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Hugging Face

DVCLive allows you to add experiment tracking capabilities to your Hugging Face projects.

Usage

To start using DVCLive, add a few lines to your training code in any Hugging Face project.

Include the DvcLiveCallback int the callbacks list passed to your Trainer:

+from dvclive.huggingface import DvcLiveCallback

. . .

 trainer = Trainer(
        model,
        args,
        train_dataset=train_data,
        eval_dataset=eval_data,
        tokenizer=tokenizer,
        compute_metrics=compute_metrics,
    )
+   trainer.add_callback(DvcLiveCallback())
    trainer.train()

This will generate the outputs as described in the Get Started.

Without requiring additional modifications to your training code, you can use DVCLive alongside DVC. See DVCLive with DVC for more info.

Parameters

  • model_file - (None by default) - The name of the file where the model will be saved at the end of each step.

  • **kwargs - Any additional arguments will be passed to Live.

Examples

  • Using model_file.
from dvclive.huggingface import DvcLiveCallback

trainer = Trainer(
    model,
    args,
    train_dataset=train_data,
    eval_dataset=eval_data,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)
trainer.add_callback(
    DvcLiveCallback(model_file="my_model_path"))
trainer.train()
  • Using **kwargs to customize Live.
from dvclive.huggingface import DvcLiveCallback

trainer = Trainer(
    model,
    args,
    train_dataset=train_data,
    eval_dataset=eval_data,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)
trainer.add_callback(
    DvcLiveCallback(model_file="my_model_path", path="custom_path"))
trainer.train()
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