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config_schemas

Dataclasses to hold the configuration details for the runners.

Classes​

APIKeys​

class APIKeys(access_key_id: str, access_key: str):

API keys for BitfountSession.

Variables​

  • static access_key : str
  • static access_key_id : str

AccessManagerConfig​

class AccessManagerConfig(url: str = 'https://am.hub.bitfount.com'):

Configuration for the access manager.

Variables​

  • static url : str

AggregatorConfig​

class AggregatorConfig(    secure: bool, weights: Optional[dict[str, Union[int, float]]] = None,):

Configuration for the Aggregator.

Variables​

  • static secure : bool
  • static weights : Optional[dict[str, typing.Union[int, float]]]

AlgorithmConfig​

class AlgorithmConfig(name: str, arguments: Optional[Any] = None):

Configuration for the Algorithm.

Subclasses​

Variables​

  • static arguments : Optional[Any]
  • static name : str

BitfountModelReferenceConfig​

class BitfountModelReferenceConfig(    model_ref: Union[Path, str],    model_version: Optional[int] = None,    username: Optional[str] = None,    weights: Optional[str] = None,):

Configuration for BitfountModelReference.

Variables​

  • static model_version : Optional[int]
  • static username : Optional[str]
  • static weights : Optional[str]

CSVReportAlgorithmArgumentsConfig​

class CSVReportAlgorithmArgumentsConfig(    save_path: Optional[Path] = None,    original_cols: Optional[list[str]] = None,    filter: Optional[list[ColumnFilter]] = None,):

Configuration for CSVReportAlgorithm arguments.

Variables​

  • static original_cols : Optional[list[str]]

CSVReportAlgorithmConfig​

class CSVReportAlgorithmConfig(    name: str,    arguments: Optional[CSVReportAlgorithmArgumentsConfig] = CSVReportAlgorithmArgumentsConfig(save_path=None, original_cols=None, filter=None),):

Configuration for CSVReportAlgorithm.

Ancestors​

Variables​

  • static name : str

CSVReportGeneratorOphthalmologyAlgorithmArgumentsConfig​

class CSVReportGeneratorOphthalmologyAlgorithmArgumentsConfig(    save_path: Optional[Path] = None,    trial_name: Optional[str] = None,    original_cols: Optional[list[str]] = None,    rename_columns: Optional[dict[str, str]] = None,    filter: Optional[list[ColumnFilter]] = None,    match_patient_visit: Optional[MatchPatientVisit] = None,    matched_csv_path: Optional[Path] = None,    produce_matched_only: bool = True,    csv_extensions: Optional[list[str]] = None,    produce_trial_notes_csv: bool = False,    sorting_columns: Optional[dict[str, str]] = None,):

Configuration for CSVReportGeneratorOphthalmologyAlgorithm arguments.

Variables​

  • static csv_extensions : Optional[list[str]]
  • static original_cols : Optional[list[str]]
  • static produce_matched_only : bool
  • static produce_trial_notes_csv : bool
  • static rename_columns : Optional[dict[str, str]]
  • static sorting_columns : Optional[dict[str, str]]
  • static trial_name : Optional[str]

CSVReportGeneratorOphthalmologyAlgorithmConfig​

class CSVReportGeneratorOphthalmologyAlgorithmConfig(    name: str,    arguments: Optional[CSVReportGeneratorOphthalmologyAlgorithmArgumentsConfig] = CSVReportGeneratorOphthalmologyAlgorithmArgumentsConfig(save_path=None, trial_name=None, original_cols=None, rename_columns=None, filter=None, match_patient_visit=None, matched_csv_path=None, produce_matched_only=True, csv_extensions=None, produce_trial_notes_csv=False, sorting_columns=None),):

Configuration for CSVReportGeneratorOphthalmologyAlgorithm.

Ancestors​

Variables​

  • static name : str

DataSplitConfig​

class DataSplitConfig(data_splitter: str = 'percentage', args: _JSONDict = {}):

Configuration for the data splitter.

Variables​

  • static data_splitter : str

DataStructureAssignConfig​

class DataStructureAssignConfig(    target: Optional[Union[str, list[str]]] = None,    image_cols: Optional[list[str]] = None,    image_prefix: Optional[str] = None,):

Configuration for the datastructure assign argument.

Variables​

  • static image_cols : Optional[list[str]]
  • static image_prefix : Optional[str]
  • static target : Union[str, list[str], ForwardRef(None)]

DataStructureConfig​

class DataStructureConfig(    table_config: Optional[DataStructureTableConfig] = None,    assign: DataStructureAssignConfig = DataStructureAssignConfig(target=None, image_cols=None, image_prefix=None),    select: DataStructureSelectConfig = DataStructureSelectConfig(include=None, include_prefix=None, exclude=None),    transform: DataStructureTransformConfig = DataStructureTransformConfig(dataset=None, batch=None, image=None, auto_convert_grayscale_images=True),    data_split: Optional[DataSplitConfig] = None,    schema_requirements: SCHEMA_REQUIREMENTS_TYPES = 'partial',    compatible_datasources: list[str] = ['CSVSource', 'DICOMSource', 'DICOMOphthalmologySource', 'HeidelbergSource'],):

Configuration for the modeller schema and dataset options.

Variables​

  • static compatible_datasources : list[str]
  • static schema_requirements : Union[Literal['empty', 'partial', 'full'], Dict[Literal['empty', 'partial', 'full'], Any]]

DataStructureSelectConfig​

class DataStructureSelectConfig(    include: Optional[list[str]] = None,    include_prefix: Optional[str] = None,    exclude: Optional[list[str]] = None,):

Configuration for the datastructure select argument.

