tuner package
check_tuner
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
print_tuner_coordinator_logs
Retrieve and print logs for the tuner coordinator job.
Returns: None
Parameters
limit: int = 500
Return
None
print_tuner_job_logs
Retrieve and print logs for the tuner job.
Returns: None
Parameters
limit: int = 500
Return
None
set_impulse_from_trial
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
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
Start the EON tuner with default settings. Use start_custom_tuner
to specify config.
Parameters
target_device: str
classification_type: str
dataset_category: str
target_latency: int
tuning_max_trials: int = None
name: str = None
Return
edgeimpulse_api.models.start_job_response.StartJobResponse
tuner_report_as_df
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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