def calc_configs(self,
configs: Configs,
configs_override: Optional[Dict[str, any]],
run_order: Optional[List[Union[List[str], str]]]):
self.configs_processor = ConfigProcessor(configs, configs_override)
self.configs_processor(run_order)
logger.log()
def test():
configs = SampleConfigs()
processor = ConfigProcessor(configs, {'module.prop2': 20})
processor()
processor.print()
print(configs.__dict__)
print(configs.module.__dict__)
def calc_configs(self, configs: Union[Configs, Dict[str, any]],
configs_override: Optional[Dict[str, any]]):
if configs_override is None:
configs_override = {}
if global_params_singleton().configs is not None:
configs_override.update(global_params_singleton().configs)
self.configs_processor = ConfigProcessor(configs, configs_override)
if self.distributed_rank == 0:
logger.log()
def calc_configs(self, configs: Configs,
configs_override: Optional[Dict[str, any]],
run_order: Optional[List[Union[List[str], str]]]):
if configs_override is None:
configs_override = {}
if global_params_singleton().configs is not None:
configs_override.update(global_params_singleton().configs)
self.configs_processor = ConfigProcessor(configs, configs_override)
self.configs_processor(run_order)
logger.log()
class SampleChild(Sample):
def __init__(self, *, test: int):
pass
new_attr = 2
@Sample.calc()
def input_model(c: Sample):
return c.workers_count * 2
@Sample.calc(Sample.input_model)
def input_model2(c: Sample):
return c.workers_count * 20
@Sample.calc('model')
def simple_model(c: Sample):
return c.total_global_steps * 3
configs = Sample()
processor = ConfigProcessor(configs)
processor()
processor.print()
print(configs.__dict__)