Variables​

  • static exclude : Optional[list[str]]
  • static include : Optional[list[str]]
  • static include_prefix : Optional[str]

DataStructureTableConfig​

class DataStructureTableConfig(    table: Union[str, dict[str, str]],    schema_types_override: Optional[Union[SchemaOverrideMapping, Mapping[str, SchemaOverrideMapping]]] = None,):

Configuration for the datastructure table arguments. Deprecated.

Variables​

  • static table : Union[str, dict[str, str]]

DataStructureTransformConfig​

class DataStructureTransformConfig(    dataset: Optional[list[dict[str, _JSONDict]]] = None,    batch: Optional[list[dict[str, _JSONDict]]] = None,    image: Optional[list[dict[str, _JSONDict]]] = None,    auto_convert_grayscale_images: bool = True,):

Configuration for the datastructure transform argument.

Variables​

  • static auto_convert_grayscale_images : bool
  • static batch : Optional[list[dict[str, dict[str, typing.Any]]]]
  • static dataset : Optional[list[dict[str, dict[str, typing.Any]]]]
  • static image : Optional[list[dict[str, dict[str, typing.Any]]]]

DatasourceConfig​

class DatasourceConfig(    datasource: str,    name: str,    data_config: PodDataConfig = PodDataConfig(force_stypes=None, column_descriptions=None, table_descriptions=None, description=None, ignore_cols=None, modifiers=None, datasource_args={}, data_split=None, auto_tidy=False, file_system_filters=None),    datasource_details_config: Optional[PodDetailsConfig] = None,    schema: Optional[Path] = None,):

Datasource configuration for a multi-datasource Pod.

Variables​

  • static datasource : str
  • static name : str

ETDRSAlgorithmArgumentsConfig​

class ETDRSAlgorithmArgumentsConfig(    laterality: str,    slo_photo_location_prefixes: Optional[SLOSegmentationLocationPrefix] = None,    slo_image_metadata_columns: Optional[SLOImageMetadataColumns] = None,    oct_image_metadata_columns: Optional[OCTImageMetadataColumns] = None,    threshold: float = 0.7,    calculate_on_oct: bool = False,    slo_mm_width: float = 8.8,    slo_mm_height: float = 8.8,):

Configuration for ETDRSAlgorithm arguments.

Variables​

  • static calculate_on_oct : bool
  • static laterality : str
  • static slo_mm_height : float
  • static slo_mm_width : float
  • static threshold : float

ETDRSAlgorithmConfig​

class ETDRSAlgorithmConfig(name: str, arguments: Optional[ETDRSAlgorithmArgumentsConfig]):

Configuration for ETDRSAlgorithm.

Ancestors​

Variables​

  • static name : str

FederatedAveragingProtocolArgumentsConfig​

class FederatedAveragingProtocolArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,    steps_between_parameter_updates: Optional[int] = None,    epochs_between_parameter_updates: Optional[int] = None,    auto_eval: bool = True,    secure_aggregation: bool = False,):

Configuration for the FedreatedAveraging Protocol arguments.

Variables​

  • static auto_eval : bool
  • static epochs_between_parameter_updates : Optional[int]
  • static secure_aggregation : bool
  • static steps_between_parameter_updates : Optional[int]

FederatedAveragingProtocolConfig​

class FederatedAveragingProtocolConfig(    name: str,    arguments: Optional[FederatedAveragingProtocolArgumentsConfig] = FederatedAveragingProtocolArgumentsConfig(aggregator=None, steps_between_parameter_updates=None, epochs_between_parameter_updates=None, auto_eval=True, secure_aggregation=False),):

Configuration for the FederatedAveraging Protocol.

Ancestors​

Variables​

  • static name : str

FederatedModelTrainingAlgorithmConfig​

class FederatedModelTrainingAlgorithmConfig(    name: str,    arguments: Optional[FederatedModelTrainingArgumentsConfig] = FederatedModelTrainingArgumentsConfig(modeller_checkpointing=True, checkpoint_filename=None),    model: Optional[ModelConfig] = None,    pretrained_file: Optional[Path] = None,):

Configuration for the FederatedModelTraining algorithm.

Ancestors​

Variables​

  • static name : str

FederatedModelTrainingArgumentsConfig​

class FederatedModelTrainingArgumentsConfig(    modeller_checkpointing: bool = True, checkpoint_filename: Optional[str] = None,):

Configuration for the FederatedModelTraining algorithm arguments.

Variables​

  • static checkpoint_filename : Optional[str]
  • static modeller_checkpointing : bool

FileSystemFilterConfig​

class FileSystemFilterConfig(    file_extension: Optional[SingleOrMulti[str]] = None,    strict_file_extension: bool = False,    file_creation_min_date: Optional[Union[Date, DateTD]] = None,    file_modification_min_date: Optional[Union[Date, DateTD]] = None,    file_creation_max_date: Optional[Union[Date, DateTD]] = None,    file_modification_max_date: Optional[Union[Date, DateTD]] = None,    min_file_size: Optional[float] = None,    max_file_size: Optional[float] = None,):

Filter files based on various criteria.

Arguments

  • file_extension: File extension(s) of the data files. If None, all files will be searched. Can either be a single file extension or a list of file extensions. Case-insensitive. Defaults to None.
  • strict_file_extension: Whether File loading should be strictly done on files with the explicit file extension provided. If set to True will only load those files in the dataset. Otherwise, it will scan the given path for files of the same type as the provided file extension. Only relevant if file_extension is provided. Defaults to False.
  • file_creation_min_date: The oldest possible date to consider for file creation. If None, this filter will not be applied. Defaults to None.
  • file_modification_min_date: The oldest possible date to consider for file modification. If None, this filter will not be applied. Defaults to None.
  • file_creation_max_date: The newest possible date to consider for file creation. If None, this filter will not be applied. Defaults to None.
  • file_modification_max_date: The newest possible date to consider for file modification. If None, this filter will not be applied. Defaults to None.
  • min_file_size: The minimum file size in megabytes to consider. If None, all files will be considered. Defaults to None.
  • max_file_size: The maximum file size in megabytes to consider. If None, all files will be considered. Defaults to None.

