Source code for aitoolbox.cloud.GoogleCloud.model_save

from aitoolbox.cloud.GoogleCloud.data_access import BaseGoogleStorageDataSaver
from aitoolbox.cloud.AWS.model_save import PyTorchS3ModelSaver, KerasS3ModelSaver
from aitoolbox.experiment.local_save.local_model_save import PyTorchLocalModelSaver, KerasLocalModelSaver


[docs]class BaseModelGoogleStorageSaver(BaseGoogleStorageDataSaver): def __init__(self, bucket_name='model-result', cloud_dir_prefix='', checkpoint_model=False): """Base model saving to Google Cloud Storage functionality Args: bucket_name (str): Google Cloud Storage bucket into which the files will be saved cloud_dir_prefix (str): destination folder path inside selected bucket checkpoint_model (bool): if the model that is going to be saved is final model or mid-training checkpoint """ BaseGoogleStorageDataSaver.__init__(self, bucket_name) self.cloud_dir_prefix = cloud_dir_prefix self.checkpoint_model = checkpoint_model
[docs]class PyTorchGoogleStorageModelSaver(BaseModelGoogleStorageSaver, PyTorchS3ModelSaver): def __init__(self, bucket_name='model-result', cloud_dir_prefix='', local_model_result_folder_path='~/project/model_result', checkpoint_model=False): """PyTorch Google Cloud Storage model saving Args: bucket_name (str): name of the bucket in the Google Cloud Storage to which the models will be saved cloud_dir_prefix (str): destination folder path inside selected bucket local_model_result_folder_path (str): root local path where project folder will be created checkpoint_model (bool): if the model being saved is checkpoint model or final end of training model """ BaseModelGoogleStorageSaver.__init__(self, bucket_name, cloud_dir_prefix, checkpoint_model) self.pytorch_local_saver = PyTorchLocalModelSaver(local_model_result_folder_path, checkpoint_model)
[docs]class KerasGoogleStorageModelSaver(BaseModelGoogleStorageSaver, KerasS3ModelSaver): def __init__(self, bucket_name='model-result', cloud_dir_prefix='', local_model_result_folder_path='~/project/model_result', checkpoint_model=False): """Keras Google Storage model saving Args: bucket_name (str): name of the bucket in the Google Cloud Storage to which the models will be saved cloud_dir_prefix (str): destination folder path inside selected bucket local_model_result_folder_path (str): root local path where project folder will be created checkpoint_model (bool): if the model being saved is checkpoint model or final end of training model """ BaseModelGoogleStorageSaver.__init__(self, bucket_name, cloud_dir_prefix, checkpoint_model) self.keras_local_saver = KerasLocalModelSaver(local_model_result_folder_path, checkpoint_model)