tuner package
check_tuner
edgeimpulse.experimental.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
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: 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
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: 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
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
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