Variables​

  • static file_creation_max_date : Union[Date, DateTD, ForwardRef(None)]
  • static file_creation_min_date : Union[Date, DateTD, ForwardRef(None)]
  • static file_modification_max_date : Union[Date, DateTD, ForwardRef(None)]
  • static file_modification_min_date : Union[Date, DateTD, ForwardRef(None)]
  • static max_file_size : Optional[float]
  • static min_file_size : Optional[float]
  • static strict_file_extension : bool

FoveaCoordinatesAlgorithmArgumentsConfig​

class FoveaCoordinatesAlgorithmArgumentsConfig(    bscan_width_col: str = 'size_width',    location_prefixes: Optional[SLOSegmentationLocationPrefix] = None,):

Configuration for FoveaCoordinatesAlgorithm arguments.

Variables​

  • static bscan_width_col : str

FoveaCoordinatesAlgorithmConfig​

class FoveaCoordinatesAlgorithmConfig(    name: str,    arguments: Optional[FoveaCoordinatesAlgorithmArgumentsConfig] = FoveaCoordinatesAlgorithmArgumentsConfig(bscan_width_col='size_width', location_prefixes=None),):

Configuration for FoveaCoordinatesAlgorithm.

Ancestors​

Variables​

  • static name : str

GAScreeningProtocolAmethystArgumentsConfig​

class GAScreeningProtocolAmethystArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,    results_notification_email: Optional[bool] = False,    trial_name: Optional[str] = None,    rename_columns: Optional[dict[str, str]] = None,):

Configuration for GAScreeningProtocolAmethyst arguments.

Variables​

  • static rename_columns : Optional[dict[str, str]]
  • static results_notification_email : Optional[bool]
  • static trial_name : Optional[str]

GAScreeningProtocolAmethystConfig​

class GAScreeningProtocolAmethystConfig(    name: str,    arguments: Optional[GAScreeningProtocolAmethystArgumentsConfig] = GAScreeningProtocolAmethystArgumentsConfig(aggregator=None, results_notification_email=False, trial_name=None, rename_columns=None),):

Configuration for GAScreeningProtocolAmethyst.

Ancestors​

Variables​

  • static name : str

GAScreeningProtocolBronzeArgumentsConfig​

class GAScreeningProtocolBronzeArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,    results_notification_email: Optional[bool] = False,    trial_name: Optional[str] = None,    rename_columns: Optional[dict[str, str]] = None,):

Configuration for GAScreeningProtocolBronze arguments.

Variables​

  • static rename_columns : Optional[dict[str, str]]
  • static results_notification_email : Optional[bool]
  • static trial_name : Optional[str]

GAScreeningProtocolBronzeConfig​

class GAScreeningProtocolBronzeConfig(    name: str,    arguments: Optional[GAScreeningProtocolBronzeArgumentsConfig] = GAScreeningProtocolBronzeArgumentsConfig(aggregator=None, results_notification_email=False, trial_name=None, rename_columns=None),):

Configuration for GAScreeningProtocolBronze.

Ancestors​

Variables​

  • static name : str

GAScreeningProtocolJadeArgumentsConfig​

class GAScreeningProtocolJadeArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,    results_notification_email: Optional[bool] = False,    trial_name: Optional[str] = None,    rename_columns: Optional[dict[str, str]] = None,):

Configuration for GAScreeningProtocolJade arguments.

Variables​

  • static rename_columns : Optional[dict[str, str]]
  • static results_notification_email : Optional[bool]
  • static trial_name : Optional[str]

GAScreeningProtocolJadeConfig​

class GAScreeningProtocolJadeConfig(    name: str,    arguments: Optional[GAScreeningProtocolJadeArgumentsConfig] = GAScreeningProtocolJadeArgumentsConfig(aggregator=None, results_notification_email=False, trial_name=None, rename_columns=None),):

Configuration for GAScreeningProtocolJade.

Ancestors​

Variables​

  • static name : str

GATrialCalculationAlgorithmBronzeArgumentsConfig​

class GATrialCalculationAlgorithmBronzeArgumentsConfig(    ga_area_include_segmentations: Optional[list[str]] = None,    ga_area_exclude_segmentations: Optional[list[str]] = None,    fovea_landmark_idx: Optional[int] = 1,):

Configuration for GATrialCalculationAlgorithmBronze arguments.

Variables​

  • static fovea_landmark_idx : Optional[int]
  • static ga_area_exclude_segmentations : Optional[list[str]]
  • static ga_area_include_segmentations : Optional[list[str]]

GATrialCalculationAlgorithmBronzeConfig​

class GATrialCalculationAlgorithmBronzeConfig(    name: str,    arguments: Optional[GATrialCalculationAlgorithmBronzeArgumentsConfig] = GATrialCalculationAlgorithmBronzeArgumentsConfig(ga_area_include_segmentations=None, ga_area_exclude_segmentations=None, fovea_landmark_idx=1),):

Configuration for GATrialCalculationAlgorithmBronze.

Ancestors​

Variables​

  • static name : str

GATrialCalculationAlgorithmJadeArgumentsConfig​

class GATrialCalculationAlgorithmJadeArgumentsConfig(    ga_area_include_segmentations: Optional[list[str]] = None,    ga_area_exclude_segmentations: Optional[list[str]] = None,):

Configuration for GATrialCalculationAlgorithmJade arguments.

