'max_experiment_duration':
lambda value: f'{util.parse_time(value)}s' if value is not None else None,
'experiment_working_directory':
util.canonical_path
}
_validation_rules = {
'trial_code_directory':
lambda value:
(Path(value).is_dir(), f'"{value}" does not exist or is not directory'),
'trial_concurrency':
lambda value: value > 0,
'trial_gpu_number':
lambda value: value >= 0,
'max_experiment_duration':
lambda value: util.parse_time(value) > 0,
'max_trial_number':
lambda value: value > 0,
'log_level':
lambda value: value in
["trace", "debug", "info", "warning", "error", "fatal"],
'training_service':
lambda value:
(type(value) is not TrainingServiceConfig, 'cannot be abstract base class')
}
def preprocess_model(base_model, trainer, applied_mutators, full_ir=True):
# TODO: this logic might need to be refactored into execution engine
if full_ir:
try:
@property
def _validation_rules(self):
return _validation_rules
_canonical_rules = {
'trial_code_directory': util.canonical_path,
'max_experiment_duration': lambda value: f'{util.parse_time(value)}s' if value is not None else None,
'experiment_working_directory': util.canonical_path
}
_validation_rules = {
'trial_code_directory': lambda value: (Path(value).is_dir(), f'"{value}" does not exist or is not directory'),
'trial_concurrency': lambda value: value > 0,
'trial_gpu_number': lambda value: value >= 0,
'max_experiment_duration': lambda value: util.parse_time(value) > 0,
'max_trial_number': lambda value: value > 0,
'log_level': lambda value: value in ["trace", "debug", "info", "warning", "error", "fatal"],
'training_service': lambda value: (type(value) is not TrainingServiceConfig, 'cannot be abstract base class')
}
def preprocess_model(base_model, trainer, applied_mutators):
try:
script_module = torch.jit.script(base_model)
except Exception as e:
_logger.error('Your base model cannot be parsed by torch.jit.script, please fix the following error:')
raise e
base_model_ir = convert_to_graph(script_module, base_model)
base_model_ir.evaluator = trainer
# handle inline mutations
