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types

Useful types for Federated Learning.

Module​

Functions​

get_task_results_directory​

def get_task_results_directory(context: Optional[ProtocolContext]) ‑> pathlib.Path:

Return the path to the task results directory based on the provided context.

If context is provided, use the derived task results directory from this. Otherwise, default to the configured OUTPUT_DIR.

Classes​

AccessCheckResult​

class AccessCheckResult(*args, **kwargs):

Container for the result of the access manager check.

Ancestors​

  • builtins.dict

Variables​

  • static limits : Optional[list[_AMResourceUsageAndLimitJSON]]
  • static response_type : _PodResponseType

AggregatorType​

class AggregatorType(    value, names=None, *, module=None, qualname=None, type=None, start=1,):

Available aggregator names from bitfount.federated.aggregator.

Ancestors​

Variables​

  • static Aggregator
  • static SecureAggregator

AlgorithmType​

class AlgorithmType(value, names=None, *, module=None, qualname=None, type=None, start=1):

Available algorithm names from bitfount.federated.algorithm.

Ancestors​

Variables​

  • static BscanImageAndMaskGenerationAlgorithm
  • static CSVReportAlgorithm
  • static CSVReportGeneratorAlgorithm
  • static CSVReportGeneratorOphthalmologyAlgorithm
  • static EHRPatientQueryAlgorithm
  • static ETDRSAlgorithm
  • static FederatedModelTraining
  • static FluidVolumeCalculationAlgorithm
  • static FoveaCoordinatesAlgorithm
  • static GATrialCalculationAlgorithm
  • static GATrialCalculationAlgorithmAmethyst
  • static GATrialCalculationAlgorithmBronze
  • static GATrialCalculationAlgorithmCharcoal
  • static GATrialCalculationAlgorithmJade
  • static GATrialPDFGeneratorAlgorithm
  • static GATrialPDFGeneratorAlgorithmAmethyst
  • static GATrialPDFGeneratorAlgorithmJade
  • static HuggingFaceImageClassificationInference
  • static HuggingFaceImageSegmentationInference
  • static HuggingFacePerplexityEvaluation
  • static HuggingFaceTextClassificationInference
  • static HuggingFaceTextGenerationInference
  • static ImageSelectionAlgorithm
  • static ModelEvaluation
  • static ModelInference
  • static ModelTrainingAndEvaluation
  • static NextGenPatientInfoDownloadAlgorithm
  • static PrivateSqlQuery
  • static RecordFilterAlgorithm
  • static ReduceCSVAlgorithmCharcoal
  • static S3UploadAlgorithm
  • static SqlQuery
  • static TIMMFineTuning
  • static TIMMInference
  • static TrialInclusionCriteriaMatchAlgorithm
  • static TrialInclusionCriteriaMatchAlgorithmAmethyst
  • static TrialInclusionCriteriaMatchAlgorithmBronze
  • static TrialInclusionCriteriaMatchAlgorithmCharcoal
  • static TrialInclusionCriteriaMatchAlgorithmJade

DatasourceContainer​

class DatasourceContainer(    name: str,    datasource: BaseSource,    datasource_details: PodDetailsConfig,    data_config: PodDataConfig,    schema: BitfountSchema,):

Contains a datasource and all the data related to it.

This represents a datasource configuration post-data-loading/configuration and so the data config and schema must be present.

Variables​

  • static data_config : PodDataConfig
  • static datasource : BaseSource
  • static datasource_details : PodDetailsConfig
  • static name : str
  • static schema : BitfountSchema

DatasourceContainerConfig​

class DatasourceContainerConfig(    name: str,    datasource: BaseSource,    datasource_details: Optional[PodDetailsConfig] = None,    data_config: Optional[PodDataConfig] = None,    schema: Optional[Union[str, os.PathLike, BitfountSchema]] = None,):

Contains a datasource and maybe some data related to it.

This represents a datasource configuration pre-data-loading/configuration and so the data config and schema are not required.

Variables​

  • static data_config : Optional[PodDataConfig]
  • static datasource : BaseSource
  • static datasource_details : Optional[PodDetailsConfig]
  • static name : str
  • static schema : Optional[Union[str, os.PathLike, BitfountSchema]]

EHRConfig​

class EHRConfig(    base_url: str,    provider: EHRProvider,    enterprise_url: Optional[str] = None,    smart_on_fhir_url: Optional[str] = None,    smart_on_fhir_resource_server_url: Optional[str] = None,):

Configuration for EHR details.