Variables​

  • static ga_area_exclude_segmentations : Optional[list[str]]
  • static ga_area_include_segmentations : Optional[list[str]]

GATrialCalculationAlgorithmJadeConfig​

class GATrialCalculationAlgorithmJadeConfig(    name: str,    arguments: Optional[GATrialCalculationAlgorithmJadeArgumentsConfig] = GATrialCalculationAlgorithmJadeArgumentsConfig(ga_area_include_segmentations=None, ga_area_exclude_segmentations=None),):

Configuration for GATrialCalculationAlgorithmJade.

Ancestors​

Variables​

  • static name : str

GATrialPDFGeneratorAlgorithmAmethystArgumentsConfig​

class GATrialPDFGeneratorAlgorithmAmethystArgumentsConfig(    report_metadata: Optional[ReportMetadata] = None,    filename_prefix: Optional[str] = None,    save_path: Optional[Path] = None,    filter: Optional[list[ColumnFilter]] = None,    pdf_filename_columns: Optional[list[str]] = None,    trial_name: Optional[str] = None,):

Configuration for GATrialPDFGeneratorAlgorithmAmethyst arguments.

Variables​

  • static filename_prefix : Optional[str]
  • static pdf_filename_columns : Optional[list[str]]
  • static trial_name : Optional[str]

GATrialPDFGeneratorAlgorithmAmethystConfig​

class GATrialPDFGeneratorAlgorithmAmethystConfig(    name: str,    arguments: Optional[GATrialPDFGeneratorAlgorithmAmethystArgumentsConfig] = GATrialPDFGeneratorAlgorithmAmethystArgumentsConfig(report_metadata=None, filename_prefix=None, save_path=None, filter=None, pdf_filename_columns=None, trial_name=None),):

Configuration for GATrialPDFGeneratorAlgorithmAmethyst.

Ancestors​

Variables​

  • static name : str

GATrialPDFGeneratorAlgorithmJadeArgumentsConfig​

class GATrialPDFGeneratorAlgorithmJadeArgumentsConfig(    report_metadata: Optional[ReportMetadata] = None,    filename_prefix: Optional[str] = None,    save_path: Optional[Path] = None,    filter: Optional[list[ColumnFilter]] = None,    pdf_filename_columns: Optional[list[str]] = None,    trial_name: Optional[str] = None,):

Configuration for GATrialPDFGeneratorAlgorithmJade arguments.

Variables​

  • static filename_prefix : Optional[str]
  • static pdf_filename_columns : Optional[list[str]]
  • static trial_name : Optional[str]

GATrialPDFGeneratorAlgorithmJadeConfig​

class GATrialPDFGeneratorAlgorithmJadeConfig(    name: str,    arguments: Optional[GATrialPDFGeneratorAlgorithmJadeArgumentsConfig] = GATrialPDFGeneratorAlgorithmJadeArgumentsConfig(report_metadata=None, filename_prefix=None, save_path=None, filter=None, pdf_filename_columns=None, trial_name=None),):

Configuration for GATrialPDFGeneratorAlgorithmJade.

Ancestors​

Variables​

  • static name : str

GenericAlgorithmConfig​

class GenericAlgorithmConfig(name: str, arguments: _JSONDict = {}):

Configuration for unspecified algorithm plugins.

Raises

  • ValueError: if the algorithm name starts with bitfount.

Ancestors​

Variables​

  • static name : str

GenericProtocolConfig​

class GenericProtocolConfig(name: str, arguments: _JSONDict = {}):

Configuration for unspecified protocol plugins.

Raises

  • ValueError: if the protocol name starts with bitfount.

Ancestors​

Variables​

  • static name : str

HubConfig​

class HubConfig(url: str = 'https://hub.bitfount.com'):

Configuration for the hub.

Variables​

  • static url : str

HuggingFaceImageClassificationInferenceAlgorithmConfig​

class HuggingFaceImageClassificationInferenceAlgorithmConfig(    name: str,    arguments: Optional[HuggingFaceImageClassificationInferenceArgumentsConfig],):

Configuration for HuggingFaceImageClassificationInference.

Ancestors​

Variables​

  • static name : str

HuggingFaceImageClassificationInferenceArgumentsConfig​

class HuggingFaceImageClassificationInferenceArgumentsConfig(    model_id: str,    apply_softmax_to_predictions: bool = True,    batch_size: int = 1,    seed: int = 42,    top_k: int = 5,):

Configuration for HuggingFaceImageClassificationInference arguments.

Variables​

  • static apply_softmax_to_predictions : bool
  • static batch_size : int
  • static model_id : str
  • static seed : int
  • static top_k : int

HuggingFaceImageSegmentationInferenceAlgorithmConfig​

class HuggingFaceImageSegmentationInferenceAlgorithmConfig(    name: str, arguments: Optional[HuggingFaceImageSegmentationInferenceArgumentsConfig],):

Configuration for HuggingFaceImageSegmentationInference.

Ancestors​

Variables​

  • static name : str

HuggingFaceImageSegmentationInferenceArgumentsConfig​

class HuggingFaceImageSegmentationInferenceArgumentsConfig(    model_id: str,    alpha: float = 0.3,    batch_size: int = 1,    dataframe_output: bool = False,    mask_threshold: float = 0.5,    overlap_mask_area_threshold: float = 0.5,    seed: int = 42,    save_path: Optional[str] = None,    subtask: Optional[str] = None,    threshold: float = 0.9,):

Configuration for HuggingFaceImageSegmentationInference arguments.

