timm_fine_tuning

HuggingFace TIMM fine-tuning Algorithm.

Borrowed with permission from https://github.com/huggingface/pytorch-image-models. Copyright 2020 Ross Wightman (https://github.com/rwightman)

Classes

TIMMFineTuning

class TIMMFineTuning(    model_id: str,    schema: Optional[BitfountSchema] = None,    datastructure: Optional[DataStructure] = None,    image_column_name: Optional[str] = None,    target_column_name: Optional[str] = None,    labels: Optional[list[str]] = None,    args: Optional[TIMMTrainingConfig] = None,    batch_transformations: Optional[Union[list[Union[str, _JSONDict]], dict[_TimmBatchTransformationStep, list[Union[str, _JSONDict]]]]] = None,    return_weights: bool = False,    save_path: Optional[Union[str, os.PathLike]] = None,):

HuggingFace TIMM Fine Tuning Algorithm.

Arguments

• ****kwargs**: Additional keyword arguments passed to the Worker side.
• args: The training configuration.
• batch_transformations: The batch transformations to be applied to the batches. Can be a list of strings or a list of dictionaries, which will be applied to both training and validation, or a dictionary with keys "train" and "validation" mapped to a list of strings or a list of dictionaries, specifying the batch transformations to be applied at each individual step. They are only applied if datastructure is not passed. Defaults to apply DEFAULT_IMAGE_TRANSFORMATIONS to both training and validation.
• datastructure: The datastructure relating to the dataset to be trained on. Defaults to None.
• image_column_name: The column name of the image column used in training. Defaults to None.
• labels: The labels of the target column. Defaults to None.
• model_id: The Hugging Face model ID.
• return_weights: Whether to return the weights of the model.
• save_path: The path to save the model to.
• schema: The schema of the dataset to be trained on. Defaults to None.
• target_column_name: The column name of the target column. Defaults to None.

Attributes

• class_name: The name of the algorithm class.
• fields_dict: A dictionary mapping all attributes that will be serialized in the class to their marshamllow field type. (e.g. fields_dict = {"class_name": fields.Str()}).
• nested_fields: A dictionary mapping all nested attributes to a registry that contains class names mapped to the respective classes. (e.g. nested_fields = {"datastructure": datastructure.registry})

Ancestors

• BaseAlgorithmFactory
• abc.ABC
• bitfount.federated.roles._RolesMixIn
• bitfount.types._BaseSerializableObjectMixIn

Variables

• static fields_dict : ClassVar[dict[str, marshmallow.fields.Field]]

• static nested_fields : ClassVar[dict[str, collections.abc.Mapping[str, Any]]]

Methods

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create

def create(self, role: Union[str, Role], **kwargs: Any) ‑> Any:

Create an instance representing the role specified.

modeller

def modeller(    self, **kwargs: Any,) ‑> bitfount.federated.algorithms.hugging_face_algorithms.base._HFModellerSide:

Returns the modeller side of the TIMMFineTuning algorithm.

worker

def worker(    self, **kwargs: Any,) ‑> bitfount.federated.algorithms.hugging_face_algorithms.timm_fine_tuning._WorkerSide:

Returns the worker side of the TIMMFineTuning algorithm.

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