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: Optional[float]

• compiler: Optional[List[pydantic.types.StrictStr]]

• early_stopping: Optional[pydantic.types.StrictBool]

• early_stopping_improvement_bar: Optional[float]

• early_stopping_window_size: Optional[float]

• enable_sem: Optional[pydantic.types.StrictBool]

• import_project_metrics: Optional[pydantic.types.StrictBool]

• import_resource_metrics: Optional[pydantic.types.StrictBool]

• initial_trials: Optional[pydantic.types.StrictInt]

• latency_sem: Optional[float]

• max_maccs: Optional[float]

• min_maccs: Optional[float]

• momf: Optional[pydantic.types.StrictBool]

• name: Optional[pydantic.types.StrictStr]

• notification_on_completion: Optional[pydantic.types.StrictBool]

• num_import_project_metrics: Optional[float]

• num_import_resource_metrics: Optional[float]

• optimization_objectives: Optional[List[pydantic.types.StrictStr]]

• optimization_precision: Optional[pydantic.types.StrictStr]

• optimization_rounds: Optional[pydantic.types.StrictInt]

• precision: Optional[List[pydantic.types.StrictStr]]

• raw_objectives: Optional[pydantic.types.StrictStr]

• search_space_template: Optional[edgeimpulse_api.models.optimize_config_search_space_template.OptimizeConfigSearchSpaceTemplate]

• space: Optional[List[edgeimpulse_api.models.tuner_space_impulse.TunerSpaceImpulse]]

• target_device: edgeimpulse_api.models.optimize_config_target_device.OptimizeConfigTargetDevice

• target_latency: pydantic.types.StrictInt

• training_cycles: Optional[pydantic.types.StrictInt]

• trials_per_optimization_round: Optional[pydantic.types.StrictInt]

• tuner_space_options: Optional[Dict[str, List[pydantic.types.StrictStr]]]

• tuning_algorithm: Optional[pydantic.types.StrictStr]

• tuning_max_trials: Optional[pydantic.types.StrictInt]

• tuning_workers: Optional[pydantic.types.StrictInt]

• verbose_logging: Optional[pydantic.types.StrictBool]

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