Variables​

  • static alpha : float
  • static batch_size : int
  • static dataframe_output : bool
  • static mask_threshold : float
  • static model_id : str
  • static overlap_mask_area_threshold : float
  • static save_path : Optional[str]
  • static seed : int
  • static subtask : Optional[str]
  • static threshold : float

HuggingFacePerplexityEvaluationAlgorithmConfig​

class HuggingFacePerplexityEvaluationAlgorithmConfig(    name: str, arguments: Optional[HuggingFacePerplexityEvaluationArgumentsConfig],):

Configuration for the HuggingFacePerplexityEvaluation algorithm.

Ancestors​

Variables​

  • static name : str

HuggingFacePerplexityEvaluationArgumentsConfig​

class HuggingFacePerplexityEvaluationArgumentsConfig(    model_id: str, stride: int = 512, seed: int = 42,):

Configuration for the HuggingFacePerplexityEvaluation algorithm arguments.

Variables​

  • static model_id : str
  • static seed : int
  • static stride : int

HuggingFaceTextClassificationInferenceAlgorithmConfig​

class HuggingFaceTextClassificationInferenceAlgorithmConfig(    name: str, arguments: Optional[HuggingFaceTextClassificationInferenceArgumentsConfig],):

Configuration for HuggingFaceTextClassificationInference.

Ancestors​

Variables​

  • static name : str

HuggingFaceTextClassificationInferenceArgumentsConfig​

class HuggingFaceTextClassificationInferenceArgumentsConfig(    model_id: str,    batch_size: int = 1,    function_to_apply: Optional[str] = None,    seed: int = 42,    top_k: int = 5,):

Configuration for HuggingFaceTextClassificationInference arguments.

Variables​

  • static batch_size : int
  • static function_to_apply : Optional[str]
  • static model_id : str
  • static seed : int
  • static top_k : int

HuggingFaceTextGenerationInferenceAlgorithmConfig​

class HuggingFaceTextGenerationInferenceAlgorithmConfig(    name: str, arguments: Optional[HuggingFaceTextGenerationInferenceArgumentsConfig],):

Configuration for the HuggingFaceTextGenerationInference algorithm.

Ancestors​

Variables​

  • static name : str

HuggingFaceTextGenerationInferenceArgumentsConfig​

class HuggingFaceTextGenerationInferenceArgumentsConfig(    model_id: str,    prompt_format: Optional[str] = None,    max_length: int = 50,    num_return_sequences: int = 1,    seed: int = 42,    min_new_tokens: int = 1,    repetition_penalty: float = 1.0,    num_beams: int = 1,    early_stopping: bool = True,    pad_token_id: Optional[int] = None,    eos_token_id: Optional[int] = None,    device: Optional[str] = None,    torch_dtype: str = 'float32',):

Configuration for the HuggingFaceTextGenerationInference algorithm arguments.

Variables​

  • static device : Optional[str]
  • static early_stopping : bool
  • static eos_token_id : Optional[int]
  • static max_length : int
  • static min_new_tokens : int
  • static model_id : str
  • static num_beams : int
  • static num_return_sequences : int
  • static pad_token_id : Optional[int]
  • static prompt_format : Optional[str]
  • static repetition_penalty : float
  • static seed : int
  • static torch_dtype : str

InferenceAndCSVReportArgumentsConfig​

class InferenceAndCSVReportArgumentsConfig(aggregator: Optional[AggregatorConfig] = None):

Configuration for InferenceAndCSVReport arguments.

Variables​

InferenceAndCSVReportConfig​

class InferenceAndCSVReportConfig(    name: str,    arguments: Optional[InferenceAndCSVReportArgumentsConfig] = InferenceAndCSVReportArgumentsConfig(aggregator=None),):

Configuration for InferenceAndCSVReport.

Ancestors​

Variables​

  • static name : str

InferenceAndReturnCSVReportArgumentsConfig​

class InferenceAndReturnCSVReportArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,):

Configuration for InferenceAndReturnCSVReport arguments.

Variables​

InferenceAndReturnCSVReportConfig​

class InferenceAndReturnCSVReportConfig(    name: str,    arguments: Optional[InferenceAndReturnCSVReportArgumentsConfig] = InferenceAndReturnCSVReportArgumentsConfig(aggregator=None),):

Configuration for InferenceAndReturnCSVReport.

Ancestors​

Variables​

  • static name : str

InstrumentedInferenceAndCSVReportArgumentsConfig​

class InstrumentedInferenceAndCSVReportArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,):

Configuration for InstrumentedInferenceAndCSVReport arguments.

Variables​

InstrumentedInferenceAndCSVReportConfig​

class InstrumentedInferenceAndCSVReportConfig(    name: str,    arguments: Optional[InstrumentedInferenceAndCSVReportArgumentsConfig] = InstrumentedInferenceAndCSVReportArgumentsConfig(aggregator=None),):

Configuration for InstrumentedInferenceAndCSVReport.

Ancestors​

Variables​

  • static name : str

JWT​

class JWT(jwt: str, expires: datetime, get_token: Callable[[], tuple[str, datetime]]):

Externally managed JWT for BitfountSession.

Variables​

  • static jwt : str

ModelAlgorithmConfig​

class ModelAlgorithmConfig(    name: str,    arguments: Optional[Any] = None,    model: Optional[ModelConfig] = None,    pretrained_file: Optional[Path] = None,):

Configuration for the Model algorithms.

Ancestors​

Subclasses​

Variables​

ModelConfig​

class ModelConfig(    name: Optional[str] = None,    structure: Optional[ModelStructureConfig] = None,    bitfount_model: Optional[BitfountModelReferenceConfig] = None,    hyperparameters: _JSONDict = {},    logger_config: Optional[LoggerConfig] = None,    dp_config: Optional[DPModellerConfig] = None,):

Configuration for the model.

