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.
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.
AlgorithmConfig
class AlgorithmConfig(name: str, arguments: Optional[Any] = None):
Configuration for the Algorithm.
Subclasses
- CSVReportAlgorithmConfig
- ColumnAverageAlgorithmConfig
- ComputeIntersectionRSAAlgorithmConfig
- GenericAlgorithmConfig
- HuggingFaceImageClassificationInferenceAlgorithmConfig
- HuggingFaceImageSegmentationInferenceAlgorithmConfig
- HuggingFacePerplexityEvaluationAlgorithmConfig
- HuggingFaceTextClassificationInferenceAlgorithmConfig
- HuggingFaceTextGenerationInferenceAlgorithmConfig
- HuggingFaceZeroShotImageClassificationInferenceAlgorithmConfig
- ModelAlgorithmConfig
- PrivateSqlQueryAlgorithmConfig
- SqlQueryAlgorithmConfig
- TIMMFineTuningAlgorithmConfig
- TIMMInferenceAlgorithmConfig
BitfountModelReferenceConfig
class BitfountModelReferenceConfig( model_ref: Union[Path, str], model_version: Optional[int] = None, username: Optional[str] = None,):
Configuration for BitfountModelReference.
Variables
- static
model_ref : Union[pathlib.Path, str]
- static
model_version : Optional[int]
- static
username : 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
filter : Optional[List[ColumnFilter]]
- static
original_cols : Optional[List[str]]
- static
save_path : Optional[pathlib.Path]
CSVReportAlgorithmConfig
class CSVReportAlgorithmConfig( name: str, arguments: Optional[CSVReportAlgorithmArgumentsConfig] = CSVReportAlgorithmArgumentsConfig(save_path=None, original_cols=None, filter=None),):
Configuration for CSVReportAlgorithm.
Ancestors
ColumnAverageAlgorithmConfig
class ColumnAverageAlgorithmConfig(name: str, arguments: ColumnAverageArgumentsConfig):
Configuration for the ColumnAverage algorithm.
Ancestors
ColumnAverageArgumentsConfig
class ColumnAverageArgumentsConfig(field: str, table_name: str):
Configuration for the ColumnAverage algorithm arguments.
ComputeIntersectionRSAAlgorithmConfig
class ComputeIntersectionRSAAlgorithmConfig( name: str, arguments: Optional[ComputeIntersectionRSAArgumentsConfig],):
Configuration for the ComputeIntersectionRSA algorithm.
Ancestors
ComputeIntersectionRSAArgumentsConfig
class ComputeIntersectionRSAArgumentsConfig():
Configuration for the ComputeIntersectionRSA algorithm arguments.
ConversationArgumentsConfig
class ConversationArgumentsConfig():
Configuration for the Conversation arguments.
ConversationConfig
class ConversationConfig( name: str, arguments: Optional[ConversationArgumentsConfig] = ConversationArgumentsConfig(),):
Configuration for Conversation.
Ancestors
DataSplitConfig
class DataSplitConfig(data_splitter: str = 'percentage', args: _JSONDict = {}):
Configuration for the data splitter.
DataStructureAssignConfig
class DataStructureAssignConfig( target: Optional[Union[str, List[str]]] = None, image_cols: Optional[List[str]] = None, image_prefix: Optional[str] = None, loss_weights_col: Optional[str] = None, multihead_col: Optional[str] = None, ignore_classes_col: Optional[str] = None,):
Configuration for the datastructure assign argument.
Variables
- static
ignore_classes_col : Optional[str]
- static
image_cols : Optional[List[str]]
- static
image_prefix : Optional[str]
- static
loss_weights_col : Optional[str]
- static
multihead_col : Optional[str]
- static
target : Union[str, List[str], ForwardRef(None)]
DataStructureConfig
class DataStructureConfig( table_config: DataStructureTableConfig, assign: DataStructureAssignConfig = DataStructureAssignConfig(target=None, image_cols=None, image_prefix=None, loss_weights_col=None, multihead_col=None, ignore_classes_col=None), select: DataStructureSelectConfig = DataStructureSelectConfig(include=[], include_prefix=None, exclude=[]), transform: DataStructureTransformConfig = DataStructureTransformConfig(dataset=None, batch=None, image=None, auto_convert_grayscale_images=True), data_split: Optional[DataSplitConfig] = None,):
Configuration for the modeller schema and dataset options.
