tuner package

check_tuner

edgeimpulse.experimental.tuner.check_tuner(
		timeout_sec=60,
		wait_for_completion=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=60

  • wait_for_completion=True

Return

edgeimpulse_api.models.optimize_state_response.OptimizeStateResponse

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

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
)> edgeimpulse_api.models.start_job_response.StartJobResponse

Replace the current impulse configuration with the one used by the trial.

Parameters

  • trial_id: str

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(
		target_device: str,
		classification_type: str,
		dataset_category: str,
		target_latency: int,
		tuning_max_trials: int = None,
		name: str = None
)> edgeimpulse_api.models.