def get_tuner_class_dict():
config_file = str(get_config_file('registered_algorithms.yml'))
if os.path.exists(config_file):
with open(config_file, 'r') as f:
config = yaml.safe_load(f)
else:
config = {}
ret = {}
for t in ['tuners', 'advisors']:
for entry in config[t]:
ret[entry['builtinName']] = entry['className']
return ret
def get_registered_algo_config_path():
return str(get_config_file('registered_algorithms.yml'))
def _save_custom_config(custom_algos):
config = defaultdict(list)
for algo in custom_algos:
config[algo.algo_type + 's'].append(algo.dump())
text = yaml.dump(dict(config), default_flow_style=False)
get_config_file('registered_algorithms.yml').write_text(text)
def _load_custom_config():
path = get_config_file('registered_algorithms.yml')
# for backward compatibility, NNI v2.5- stores all algorithms in this file
return [algo for algo in _load_config_file(path) if not algo.is_builtin]
def _save(config):
json.dump(config, get_config_file('training_services.json').open('w'), indent=4)
def _load():
return json.load(get_config_file('training_services.json').open())