def Do(self, input_dict: Dict[str, List[types.Artifact]],
output_dict: Dict[str, List[types.Artifact]],
exec_properties: Dict[str, Any]) -> None:
if tfx_tuner.get_tune_args(exec_properties):
raise ValueError(
"TuneArgs is not supported by this Tuner's Executor.")
metalearning_algorithm = None
if 'metalearning_algorithm' in exec_properties:
metalearning_algorithm = exec_properties.get(
'metalearning_algorithm')
warmup_trials = 0
warmup_trial_data = None
if metalearning_algorithm:
warmup_tuner, warmup_trials = self.warmup(input_dict,
exec_properties,
metalearning_algorithm)
warmup_trial_data = extract_tuner_trial_progress(warmup_tuner)
else:
logging.info('MetaLearning Algorithm not provided.')
# Create new fn_args for final tuning stage.
fn_args = fn_args_utils.get_common_fn_args(
input_dict, exec_properties, working_dir=self._get_tmp_dir())
tuner_fn = udf_utils.get_fn(exec_properties, 'tuner_fn')
tuner_fn_result = tuner_fn(fn_args)
tuner_fn_result.tuner.oracle.max_trials = max(
(tuner_fn_result.tuner.oracle.max_trials - warmup_trials), 1)
tuner = self.search(tuner_fn_result)
tuner_trial_data = extract_tuner_trial_progress(tuner)
if warmup_trial_data:
cumulative_tuner_trial_data, best_tuner_ix = merge_trial_data(
warmup_trial_data, tuner_trial_data)
cumulative_tuner_trial_data[
'warmup_trial_data'] = warmup_trial_data[BEST_CUMULATIVE_SCORE]
cumulative_tuner_trial_data['tuner_trial_data'] = tuner_trial_data[
BEST_CUMULATIVE_SCORE]
if isinstance(tuner.oracle.objective, kerastuner.Objective):
cumulative_tuner_trial_data[
'objective'] = tuner.oracle.objective.name
else:
cumulative_tuner_trial_data[
'objective'] = 'objective not understood'
tuner_trial_data = cumulative_tuner_trial_data
best_tuner = warmup_tuner if best_tuner_ix == 0 else tuner
else:
best_tuner = tuner
tfx_tuner.write_best_hyperparameters(best_tuner, output_dict)
tuner_plot_path = os.path.join(
artifact_utils.get_single_uri(output_dict['trial_summary_plot']),
'tuner_plot_data.txt')
io_utils.write_string_file(tuner_plot_path,
json.dumps(tuner_trial_data))
logging.info('Tuner plot data written at: %s', tuner_plot_path)
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]) -> None:
tuner = self._search(input_dict, exec_properties)
if self._tuner_id is not None and not self._is_chief:
logging.info('Returning since this is not chief worker.')
return
tuner_executor.write_best_hyperparameters(tuner, output_dict)
self._close()
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
exec_properties: Dict[Text, Any]) -> None:
tuner = self._search(input_dict, exec_properties)
if not self._is_chief:
logging.info('Returning since this is not chief worker.')
tuner_executor.write_best_hyperparameters(tuner, output_dict)
if self._chief_process and self._chief_process.is_alive():
logging.info('Terminating chief oracle at PID: %s',
self._chief_process.pid)
self._chief_process.terminate()