Variables​

  • static hyperparameters : dict[str, typing.Any]
  • static name : Optional[str]

ModelEvaluationAlgorithmConfig​

class ModelEvaluationAlgorithmConfig(    name: str,    arguments: Optional[ModelEvaluationArgumentsConfig],    model: Optional[ModelConfig] = None,    pretrained_file: Optional[Path] = None,):

Configuration for the ModelEvaluation algorithm.

Ancestors​

Variables​

  • static name : str

ModelEvaluationArgumentsConfig​

class ModelEvaluationArgumentsConfig():

Configuration for the ModelEvaluation algorithm arguments.

ModelInferenceAlgorithmConfig​

class ModelInferenceAlgorithmConfig(    name: str,    arguments: ModelInferenceArgumentsConfig = ModelInferenceArgumentsConfig(class_outputs=None),    model: Optional[ModelConfig] = None,    pretrained_file: Optional[Path] = None,):

Configuration for the ModelInference algorithm.

Ancestors​

Variables​

  • static name : str

ModelInferenceArgumentsConfig​

class ModelInferenceArgumentsConfig(class_outputs: Optional[list[str]] = None):

Configuration for the ModelInference algorithm arguments.

Variables​

  • static class_outputs : Optional[list[str]]

ModelStructureConfig​

class ModelStructureConfig(name: str, arguments: _JSONDict = {}):

Configuration for the ModelStructure.

Variables​

  • static name : str

ModelTrainingAndEvaluationAlgorithmConfig​

class ModelTrainingAndEvaluationAlgorithmConfig(    name: str,    arguments: Optional[ModelTrainingAndEvaluationArgumentsConfig],    model: Optional[ModelConfig] = None,    pretrained_file: Optional[Path] = None,):

Configuration for the ModelTrainingAndEvaluation algorithm.

Ancestors​

Variables​

  • static name : str

ModelTrainingAndEvaluationArgumentsConfig​

class ModelTrainingAndEvaluationArgumentsConfig():

Configuration for the ModelTrainingAndEvaluation algorithm arguments.

ModellerConfig​

class ModellerConfig(    pods: PodsConfig,    task: TaskConfig,    secrets: Optional[Union[APIKeys, JWT]] = None,    modeller: ModellerUserConfig = ModellerUserConfig(username='_default', identity_verification_method='oidc-device-code', private_key_file=None),    hub: HubConfig = HubConfig(url='https://hub.bitfount.com'),    message_service: MessageServiceConfig = MessageServiceConfig(url='messaging.staging.bitfount.com', port=443, tls=True, use_local_storage=False),    version: Optional[str] = None,    project_id: Optional[str] = None,    run_on_new_data_only: bool = False,    batched_execution: Optional[bool] = None,):

Full configuration for the modeller.

Subclasses​

Variables​

  • static batched_execution : Optional[bool]
  • static project_id : Optional[str]
  • static run_on_new_data_only : bool
  • static secrets : Union[APIKeys, JWT, ForwardRef(None)]
  • static version : Optional[str]

ModellerUserConfig​

class ModellerUserConfig(    username: str = '_default',    identity_verification_method: str = 'oidc-device-code',    private_key_file: Optional[Path] = None,):

Configuration for the modeller.

Arguments

  • username: The username of the modeller. This can be picked up automatically from the session but can be overridden here.
  • identity_verification_method: The method to use for identity verification. Accepts one of the values in IDENTITY_VERIFICATION_METHODS, i.e. one of key-based, oidc-auth-code or oidc-device-code.
  • private_key_file: The path to the private key file for key-based identity verification.

Variables​

  • static identity_verification_method : str
  • static username : str

PathConfig​

class PathConfig(path: Path):

Configuration for the path.

Variables​

PodConfig​

class PodConfig(    name: str,    secrets: Optional[Union[APIKeys, JWT]] = None,    pod_details_config: Optional[PodDetailsConfig] = None,    datasource: Optional[str] = None,    data_config: Optional[PodDataConfig] = None,    schema: Optional[Path] = None,    datasources: Optional[list[DatasourceConfig]] = None,    access_manager: AccessManagerConfig = AccessManagerConfig(url='https://am.hub.bitfount.com'),    hub: HubConfig = HubConfig(url='https://hub.bitfount.com'),    message_service: MessageServiceConfig = MessageServiceConfig(url='messaging.staging.bitfount.com', port=443, tls=True, use_local_storage=False),    differential_privacy: Optional[DPPodConfig] = None,    approved_pods: Optional[list[str]] = None,    username: str = '_default',    update_schema: bool = False,    pod_db: Union[bool, PodDbConfig] = False,    show_datapoints_with_results_in_db: bool = True,    version: Optional[str] = None,):

Full configuration for the pod.

Raises

  • ValueError: If a username is not provided alongside API keys.

Variables​

  • static approved_pods : Optional[list[str]]
  • static datasource : Optional[str]
  • static name : str
  • static secrets : Union[APIKeys, JWT, ForwardRef(None)]
  • static show_datapoints_with_results_in_db : bool
  • static update_schema : bool
  • static username : str
  • static version : Optional[str]
  • pod_id : str - The pod ID of the pod specified.

PodDataConfig​

class PodDataConfig(    force_stypes: Optional[dict] = None,    column_descriptions: Optional[Union[Mapping[str, Mapping[str, str]], Mapping[str, str]]] = None,    table_descriptions: Optional[Mapping[str, str]] = None,    description: Optional[str] = None,    ignore_cols: Optional[Union[list[str], Mapping[str, list[str]]]] = None,    modifiers: Optional[dict[str, DataPathModifiers]] = None,    datasource_args: _JSONDict = {},    data_split: Optional[DataSplitConfig] = None,    auto_tidy: bool = False,    file_system_filters: Optional[FileSystemFilterConfig] = None,):

Configuration for the Schema, BaseSource and Pod.

