Lightning-AI/pytorch-lightning · error · ValueError

method {method!r} is invalid. Should be one of {supported_me

Error message

method {method!r} is invalid. Should be one of {supported_methods}.

What it means

The internal _check_tuner_configuration helper validates the method string used by tuner methods. It raises ValueError when method is not one of ('fit', 'validate', 'test', 'predict') — e.g. a typo like 'training' or 'Validate'.

Source

Thrown at src/lightning/pytorch/tuner/tuning.py:213

        lr_finder_callback._early_exit = True
        self._trainer.callbacks = [lr_finder_callback] + self._trainer.callbacks

        self._trainer.fit(model, train_dataloaders, val_dataloaders, datamodule)

        self._trainer.callbacks = [cb for cb in self._trainer.callbacks if cb is not lr_finder_callback]

        return lr_finder_callback.optimal_lr


def _check_tuner_configuration(
    train_dataloaders: Optional[Union[TRAIN_DATALOADERS, "pl.LightningDataModule"]] = None,
    val_dataloaders: Optional[EVAL_DATALOADERS] = None,
    dataloaders: Optional[EVAL_DATALOADERS] = None,
    method: Literal["fit", "validate", "test", "predict"] = "fit",
) -> None:
    supported_methods = ("fit", "validate", "test", "predict")
    if method not in supported_methods:
        raise ValueError(f"method {method!r} is invalid. Should be one of {supported_methods}.")

    if method == "fit":
        if dataloaders is not None:
            raise MisconfigurationException(
                f"In tuner with method={method!r}, `dataloaders` argument should be None,"
                " please consider setting `train_dataloaders` and `val_dataloaders` instead."
            )
    else:
        if train_dataloaders is not None or val_dataloaders is not None:
            raise MisconfigurationException(
                f"In tuner with `method`={method!r}, `train_dataloaders` and `val_dataloaders`"
                " arguments should be None, please consider setting `dataloaders` instead."
            )


def _check_lr_find_configuration(trainer: "pl.Trainer") -> None:
    # local import to avoid circular import
    from lightning.pytorch.callbacks.lr_finder import LearningRateFinder

View on GitHub (pinned to 9fed5c27d2)

Solutions

1. Use one of the exact strings: 'fit', 'validate', 'test', 'predict'
2. Omit method entirely if 'fit' is what you want

Example fix

// before
trainer.tuner.scale_batch_size(model, method="training")
// after
trainer.tuner.scale_batch_size(model, method="fit")

assert method in ("fit", "validate", "test", "predict"), method

def is_valid_tuner_method(m: str) -> bool:
    return m in ("fit", "validate", "test", "predict")

When it happens

Trigger: Passing method="training", "fit_train", or any unrecognized string to tuner.lr_find or tuner.scale_batch_size.

Common situations: Typos or assuming method accepts strategy names or custom stage names.

Understand the failure class

Background: "Must be a positive integer", "Invalid value", "Unsupported": the invalid-argument-value error family, when a library rejects the value you pass — this error's family across 35 libraries.

Related errors

• Device should be CUDA, got {device} instead.
• You requested to find {num_devices} devices but there are no
• `setup_optimizers` requires at least one optimizer as input.
• `setup_dataloaders` requires at least one dataloader as inpu
• Passed `{type(self).__name__}(precision={precision!r})`. Pre

AI-assisted analysis of Lightning-AI/pytorch-lightning@9fed5c27d2 (2026-08-28). Data as JSON: /api/errors/3fda1e5c9ab024ee. Report an issue: GitHub.