Callbacks with Trainer

1. Adding Callbacks to TrainConfig

To use callbacks in TrainConfig, add them to the callbacks list when configuring the training process.

from perceptrain import TrainConfig
from perceptrain.callbacks import SaveCheckpoint, PrintMetrics

config = TrainConfig(
    max_iter=10000,
    callbacks=[
        SaveCheckpoint(on="val_epoch_end", called_every=50),
        PrintMetrics(on="train_epoch_end", called_every=100),
    ]
)

2. Using Callbacks with Trainer

The Trainer class in perceptrain provides built-in support for executing callbacks at various stages in the training process, managed through a callback manager. By default, several callbacks are added to specific hooks to automate common tasks, such as check-pointing, metric logging, and model tracking.

Default Callbacks

Below is a list of the default callbacks and their assigned hooks:

• train_start: WritePlots, SaveCheckpoint, WriteMetrics
• train_epoch_end: SaveCheckpoint, PrintMetrics, WritePlots, LivePlotMetrics, WriteMetrics
• val_epoch_end: SaveBestCheckpoint, WriteMetrics
• train_end: LogHyperparameters, LogModelTracker, WriteMetrics, SaveCheckpoint, WritePlots

These defaults handle common needs, but you can also add custom callbacks to any hook.

Example: Adding a Custom Callback

To create a custom Trainer that includes a PrintMetrics callback executed specifically at the end of each epoch, follow the steps below.

from perceptrain.trainer import Trainer
from perceptrain.callbacks import PrintMetrics

class CustomTrainer(Trainer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.print_metrics_callback = PrintMetrics(on="train_epoch_end", called_every = 10)

    def on_train_epoch_end(self, train_epoch_loss_metrics):
        self.print_metrics_callback.run_callback(self)