Arguments

  • force_stypes: The semantic types to force for the data. Can either be: - A mapping from pod name to type-to-column mapping (e.g. {"pod_name": {"categorical": ["col1", "col2"]}}). - A direct mapping from type to column names (e.g. {"categorical": ["col1", "col2"]}).
  • ignore_cols: The columns to ignore. This is passed to the data source.
  • modifiers: The modifiers to apply to the data. This is passed to the BaseSource.
  • datasource_args: Key-value pairs of arguments to pass to the data source constructor.
  • data_split: The data split configuration. This is passed to the data source.
  • auto_tidy: Whether to automatically tidy the data. This is used by the Pod and will result in removal of NaNs and normalisation of numeric values. Defaults to False.
  • file_system_filters: Filter files based on various criteria for datasources that are FileSystemIterable. Defaults to None.

Variables​

  • static auto_tidy : bool
  • static datasource_args : dict[str, typing.Any]
  • static description : Optional[str]
  • static force_stypes : Optional[dict]

PodDbConfig​

class PodDbConfig(path: Path):

Configuration of the Pod DB.

Variables​

PodDetailsConfig​

class PodDetailsConfig(display_name: str, description: str = ''):

Configuration for the pod details.

Arguments

  • display_name: The display name of the pod.
  • description: The description of the pod.

Variables​

  • static description : str
  • static display_name : str

PodsConfig​

class PodsConfig(identifiers: list[str]):

Configuration for the pods to use for the modeller.

Variables​

  • static identifiers : list[str]

PrivateSqlQueryAlgorithmConfig​

class PrivateSqlQueryAlgorithmConfig(    name: str, arguments: PrivateSqlQueryArgumentsConfig,):

Configuration for the PrivateSqlQuery algorithm.

Ancestors​

Variables​

  • static name : str

PrivateSqlQueryArgumentsConfig​

class PrivateSqlQueryArgumentsConfig(    query: str,    epsilon: float,    delta: float,    column_ranges: dict[str, Optional[PrivateSqlQueryColumnArgumentsConfig]],    table: Optional[str] = None,    db_schema: Optional[str] = None,):

Configuration for the PrivateSqlQuery algorithm arguments.

Variables​

  • static db_schema : Optional[str]
  • static delta : float
  • static epsilon : float
  • static query : str
  • static table : Optional[str]

PrivateSqlQueryColumnArgumentsConfig​

class PrivateSqlQueryColumnArgumentsConfig(    lower: Optional[int] = None, upper: Optional[int] = None,):

Configuration for the PrivateSqlQuery algorithm column arguments.

Variables​

  • static lower : Optional[int]
  • static upper : Optional[int]

ProtocolConfig​

class ProtocolConfig(name: str, arguments: Optional[Any] = None):

Configuration for the Protocol.

Subclasses​

Variables​

  • static arguments : Optional[Any]
  • static name : str

ResultsOnlyProtocolArgumentsConfig​

class ResultsOnlyProtocolArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None, secure_aggregation: bool = False,):

Configuration for the ResultsOnly Protocol arguments.

Variables​

  • static secure_aggregation : bool

ResultsOnlyProtocolConfig​

class ResultsOnlyProtocolConfig(    name: str,    arguments: Optional[ResultsOnlyProtocolArgumentsConfig] = ResultsOnlyProtocolArgumentsConfig(aggregator=None, secure_aggregation=False),):

Configuration for the ResultsOnly Protocol.

Ancestors​

Variables​

  • static name : str

RetinalDiseaseProtocolCobaltArgumentsConfig​

class RetinalDiseaseProtocolCobaltArgumentsConfig(    aggregator: Optional[AggregatorConfig] = None,):

Configuration for RetinalDiseaseProtocolCobalt arguments.

Variables​

RetinalDiseaseProtocolCobaltConfig​

class RetinalDiseaseProtocolCobaltConfig(    name: str,    arguments: Optional[RetinalDiseaseProtocolCobaltArgumentsConfig] = RetinalDiseaseProtocolCobaltArgumentsConfig(aggregator=None),):

Configuration for RetinalDiseaseProtocolCobalt.

Ancestors​

Variables​

  • static name : str

SqlQueryAlgorithmConfig​

class SqlQueryAlgorithmConfig(name: str, arguments: SqlQueryArgumentsConfig):

Configuration for the SqlQuery algorithm.

Ancestors​

Variables​

  • static name : str

SqlQueryArgumentsConfig​

class SqlQueryArgumentsConfig(query: str, table: Optional[str] = None):

Configuration for the SqlQuery algorithm arguments.

Variables​

  • static query : str
  • static table : Optional[str]

TIMMFineTuningAlgorithmConfig​

class TIMMFineTuningAlgorithmConfig(    name: str, arguments: Optional[TIMMFineTuningArgumentsConfig],):

Configuration for TIMMFineTuning algorithm.

Ancestors​

Variables​

  • static name : str

TIMMFineTuningArgumentsConfig​

class TIMMFineTuningArgumentsConfig(    model_id: str,    args: Optional[TIMMTrainingConfig] = None,    batch_transformations: Optional[Union[list[Union[str, _JSONDict]], dict[str, list[Union[str, _JSONDict]]]]] = None,    labels: Optional[list[str]] = None,    return_weights: bool = False,    save_path: Optional[Path] = None,):

Configuration for TIMMFineTuning algorithm arguments.

