tuner package

check_tuner

edgeimpulse.experimental.tuner.check_tuner(
		timeout_sec: Optional[int] = None,
		wait_for_completion: bool = True
) ‑> edgeimpulse_api.models.optimize_state_response.OptimizeStateResponse

Check the current state of the tuner and optionally waits until the tuner has completed.

Parameters

• timeout_sec: Optional[int] = None

• wait_for_completion: bool = True

Return

edgeimpulse_api.models.optimize_state_response.OptimizeStateResponse

get_tuner_run_state

edgeimpulse.experimental.tuner.get_tuner_run_state(
		tuner_coordinator_job_id: int
) ‑> edgeimpulse_api.models.optimize_state_response.OptimizeStateResponse

Retrieve the current state of the tuner run.

Returns: OptimizeStateResponse: The OptimizeStateResponse object representing the current Tuner state.

Parameters

• tuner_coordinator_job_id: int

Return

edgeimpulse_api.models.optimize_state_response.OptimizeStateResponse

list_tuner_runs

edgeimpulse.experimental.tuner.list_tuner_runs(
		
) ‑> edgeimpulse_api.models.list_tuner_runs_response.ListTunerRunsResponse

List the tuner runs that have been done in the current project.

Returns: ListTunerRunsResponse: An object containing all the tuner runs

Return

edgeimpulse_api.models.list_tuner_runs_response.ListTunerRunsResponse

print_tuner_coordinator_logs

edgeimpulse.experimental.tuner.print_tuner_coordinator_logs(
		limit: int = 500
) ‑> None

Retrieve and print logs for the tuner coordinator job.

Returns: None

Parameters

• limit: int = 500

Return

None

print_tuner_job_logs

edgeimpulse.experimental.tuner.print_tuner_job_logs(
		limit: int = 500
) ‑> None

Retrieve and print logs for the tuner job.

Returns: None

Parameters

• limit: int = 500

Return

None

set_impulse_from_trial

edgeimpulse.experimental.tuner.set_impulse_from_trial(
		trial_id: str,
		timeout_sec: Optional[float] = None,
		wait_for_completion: Optional[bool] = True
) ‑> edgeimpulse_api.models.start_job_response.StartJobResponse

Replace the current Impulse configuration with one found in a trial fromm the tuner.

Parameters

• trial_id: str

• timeout_sec: Optional[float] = None

• wait_for_completion: Optional[bool] = True

Return

edgeimpulse_api.models.start_job_response.StartJobResponse

start_custom_tuner

edgeimpulse.experimental.tuner.start_custom_tuner(
		config: edgeimpulse_api.models.optimize_config.OptimizeConfig
) ‑> edgeimpulse_api.models.start_job_response.StartJobResponse

Start a tuner job with custom configuration.

Parameters

• config: edgeimpulse_api.models.optimize_config.OptimizeConfig

Return

edgeimpulse_api.models.start_job_response.StartJobResponse

start_tuner

edgeimpulse.experimental.tuner.start_tuner(
		space: List[edgeimpulse_api.models.tuner_space_impulse.TunerSpaceImpulse],
		target_device: str,
		target_latency: int,
		tuning_max_trials: Optional[int] = None,
		name: Optional[str] = None
) ‑> edgeimpulse_api.models.start_job_response.StartJobResponse

Start the EON tuner with default settings. Use start_custom_tuner to specify config.

Parameters

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

• target_device: str

• target_latency: int

• tuning_max_trials: Optional[int] = None

• name: Optional[str] = None

Return

edgeimpulse_api.models.start_job_response.StartJobResponse

tuner_report_as_df

edgeimpulse.experimental.tuner.tuner_report_as_df(
		state: edgeimpulse_api.models.optimize_state_response.OptimizeStateResponse
)

Get a tuner trial report dataframe with model metrics and block configuration.

This method needs pandas to be installed.

Generate a dataframe on the tuner trials including used input, model, learn block configuration and model validation metrics.

Parameters

• state: edgeimpulse_api.models.optimize_state_response.OptimizeStateResponse