Variables
- static
assign : DataStructureAssignConfig
- static
data_split : Optional[DataSplitConfig]
- static
select : DataStructureSelectConfig
- static
table_config : DataStructureTableConfig
- static
transform : DataStructureTransformConfig
DataStructureSelectConfig
class DataStructureSelectConfig( include: List[str] = [], include_prefix: Optional[str] = None, exclude: List[str] = [],):
Configuration for the datastructure select argument.
Variables
- static
exclude : List[str]
- static
include : List[str]
- static
include_prefix : Optional[str]
DataStructureTableConfig
class DataStructureTableConfig( table: Optional[Union[str, Dict[str, str]]] = None, query: Optional[Union[str, Dict[str, str]]] = None, schema_types_override: Optional[Union[SchemaOverrideMapping, Mapping[str, SchemaOverrideMapping]]] = None,):
Configuration for the datastructure table arguments.
Variables
- static
query : Union[str, Dict[str, str], ForwardRef(None)]
- static
schema_types_override : Union[Mapping[Literal['categorical', 'continuous', 'image', 'text'], List[Union[str, Mapping[str, Mapping[str, int]]]]], Mapping[str, Mapping[Literal['categorical', 'continuous', 'image', 'text'], List[Union[str, Mapping[str, Mapping[str, int]]]]]], ForwardRef(None)]
- static
table : Union[str, Dict[str, str], ForwardRef(None)]
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, Any]]]]
- static
dataset : Optional[List[Dict[str, Dict[str, Any]]]]
- static
image : Optional[List[Dict[str, Dict[str, Any]]]]
DatasourceConfig
class DatasourceConfig( datasource: str, data_config: PodDataConfig = PodDataConfig(force_stypes=None, column_descriptions=None, table_descriptions=None, ignore_cols=None, modifiers=None, datasource_args={}, data_split=DataSplitConfig(data_splitter='percentage', args={}), auto_tidy=False), name: Optional[str] = None, datasource_details_config: Optional[PodDetailsConfig] = None, schema: Optional[Path] = None,):
Datasource configuration for a multi-datasource Pod.
Variables
- static
data_config : PodDataConfig
- static
datasource : str
- static
datasource_details_config : Optional[PodDetailsConfig]
- static
name : Optional[str]
- static
schema : Optional[pathlib.Path]
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
aggregator : Optional[AggregatorConfig]
- 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
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
FederatedModelTrainingArgumentsConfig
class FederatedModelTrainingArgumentsConfig( modeller_checkpointing: bool = True, checkpoint_filename: Optional[str] = None,):
Configuration for the FederatedModelTraining algorithm arguments.
GenericAlgorithmConfig
class GenericAlgorithmConfig(name: str, arguments: _JSONDict = {}):
Configuration for unspecified algorithm plugins.
Raises
ValueError
: if the algorithm name starts withbitfount.
Ancestors
GenericProtocolConfig
class GenericProtocolConfig(name: str, arguments: _JSONDict = {}):
Configuration for unspecified protocol plugins.
Raises
ValueError
: if the protocol name starts withbitfount.
Ancestors
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
arguments : Optional[HuggingFaceImageClassificationInferenceArgumentsConfig]
- static
name : str
HuggingFaceImageClassificationInferenceArgumentsConfig
class HuggingFaceImageClassificationInferenceArgumentsConfig( model_id: str, image_column_name: 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
image_column_name : str
- 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
arguments : Optional[HuggingFaceImageSegmentationInferenceArgumentsConfig]
- static
name : str
HuggingFaceImageSegmentationInferenceArgumentsConfig
class HuggingFaceImageSegmentationInferenceArgumentsConfig( model_id: str, image_column_name: 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
image_column_name : str
- 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
arguments : Optional[HuggingFacePerplexityEvaluationArgumentsConfig]
- static
name : str
HuggingFacePerplexityEvaluationArgumentsConfig
class HuggingFacePerplexityEvaluationArgumentsConfig( model_id: str, text_column_name: str, stride: int = 512, seed: int = 42,):
Configuration for the HuggingFacePerplexityEvaluation algorithm arguments.
