qualia_core.qualia module

class qualia_core.qualia.TrainResult(name: 'str', i: 'int', model: 'Any', params: 'int', mem_params: 'int', acc: 'float', metrics: 'dict[str, Any]', datamodel: 'DataModel[RawData]', trainset: 'RawData', testset: 'RawData', framework: 'LearningFramework[Any]', batch_size: 'int', optimizer: 'Any', log: 'bool', dataaugmentations: 'list[DataAugmentation]', experimenttracking: 'ExperimentTracking | None')[source]

Bases: object

name: str

i: int

model: Any

params: int

mem_params: int

acc: float

metrics: dict[str, Any]

datamodel: DataModel[RawData]

trainset: RawData

testset: RawData

framework: LearningFramework[Any]

batch_size: int

optimizer: Any

log: bool

dataaugmentations: list[DataAugmentation]

experimenttracking: ExperimentTracking | None

qualia_core.qualia.gen_tag(mname: str, q: str, o: int, i: int, c: int) → str[source]

qualia_core.qualia.instantiate_model(dataset: RawData, framework: LearningFramework[T], model: type[T], model_params: ModelParamsConfigDict | None, model_name: str, iteration: int, load: bool = True) → T[source]

qualia_core.qualia.train(datamodel: RawDataModel, train_epochs: int, iteration: int, framework: LearningFramework[T], model: type[T], model_name: str, model_params: RecursiveConfigDict | None = None, batch_size: int | None = None, optimizer: OptimizerConfigDict | None = None, load: bool = False, train: bool = True, evaluate: bool = True, dataaugmentations: list[DataAugmentation] | None = None, experimenttracking: ExperimentTracking | None = None, use_test_as_valid: bool = False) → TrainResult[source]

qualia_core.qualia.prepare_deploy(datamodel, model_kind, model_name, model, framework, iteration, deploy_target, quantize='float32', optimize=None, compress=1, tag='main', converter=None, converter_params={}, deployers=None, deployer_params={}, representative_dataset=None)[source]

qualia_core.qualia.deploy(model_kind, deploy_target, tag='main', deployers=None, deployer_params={})[source]

qualia_core.qualia.evaluate(datamodel, model_kind, model_name, model, framework, iteration, target, quantization, fmem_params, tag, limit=None, evaluator=None, evaluator_params={}, dataaugmentations=None)[source]