and last one - last.pth.

FYI, source code for train method is here and checkpointer logic through CheckpointCallback is here.

from catalyst.

gazay avatar gazay commented on May 12, 2024

Awesome, thank you!

from catalyst.

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