Ancestors​

  • bitfount.types.UsedForConfigSchemas

Variables​

  • static base_url : str
  • static enterprise_url : Optional[str]
  • static provider : Literal['nextgen enterprise', 'nextech intellechartpro r4', 'smarthealthit r4', 'epic r4']
  • static smart_on_fhir_resource_server_url : Optional[str]
  • static smart_on_fhir_url : Optional[str]

HubConfig​

class HubConfig(    username: Optional[str],    secrets: Optional[Union[APIKeys, ExternallyManagedJWT]],    session: Optional[BitfountSession] = None,    session_info: Optional[dict] = None,):

Configuration for connecting to Bitfount Hub.

Variables​

  • static secrets : Optional[Union[APIKeys, ExternallyManagedJWT]]
  • static session : Optional[BitfountSession]
  • static session_info : Optional[dict]
  • static username : Optional[str]

HuggingFaceImageClassificationInferenceDictionary​

class HuggingFaceImageClassificationInferenceDictionary(*args, **kwargs):

Hugging Face dictionary response for image classification.

Ancestors​

  • builtins.dict

Variables​

  • static image_classification : str

InferenceLimits​

class InferenceLimits(*, limit: int, total_usage: int):

Container class for model inference usage limits.

Attributes

  • limit: The total number of inferences that can be performed.
  • total_usage: The total number of inferences performed so far.

Variables​

  • static limit : int
  • static total_usage : int
  • initial_total_usage : int - Returns the total usage that was set when this instance was created.

Static methods​

from_access_check_result​

def from_access_check_result(access_check_result: AccessCheckResult) ‑> dict:

Construct model name -> inference usage limits from access check results.

If a model usage limit is undefined or not present, this indicates that there is no usage limit, and so we do not add this to the constructed dict.

Returns Dictionary of full model name (e.g. "some-model-owner/some-model:12" to the model inference usage limits for that model as detailed in access_check_results).

MinimalDatasourceConfig​

class MinimalDatasourceConfig(    datasource_cls_name: str,    name: str,    datasource_args: _JSONDict,    file_system_filters: Optional[FileSystemFilterConfig],    data_split: Optional[DataSplitConfig],):

Minimal serializable configuration required for creating a datasource.

Variables​

  • static data_split : Optional[DataSplitConfig]
  • static datasource_args : _JSONDict
  • static datasource_cls_name : str
  • static file_system_filters : Optional[FileSystemFilterConfig]
  • static name : str

MinimalSchemaGenerationConfig​

class MinimalSchemaGenerationConfig(    datasource_name: str,    description: Optional[str],    column_descriptions: Optional[Union[Mapping[str, Mapping[str, str]], Mapping[str, str]]],    ignore_cols: Optional[list[str]],    force_stypes: "Optional[MutableMapping[Literal['categorical', 'continuous', 'image', 'text', 'image_prefix'], list[str]]]",):

Minimal serializable configuration required for creating a schema.

Variables​

  • static column_descriptions : Union[Mapping[str, Mapping[str, str]], Mapping[str, str], ForwardRef(None)]
  • static datasource_name : str
  • static description : Optional[str]
  • static force_stypes : Optional[MutableMapping[Literal['categorical', 'continuous', 'image', 'text', 'image_prefix'], list[str]]]
  • static ignore_cols : Optional[list]

MinimalSchemaUploadConfig​

class MinimalSchemaUploadConfig(    public_metadata: PodPublicMetadata,    access_manager_public_key: RSAPublicKey,    pod_public_key: RSAPublicKey,):

Minimal serializable configuration required for uploading a schema.

Variables​

  • static access_manager_public_key : RSAPublicKey
  • static pod_public_key : RSAPublicKey
  • static public_metadata : PodPublicMetadata

ModelURLs​

class ModelURLs(    *, model_download_url: _S3PresignedURL, model_weights_url: _S3PresignedURL | None,):

Container class for model download URLs from the authorisation checker.

Attributes

  • model_download_url: URL for downloading the model code.
  • model_weights_url: URL for downloading the model weights.

Variables​

  • static model_download_url : bitfount.types._S3PresignedURL
  • static model_weights_url : Optional[bitfount.types._S3PresignedURL]

Static methods​

from_access_check_result​

def from_access_check_result(access_check_result: AccessCheckResult) ‑> dict:

Construct model name -> model download URLs from access check results.

