def serialize_config(params: config_definitions.ExperimentConfig,
model_dir: str):
"""Serializes and saves the experiment config."""
params_save_path = os.path.join(model_dir, 'params.yaml')
logging.info('Saving experiment configuration to %s', params_save_path)
tf.io.gfile.makedirs(model_dir)
hyperparams.save_params_dict_to_yaml(params, params_save_path)
def serialize_config(experiment_params: params.EdgeTPUBERTCustomParams,
model_dir: str):
"""Serializes and saves the experiment config."""
params_save_path = os.path.join(model_dir, 'params.yaml')
logging.info('Saving experiment configuration to %s', params_save_path)
tf.io.gfile.makedirs(model_dir)
hyperparams.save_params_dict_to_yaml(experiment_params, params_save_path)
def serialize_config(params: config_definitions.ExperimentConfig,
model_dir: str):
"""Serializes and saves the experiment config."""
if model_dir is None:
raise ValueError('model_dir must be specified, but got None')
params_save_path = os.path.join(model_dir, 'params.yaml')
logging.info('Saving experiment configuration to %s', params_save_path)
tf.io.gfile.makedirs(model_dir)
hyperparams.save_params_dict_to_yaml(params, params_save_path)
def test_encoder_from_yaml(self):
config = encoders.EncoderConfig(
type="bert", bert=encoders.BertEncoderConfig(num_layers=1))
encoder = encoders.build_encoder(config)
ckpt = tf.train.Checkpoint(encoder=encoder)
ckpt_path = ckpt.save(self.get_temp_dir() + "/ckpt")
params_save_path = os.path.join(self.get_temp_dir(), "params.yaml")
hyperparams.save_params_dict_to_yaml(config, params_save_path)
retored_cfg = encoders.EncoderConfig.from_yaml(params_save_path)
retored_encoder = encoders.build_encoder(retored_cfg)
status = tf.train.Checkpoint(encoder=retored_encoder).restore(ckpt_path)
status.assert_consumed()
