def run(force_rerun: bool, params_path: Optional[str],
changed_params_path: Optional[str], device_id: Optional[int]) -> None:
module = get_params_module(params_path, 'cl_default_config')
config = module.classifier_config
if changed_params_path:
with open(changed_params_path, 'r') as f:
patch = dict(jsons.loads(f.read()))
config = patch_config(config, patch)
if gpu.gpu_available():
gpu_id_to_use = device_id if device_id is not None else get_current_device(
)
logger.debug(f'Using gpu with id: {gpu_id_to_use}')
with device(gpu_id_to_use):
run_on_device(config, force_rerun)
else:
run_on_device(config, force_rerun)
def run(find_lr: bool, force_rerun: bool, params_path: Optional[str],
changed_params_path: Optional[str], device_id: Optional[int]) -> None:
if find_lr:
module = get_params_module(params_path, 'lm_lr_default_config')
config = module.lm_lr_config
else:
module = get_params_module(params_path, 'lm_default_config')
config = module.lm_config
if changed_params_path:
with open(changed_params_path, 'r') as f:
patch = dict(jsons.loads(f.read()))
config = patch_config(config, patch)
logger.info(f'Using config: {jsons.dumps(config)}')
if gpu.gpu_available():
gpu_id_to_use = device_id if device_id is not None else get_current_device(
)
with device(gpu_id_to_use):
run_on_device(config, find_lr, force_rerun)
else:
run_on_device(config, find_lr, force_rerun)
def test_patch(self):
changed_config = patch_config(classifier_config,
{'training.lrs.base_lr': 1000.0})
self.assertEqual(changed_config.training.lrs.base_lr, 1000)
def test_patch_no_attribute2(self):
with self.assertRaises(AttributeError):
patch_config(classifier_config,
{'not_existent_attr.lrs.base_lr': 1000.0})
def test_patch_no_attribute1(self):
with self.assertRaises(AttributeError):
patch_config(classifier_config,
{'training.lrs.not_existent_attr': 1000.0})