| Available Callbacks | |
| Here is the list of the available [TrainerCallback] in the library: | |
| [[autodoc]] integrations.CometCallback | |
| - setup | |
| [[autodoc]] DefaultFlowCallback | |
| [[autodoc]] PrinterCallback | |
| [[autodoc]] ProgressCallback | |
| [[autodoc]] EarlyStoppingCallback | |
| [[autodoc]] integrations.TensorBoardCallback | |
| [[autodoc]] integrations.WandbCallback | |
| - setup | |
| [[autodoc]] integrations.MLflowCallback | |
| - setup | |
| [[autodoc]] integrations.AzureMLCallback | |
| [[autodoc]] integrations.CodeCarbonCallback | |
| [[autodoc]] integrations.NeptuneCallback | |
| [[autodoc]] integrations.ClearMLCallback | |
| [[autodoc]] integrations.DagsHubCallback | |
| [[autodoc]] integrations.FlyteCallback | |
| [[autodoc]] integrations.DVCLiveCallback | |
| - setup | |
| TrainerCallback | |
| [[autodoc]] TrainerCallback | |
| Here is an example of how to register a custom callback with the PyTorch [Trainer]: | |
| thon | |
| class MyCallback(TrainerCallback): | |
| "A callback that prints a message at the beginning of training" | |
| def on_train_begin(self, args, state, control, **kwargs): | |
| print("Starting training") | |
| trainer = Trainer( | |
| model, | |
| args, | |
| train_dataset=train_dataset, | |
| eval_dataset=eval_dataset, | |
| callbacks=[MyCallback], # We can either pass the callback class this way or an instance of it (MyCallback()) | |
| ) | |
| Another way to register a callback is to call trainer.add_callback() as follows: | |
| thon | |
| trainer = Trainer() | |
| trainer.add_callback(MyCallback) | |
| Alternatively, we can pass an instance of the callback class | |
| trainer.add_callback(MyCallback()) | |
| TrainerState | |
| [[autodoc]] TrainerState | |
| TrainerControl | |
| [[autodoc]] TrainerControl |