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check_tuner

edgeimpulse.tuner.check_tuner(
		timeout_sec: int | None = 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: int | None = None
  • wait_for_completion: bool = True
Return edgeimpulse_api.models.optimize_state_response.OptimizeStateResponse

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get_tuner_run_state

edgeimpulse.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

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list_tuner_runs

edgeimpulse.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 
edgeimpulse.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.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

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set_impulse_from_trial

edgeimpulse.tuner.set_impulse_from_trial(
		trial_id: str,
		timeout_sec: float | None = None,
		wait_for_completion: bool | None = 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: float | None = None
  • wait_for_completion: bool | None = True
Return edgeimpulse_api.models.start_job_response.StartJobResponse

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start_custom_tuner

edgeimpulse.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

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start_tuner

edgeimpulse.tuner.start_tuner(
		space: List[edgeimpulse_api.models.tuner_space_impulse.TunerSpaceImpulse],
		target_device: str,
		target_latency: int,
		tuning_max_trials: int | None = None,
		name: str | None = 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: int | None = None
  • name: str | None = None
Return edgeimpulse_api.models.start_job_response.StartJobResponse

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tuner_report_as_df

edgeimpulse.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
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