Variables
- static
model_id : str
- static
seed : int
- static
stride : int
- static
text_column_name : str
HuggingFaceTextClassificationInferenceAlgorithmConfig
class HuggingFaceTextClassificationInferenceAlgorithmConfig( name: str, arguments: Optional[HuggingFaceTextClassificationInferenceArgumentsConfig],):
Configuration for HuggingFaceTextClassificationInference.
Ancestors
Variables
- static
arguments : Optional[HuggingFaceTextClassificationInferenceArgumentsConfig]
- static
name : str
HuggingFaceTextClassificationInferenceArgumentsConfig
class HuggingFaceTextClassificationInferenceArgumentsConfig( model_id: str, target_column_name: 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
target_column_name : str
- static
top_k : int
HuggingFaceTextGenerationInferenceAlgorithmConfig
class HuggingFaceTextGenerationInferenceAlgorithmConfig( name: str, arguments: Optional[HuggingFaceTextGenerationInferenceArgumentsConfig],):
Configuration for the HuggingFaceTextGenerationInference algorithm.
Ancestors
Variables
- static
arguments : Optional[HuggingFaceTextGenerationInferenceArgumentsConfig]
- static
name : str
HuggingFaceTextGenerationInferenceArgumentsConfig
class HuggingFaceTextGenerationInferenceArgumentsConfig( model_id: str, text_column_name: Optional[str] = None, 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
text_column_name : Optional[str]
- static
torch_dtype : str
HuggingFaceZeroShotImageClassificationInferenceAlgorithmConfig
class HuggingFaceZeroShotImageClassificationInferenceAlgorithmConfig( name: str, arguments: Optional[HuggingFaceZeroShotImageClassificationInferenceArgumentsConfig],):
Configuration for HuggingFaceZeroShotImageClassificationInference.
Ancestors
Variables
- static
arguments : Optional[HuggingFaceZeroShotImageClassificationInferenceArgumentsConfig]
- static
name : str
HuggingFaceZeroShotImageClassificationInferenceArgumentsConfig
class HuggingFaceZeroShotImageClassificationInferenceArgumentsConfig( model_id: str, image_column_name: str, candidate_labels: List[str], batch_size: int = 1, class_outputs: Optional[List[str]] = None, hypothesis_template: Optional[str] = None, seed: int = 42,):
Configuration for HuggingFaceZeroShotImageClassificationInference arguments.
Variables
- static
batch_size : int
- static
candidate_labels : List[str]
- static
class_outputs : Optional[List[str]]
- static
hypothesis_template : Optional[str]
- static
image_column_name : str
- static
model_id : str
- static
seed : int
InferenceAndCSVReportArgumentsConfig
class InferenceAndCSVReportArgumentsConfig(aggregator: Optional[AggregatorConfig] = None):
Configuration for InferenceAndCSVReport arguments.
Variables
- static
aggregator : Optional[AggregatorConfig]
InferenceAndCSVReportConfig
class InferenceAndCSVReportConfig( name: str, arguments: Optional[InferenceAndCSVReportArgumentsConfig] = InferenceAndCSVReportArgumentsConfig(aggregator=None),):
Configuration for InferenceAndCSVReport.
Ancestors
InstrumentedInferenceAndCSVReportArgumentsConfig
class InstrumentedInferenceAndCSVReportArgumentsConfig( aggregator: Optional[AggregatorConfig] = None,):
Configuration for InstrumentedInferenceAndCSVReport arguments.
Variables
- static
aggregator : Optional[AggregatorConfig]
InstrumentedInferenceAndCSVReportConfig
class InstrumentedInferenceAndCSVReportConfig( name: str, arguments: Optional[InstrumentedInferenceAndCSVReportArgumentsConfig] = InstrumentedInferenceAndCSVReportArgumentsConfig(aggregator=None),):
Configuration for InstrumentedInferenceAndCSVReport.