Variables​

  • static batch_transformations : Union[list[Union[str, dict[str, Any]]], dict[str, list[Union[str, dict[str, Any]]]], ForwardRef(None)]
  • static labels : Optional[list[str]]
  • static model_id : str
  • static return_weights : bool

TIMMInferenceAlgorithmConfig​

class TIMMInferenceAlgorithmConfig(    name: str, arguments: Optional[TIMMInferenceArgumentsConfig],):

Configuration for TIMMInference algorithm.

Ancestors​

Variables​

  • static name : str

TIMMInferenceArgumentsConfig​

class TIMMInferenceArgumentsConfig(    model_id: str,    num_classes: Optional[int] = None,    checkpoint_path: Optional[Path] = None,    class_outputs: Optional[list[str]] = None,):

Configuration for TIMMInference algorithm arguments.

Variables​

  • static class_outputs : Optional[list[str]]
  • static model_id : str
  • static num_classes : Optional[int]

TaskConfig​

class TaskConfig(    protocol: Union[ProtocolConfig._get_subclasses()],    algorithm: Union[Union[AlgorithmConfig._get_subclasses()], list[Union[AlgorithmConfig._get_subclasses()]]],    data_structure: DataStructureConfig,    aggregator: Optional[AggregatorConfig] = None,    transformation_file: Optional[Path] = None,):

Configuration for the task.

Variables​

TemplatedModellerConfig​

class TemplatedModellerConfig(    pods: PodsConfig,    task: TaskConfig,    secrets: Optional[Union[APIKeys, JWT]] = None,    modeller: ModellerUserConfig = ModellerUserConfig(username='_default', identity_verification_method='oidc-device-code', private_key_file=None),    hub: HubConfig = HubConfig(url='https://hub.bitfount.com'),    message_service: MessageServiceConfig = MessageServiceConfig(url='messaging.staging.bitfount.com', port=443, tls=True, use_local_storage=False),    version: Optional[str] = None,    project_id: Optional[str] = None,    run_on_new_data_only: bool = False,    batched_execution: Optional[bool] = None,    template: Any = None,):

Schema for task templates.

Ancestors​

Variables​

  • static template : Any

TrialInclusionCriteriaMatchAlgorithmAmethystArgumentsConfig​

class TrialInclusionCriteriaMatchAlgorithmAmethystArgumentsConfig(    cnv_threshold: float = 0.5,    largest_ga_lesion_lower_bound: float = 1.26,    total_ga_area_lower_bound: float = 2.5,    total_ga_area_upper_bound: float = 17.5,):

Configuration for TrialInclusionCriteriaMatchAlgorithmAmethyst arguments.

Variables​

  • static cnv_threshold : float
  • static largest_ga_lesion_lower_bound : float
  • static total_ga_area_lower_bound : float
  • static total_ga_area_upper_bound : float

TrialInclusionCriteriaMatchAlgorithmAmethystConfig​

class TrialInclusionCriteriaMatchAlgorithmAmethystConfig(    name: str,    arguments: Optional[TrialInclusionCriteriaMatchAlgorithmAmethystArgumentsConfig] = TrialInclusionCriteriaMatchAlgorithmAmethystArgumentsConfig(cnv_threshold=0.5, largest_ga_lesion_lower_bound=1.26, total_ga_area_lower_bound=2.5, total_ga_area_upper_bound=17.5),):

Configuration for TrialInclusionCriteriaMatchAlgorithmAmethyst.

Ancestors​

Variables​

  • static name : str

TrialInclusionCriteriaMatchAlgorithmBronzeArgumentsConfig​

class TrialInclusionCriteriaMatchAlgorithmBronzeArgumentsConfig(    cnv_threshold: float = 0.5,    largest_ga_lesion_lower_bound: float = 1.26,    total_ga_area_lower_bound: float = 2.5,    total_ga_area_upper_bound: float = 17.5,    distance_from_fovea_lower_bound: float = 0.0,    distance_from_fovea_upper_bound: float = inf,    exclude_foveal_ga: bool = False,):

Configuration for TrialInclusionCriteriaMatchAlgorithmBronze arguments.

Variables​

  • static cnv_threshold : float
  • static distance_from_fovea_lower_bound : float
  • static distance_from_fovea_upper_bound : float
  • static exclude_foveal_ga : bool
  • static largest_ga_lesion_lower_bound : float
  • static total_ga_area_lower_bound : float
  • static total_ga_area_upper_bound : float

TrialInclusionCriteriaMatchAlgorithmBronzeConfig​

class TrialInclusionCriteriaMatchAlgorithmBronzeConfig(    name: str,    arguments: Optional[TrialInclusionCriteriaMatchAlgorithmBronzeArgumentsConfig] = TrialInclusionCriteriaMatchAlgorithmBronzeArgumentsConfig(cnv_threshold=0.5, largest_ga_lesion_lower_bound=1.26, total_ga_area_lower_bound=2.5, total_ga_area_upper_bound=17.5, distance_from_fovea_lower_bound=0.0, distance_from_fovea_upper_bound=inf, exclude_foveal_ga=False),):

Configuration for TrialInclusionCriteriaMatchAlgorithmBronze.

Ancestors​

Variables​

  • static name : str

TrialInclusionCriteriaMatchAlgorithmJadeArgumentsConfig​

class TrialInclusionCriteriaMatchAlgorithmJadeArgumentsConfig():

Configuration for TrialInclusionCriteriaMatchAlgorithmJade arguments.

TrialInclusionCriteriaMatchAlgorithmJadeConfig​

class TrialInclusionCriteriaMatchAlgorithmJadeConfig(    name: str,    arguments: Optional[TrialInclusionCriteriaMatchAlgorithmJadeArgumentsConfig] = TrialInclusionCriteriaMatchAlgorithmJadeArgumentsConfig(),):

Configuration for TrialInclusionCriteriaMatchAlgorithmJade.

Ancestors​

Variables​

  • static name : str