model_save

class aitoolbox.cloud.GoogleCloud.model_save.BaseModelGoogleStorageSaver(bucket_name='model-result', cloud_dir_prefix='', checkpoint_model=False)[source]

Bases: BaseGoogleStorageDataSaver

Base model saving to Google Cloud Storage functionality

Parameters:

  • 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

class aitoolbox.cloud.GoogleCloud.model_save.PyTorchGoogleStorageModelSaver(bucket_name='model-result', cloud_dir_prefix='', local_model_result_folder_path='~/project/model_result', checkpoint_model=False)[source]

Bases: BaseModelGoogleStorageSaver, PyTorchS3ModelSaver

PyTorch Google Cloud Storage model saving

Parameters:

  • 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

class aitoolbox.cloud.GoogleCloud.model_save.KerasGoogleStorageModelSaver(bucket_name='model-result', cloud_dir_prefix='', local_model_result_folder_path='~/project/model_result', checkpoint_model=False)[source]

Bases: BaseModelGoogleStorageSaver, KerasS3ModelSaver

Keras Google Storage model saving

Parameters:

  • 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