Ancestors
Variables
- static
arguments : Optional[InstrumentedInferenceAndCSVReportArgumentsConfig]
- static
name : str
JWT
class JWT(jwt: str, expires: datetime, get_token: Callable[[], Tuple[str, datetime]]):
Externally managed JWT for BitfountSession.
Variables
- static
expires : datetime.datetime
- static
get_token : Callable[[], Tuple[str, datetime.datetime]]
- 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
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
bitfount_model : Optional[BitfountModelReferenceConfig]
- static
dp_config : Optional[DPModellerConfig]
- static
hyperparameters : Dict[str, Any]
- static
logger_config : Optional[LoggerConfig]
- static
name : Optional[str]
- static
structure : Optional[ModelStructureConfig]
ModelEvaluationAlgorithmConfig
class ModelEvaluationAlgorithmConfig( name: str, arguments: Optional[ModelEvaluationArgumentsConfig], model: Optional[ModelConfig] = None, pretrained_file: Optional[Path] = None,):
Configuration for the ModelEvaluation algorithm.
Ancestors
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
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.
ModelTrainingAndEvaluationAlgorithmConfig
class ModelTrainingAndEvaluationAlgorithmConfig( name: str, arguments: Optional[ModelTrainingAndEvaluationArgumentsConfig], model: Optional[ModelConfig] = None, pretrained_file: Optional[Path] = None,):
Configuration for the ModelTrainingAndEvaluation algorithm.
Ancestors
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.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.
Variables
- static
batched_execution : Optional[bool]
- static
hub : HubConfig
- static
message_service : MessageServiceConfig
- static
modeller : ModellerUserConfig
- static
pods : PodsConfig
- static
project_id : Optional[str]
- static
run_on_new_data_only : bool
- static
task : TaskConfig
- 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 inIDENTITY_VERIFICATION_METHODS
, i.e. one ofkey-based
,saml
,oidc-auth-code
oroidc-device-code
.private_key_file
: The path to the private key file for key-based identity verification.
Variables
- static
identity_verification_method : str
- static
private_key_file : Optional[pathlib.Path]
- static
username : str
PathConfig
class PathConfig(path: Path):
Configuration for the path.
Variables
- static
path : pathlib.Path
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.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
access_manager : AccessManagerConfig
- static
approved_pods : Optional[List[str]]
- static
data_config : Optional[PodDataConfig]
- static
datasource : Optional[str]
- static
datasources : Optional[List[DatasourceConfig]]
- static
differential_privacy : Optional[DPPodConfig]
- static
hub : HubConfig
- static
message_service : MessageServiceConfig
- static
name : str
- static
pod_db : Union[bool, PodDbConfig]
- static
pod_details_config : Optional[PodDetailsConfig]
- static
schema : Optional[pathlib.Path]
- 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[Mapping[str, MutableMapping[Union[_ForceStypeValue, _SemanticTypeValue], List[str]]]] = None, column_descriptions: Optional[Mapping[str, Mapping[str, str]]] = None, table_descriptions: Optional[Mapping[str, str]] = None, ignore_cols: Optional[Mapping[str, List[str]]] = None, modifiers: Optional[Dict[str, DataPathModifiers]] = None, datasource_args: _JSONDict = {}, data_split: DataSplitConfig = DataSplitConfig(data_splitter='percentage', args={}), auto_tidy: bool = False,):
Configuration for the Schema, BaseSource and Pod.
Arguments
force_stypes
: The semantic types to force for the data. This is passed to theBitfountSchema
. This should be a mapping from pod name to a mapping from the type to a list of column names (e.g.{"pod_name": {categorical: ["col1", "col2"]}}
), or a mapping from each table name in a multitable datasource to a mapping from the type to a list of column names (e.g.{"table1": {categorical: ["col1", "col2"]}, "table2": {continuous: ["col3", "col4"]}}
).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 theBaseSource
.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 thePod
and will result in removal of NaNs and normalisation of numeric values. Defaults to False.