Returns Dictionary of full model name/id (e.g. "some-model-owner/some-model:12" to the model download URL and/or model weights URL for that model as detailed in access_check_results).

ProtocolContext​

class ProtocolContext(    *,    inference_limits: dict[str, InferenceLimits] = {},    model_urls: dict[str, ModelURLs] = {},    task_context: Optional[TaskContext] = None,    project_id: Optional[str] = None,    task_id: Optional[str] = None,):

Details needed for the protocol at runtime.

Attributes

  • inference_limits: A mapping of model name (full name, including owner and version) to the inference limits information for that model. e.g. {"some-model-owner/some-model:12": {"limit": 90, "total_usage": 10}}
  • model_urls: A mapping of model name (full name, including owner and version) to any URLs needed to download the model/weights in the context of the task.
  • task_context: Optional. Which context (modeller or worker) the task is running in.
  • project_id: Optional. The ID of the project this task is part of.
  • task_id: Optional. The ID of the task this context is for.

Variables​

  • static inference_limits : dict
  • static model_urls : dict
  • static project_id : Optional[str]
  • static task_id : Optional[str]

Methods​

get_task_results_dir​

def get_task_results_dir(self) ‑> pathlib.Path:

Get the directory where task results should be stored for this task run.

If TASK_RESULTS_DIR is set, that is used as the base. Otherwise, the base will be OUTPUT_DIR/"task-results".

Within that directory, create a subdirectories named after the project ID, and task ID, if provided.

ProtocolType​

class ProtocolType(value, names=None, *, module=None, qualname=None, type=None, start=1):

Available protocol names from bitfount.federated.protocol.

Ancestors​

Variables​

  • static BasicOCTProtocol
  • static DataExtractionProtocolCharcoal
  • static FederatedAveraging
  • static FluidVolumeScreeningProtocol
  • static GAScreeningProtocol
  • static GAScreeningProtocolAmethyst
  • static GAScreeningProtocolBronze
  • static GAScreeningProtocolBronzeWithEHR
  • static GAScreeningProtocolCharcoal
  • static GAScreeningProtocolJade
  • static InSiteInsightsProtocol
  • static InferenceAndCSVReport
  • static InferenceAndImageOutput
  • static InferenceAndReturnCSVReport
  • static InstrumentedInferenceAndCSVReport
  • static NextGenSearchProtocol
  • static ResultsOnly
  • static RetinalDiseaseProtocolCobalt

SerializedAggregator​

class SerializedAggregator(*args, **kwargs):

Serialized representation of an aggregator.

Ancestors​

  • builtins.dict

Variables​

  • static class_name : str

SerializedAlgorithm​

class SerializedAlgorithm(*args, **kwargs):

Serialized representation of an algorithm.

Ancestors​

  • builtins.dict

Variables​

  • static class_name : str

SerializedDataStructure​

class SerializedDataStructure(*args, **kwargs):

Serialized representation of a data structure.

Ancestors​

  • builtins.dict

Variables​

  • static compatible_datasources : typing_extensions.NotRequired[list[str]]
  • static schema_requirements : typing_extensions.NotRequired[dict[str, typing.Any]]
  • static table : typing_extensions.NotRequired[typing.Union[str, dict[str, str]]]

SerializedModel​

class SerializedModel(*args, **kwargs):

Serialized representation of a model.

Ancestors​

  • builtins.dict

Variables​

  • static class_name : str
  • static hub : NotRequired[Optional[BitfountHub]]
  • static schema : NotRequired[_StrAnyDict]

SerializedProtocol​

class SerializedProtocol(*args, **kwargs):

Serialized representation of a protocol.

Ancestors​

  • builtins.dict

Variables​

  • static class_name : str
  • static primary_results_path : typing_extensions.NotRequired[str]

TaskContext​

class TaskContext(value, names=None, *, module=None, qualname=None, type=None, start=1):

Describes the context (modeller or worker) in which the task is running.

This is used for models where the model differs depending on if it is on the modeller-side or worker-side of the federated process. It is also used for batched execution.

Ancestors​

Variables​

  • static MODELLER
  • static WORKER

TextGenerationDictionary​

class TextGenerationDictionary(*args, **kwargs):

Hugging Face dictionary response for text generation.

Ancestors​

  • builtins.dict

Variables​

  • static generated_text : str