optimize_config module

OptimizeConfig

class edgeimpulse_api.models.optimize_config.OptimizeConfig(
		**data: Any
)

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Parameters

• **data: Any

Bases

• pydantic.main.BaseModel

• pydantic.utils.Representation

Class variables

• Config

• accuracy_sem: float | None

• compiler: List[pydantic.types.StrictStr] | None

• disable_constraints: pydantic.types.StrictBool | None

• disable_deduplicate: pydantic.types.StrictBool | None

• early_stopping: pydantic.types.StrictBool | None

• early_stopping_improvement_bar: float | None

• early_stopping_window_size: float | None

• enable_sem: pydantic.types.StrictBool | None

• import_project_metrics: pydantic.types.StrictBool | None

• import_resource_metrics: pydantic.types.StrictBool | None

• initial_trials: pydantic.types.StrictInt | None

• latency_sem: float | None

• max_maccs: float | None

• min_maccs: float | None

• momf: pydantic.types.StrictBool | None

• name: pydantic.types.StrictStr | None

• notification_on_completion: pydantic.types.StrictBool | None

• num_import_project_metrics: float | None

• num_import_resource_metrics: float | None

• optimization_objectives: List[edgeimpulse_api.models.optimize_config_optimization_objectives_inner.OptimizeConfigOptimizationObjectivesInner] | None

• optimization_precision: pydantic.types.StrictStr | None

• optimization_rounds: pydantic.types.StrictInt | None

• precision: List[pydantic.types.StrictStr] | None

• raw_objectives: pydantic.types.StrictStr | None

• search_space_template: edgeimpulse_api.models.optimize_config_search_space_template.OptimizeConfigSearchSpaceTemplate | None

• space: List[edgeimpulse_api.models.tuner_space_impulse.TunerSpaceImpulse] | None

• target_device: edgeimpulse_api.models.optimize_config_target_device.OptimizeConfigTargetDevice

• target_latency: pydantic.types.StrictInt

• training_cycles: pydantic.types.StrictInt | None

• trials_per_optimization_round: pydantic.types.StrictInt | None

• tuner_space_options: Dict[str, List[pydantic.types.StrictStr]] | None

• tuning_algorithm: pydantic.types.StrictStr | None

• tuning_max_trials: pydantic.types.StrictInt | None

• tuning_workers: pydantic.types.StrictInt | None

• verbose_logging: pydantic.types.StrictBool | None

Static methods

from_dict

edgeimpulse_api.models.optimize_config.from_dict(
		obj: dict
) ‑> edgeimpulse_api.models.optimize_config.OptimizeConfig

Create an instance of OptimizeConfig from a dict

Parameters

• obj: dict

Return

edgeimpulse_api.models.optimize_config.OptimizeConfig

from_json

edgeimpulse_api.models.optimize_config.from_json(
		json_str: str
) ‑> edgeimpulse_api.models.optimize_config.OptimizeConfig

Create an instance of OptimizeConfig from a JSON string

Parameters

• json_str: str

Return

edgeimpulse_api.models.optimize_config.OptimizeConfig

optimization_precision_validate_enum

edgeimpulse_api.models.optimize_config.optimization_precision_validate_enum(
		v
)

Parameters

• v

tuning_algorithm_validate_enum

edgeimpulse_api.models.optimize_config.tuning_algorithm_validate_enum(
		v
)

Parameters

• v

Methods

to_dict

edgeimpulse_api.models.optimize_config.to_dict(
		self
)

Returns the dictionary representation of the model using alias

Parameters

• self

to_json

edgeimpulse_api.models.optimize_config.to_json(
		self,
		indent=None
) ‑> str

Returns the JSON representation of the model using alias

Parameters

• self

• indent=None

Return

str

to_str

edgeimpulse_api.models.optimize_config.to_str(
		self
) ‑> str

Returns the string representation of the model using alias

Parameters

• self

Return

str