Variables
- static
auto_tidy : bool
- static
column_descriptions : Optional[Mapping[str, Mapping[str, str]]]
- static
data_split : DataSplitConfig
- static
datasource_args : Dict[str, Any]
- static
force_stypes : Optional[Mapping[str, MutableMapping[Union[Literal['categorical', 'continuous', 'image', 'text'], Literal['categorical', 'continuous', 'image', 'text', 'image_prefix']], List[str]]]]
- static
ignore_cols : Optional[Mapping[str, List[str]]]
- static
modifiers : Optional[Dict[str, DataPathModifiers]]
- static
table_descriptions : Optional[Mapping[str, str]]
PodDbConfig
class PodDbConfig(path: Path):
Configuration of the Pod DB.
Variables
- static
path : pathlib.Path
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.
PodsConfig
class PodsConfig(identifiers: List[str]):
Configuration for the pods to use for the modeller.
Variables
- static
identifiers : List[str]
PrivateSetIntersectionArgumentsConfig
class PrivateSetIntersectionArgumentsConfig( datasource: Optional[DatasourceConfig] = None, datasource_columns: Optional[List[str]] = None, datasource_table: Optional[str] = None, pod_columns: Optional[List[str]] = None, pod_table: Optional[str] = None, aggregator: Optional[AggregatorConfig] = None,):
Configuration for the PSI Protocol arguments.
Variables
- static
aggregator : Optional[AggregatorConfig]
- static
datasource : Optional[DatasourceConfig]
- static
datasource_columns : Optional[List[str]]
- static
datasource_table : Optional[str]
- static
pod_columns : Optional[List[str]]
- static
pod_table : Optional[str]
PrivateSetIntersectionProtocolConfig
class PrivateSetIntersectionProtocolConfig( name: str, arguments: Optional[PrivateSetIntersectionArgumentsConfig] = PrivateSetIntersectionArgumentsConfig(datasource=None, datasource_columns=None, datasource_table=None, pod_columns=None, pod_table=None, aggregator=None),):
Configuration for the PSI Protocol.
Ancestors
PrivateSqlQueryAlgorithmConfig
class PrivateSqlQueryAlgorithmConfig( name: str, arguments: PrivateSqlQueryArgumentsConfig,):
Configuration for the PrivateSqlQuery algorithm.
Ancestors
PrivateSqlQueryArgumentsConfig
class PrivateSqlQueryArgumentsConfig( query: str, epsilon: float, delta: float, column_ranges: Dict[str, Dict[str, int]], table: Optional[str] = None, db_schema: Optional[str] = None,):
Configuration for the PrivateSqlQuery algorithm arguments.
Variables
- static
column_ranges : Dict[str, Dict[str, int]]
- static
db_schema : Optional[str]
- static
delta : float
- static
epsilon : float
- static
query : str
- static
table : Optional[str]
ProtocolConfig
class ProtocolConfig(name: str, arguments: Optional[Any] = None):
Configuration for the Protocol.
Subclasses
ResultsOnlyProtocolArgumentsConfig
class ResultsOnlyProtocolArgumentsConfig( aggregator: Optional[AggregatorConfig] = None, secure_aggregation: bool = False,):
Configuration for the ResultsOnly Protocol arguments.
ResultsOnlyProtocolConfig
class ResultsOnlyProtocolConfig( name: str, arguments: Optional[ResultsOnlyProtocolArgumentsConfig] = ResultsOnlyProtocolArgumentsConfig(aggregator=None, secure_aggregation=False),):
Configuration for the ResultsOnly Protocol.
Ancestors
SqlQueryAlgorithmConfig
class SqlQueryAlgorithmConfig(name: str, arguments: SqlQueryArgumentsConfig):
Configuration for the SqlQuery algorithm.
Ancestors
SqlQueryArgumentsConfig
class SqlQueryArgumentsConfig(query: str, table: Optional[str] = None):
Configuration for the SqlQuery algorithm arguments.
TIMMFineTuningAlgorithmConfig
class TIMMFineTuningAlgorithmConfig( name: str, arguments: Optional[TIMMFineTuningArgumentsConfig],):
Configuration for TIMMFineTuning algorithm.
Ancestors
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, image_column_name: Optional[str] = None, labels: Optional[List[str]] = None, return_weights: bool = False, save_path: Optional[Path] = None, target_column_name: Optional[str] = None,):
Configuration for TIMMFineTuning algorithm arguments.
Variables
- static
args : Optional[TIMMTrainingConfig]
- static
batch_transformations : Union[List[Union[str, Dict[str, Any]]], Dict[str, List[Union[str, Dict[str, Any]]]], ForwardRef(None)]
- static
image_column_name : Optional[str]
- static
labels : Optional[List[str]]
- static
model_id : str
- static
return_weights : bool
- static
save_path : Optional[pathlib.Path]
- static
target_column_name : Optional[str]
TIMMInferenceAlgorithmConfig
class TIMMInferenceAlgorithmConfig( name: str, arguments: Optional[TIMMInferenceArgumentsConfig],):
Configuration for TIMMInference algorithm.
Ancestors
TIMMInferenceArgumentsConfig
class TIMMInferenceArgumentsConfig( model_id: str, image_column_name: str, num_classes: Optional[int] = None, checkpoint_path: Optional[Path] = None, class_outputs: Optional[List[str]] = None,):
Configuration for TIMMInference algorithm arguments.
Variables
- static
checkpoint_path : Optional[pathlib.Path]
- static
class_outputs : Optional[List[str]]
- static
image_column_name : 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: Optional[DataStructureConfig] = None, aggregator: Optional[AggregatorConfig] = None, transformation_file: Optional[Path] = None,):
Configuration for the task.
Variables
- static
aggregator : Optional[AggregatorConfig]
- static
algorithm : Union[HuggingFaceZeroShotImageClassificationInferenceAlgorithmConfig, TIMMInferenceAlgorithmConfig, TIMMFineTuningAlgorithmConfig, HuggingFaceTextClassificationInferenceAlgorithmConfig, HuggingFaceImageSegmentationInferenceAlgorithmConfig, HuggingFaceImageClassificationInferenceAlgorithmConfig, CSVReportAlgorithmConfig, HuggingFaceTextGenerationInferenceAlgorithmConfig, HuggingFacePerplexityEvaluationAlgorithmConfig, ComputeIntersectionRSAAlgorithmConfig, PrivateSqlQueryAlgorithmConfig, SqlQueryAlgorithmConfig, ColumnAverageAlgorithmConfig, ModelInferenceAlgorithmConfig, ModelEvaluationAlgorithmConfig, ModelTrainingAndEvaluationAlgorithmConfig, FederatedModelTrainingAlgorithmConfig, GenericAlgorithmConfig, List[Union[HuggingFaceZeroShotImageClassificationInferenceAlgorithmConfig, TIMMInferenceAlgorithmConfig, TIMMFineTuningAlgorithmConfig, HuggingFaceTextClassificationInferenceAlgorithmConfig, HuggingFaceImageSegmentationInferenceAlgorithmConfig, HuggingFaceImageClassificationInferenceAlgorithmConfig, CSVReportAlgorithmConfig, HuggingFaceTextGenerationInferenceAlgorithmConfig, HuggingFacePerplexityEvaluationAlgorithmConfig, ComputeIntersectionRSAAlgorithmConfig, PrivateSqlQueryAlgorithmConfig, SqlQueryAlgorithmConfig, ColumnAverageAlgorithmConfig, ModelInferenceAlgorithmConfig, ModelEvaluationAlgorithmConfig, ModelTrainingAndEvaluationAlgorithmConfig, FederatedModelTrainingAlgorithmConfig, GenericAlgorithmConfig]]]
- static
data_structure : Optional[DataStructureConfig]
- static
protocol : Union[ConversationConfig, InstrumentedInferenceAndCSVReportConfig, InferenceAndCSVReportConfig, FederatedAveragingProtocolConfig, ResultsOnlyProtocolConfig, PrivateSetIntersectionProtocolConfig, GenericProtocolConfig]
- static
transformation_file : Optional[pathlib.Path]