Merge pull request #685 from mv1388/remove_deprecated_code

Remove deprecated code
mv1388 · Jul 8, 2022 · 055a2d7 · 055a2d7
2 parents f2f55d1 + c881069
commit 055a2d7
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diff --git a/aitoolbox/cloud/AWS/model_save.py b/aitoolbox/cloud/AWS/model_save.py
@@ -178,27 +178,3 @@ def save_model(self, model, project_name, experiment_name, experiment_timestamp=
         full_model_s3_path = os.path.join(self.bucket_name, model_s3_path)
 
         return full_model_s3_path, experiment_timestamp, model_local_path
-
-
-# class TensorFlowS3ModelSaver(AbstractModelSaver, BaseModelSaver):
-#     def __init__(self, bucket_name='model-result', cloud_dir_prefix='',
-#                  local_model_result_folder_path='~/project/model_result', checkpoint_model=False):
-#         """TensorFlow S3 model saving
-#
-#         Not implemented yet, just a placeholder
-#
-#         Args:
-#             bucket_name (str): name of the bucket in the S3 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
-#         """
-#         BaseModelSaver.__init__(self, bucket_name, cloud_dir_prefix, checkpoint_model)
-#         self.tf_local_saver = TensorFlowLocalModelSaver(local_model_result_folder_path, checkpoint_model)
-#
-#         raise NotImplementedError
-#
-#     def save_model(self, model, project_name, experiment_name, experiment_timestamp=None, epoch=None,
-#                    protect_existing_folder=True):
-#         raise NotImplementedError
-#         pass
diff --git a/aitoolbox/cloud/GoogleCloud/model_save.py b/aitoolbox/cloud/GoogleCloud/model_save.py
@@ -45,20 +45,3 @@ def __init__(self, bucket_name='model-result', cloud_dir_prefix='',
         """
         BaseModelGoogleStorageSaver.__init__(self, bucket_name, cloud_dir_prefix, checkpoint_model)
         self.keras_local_saver = KerasLocalModelSaver(local_model_result_folder_path, checkpoint_model)
-
-
-# class TensorFlowGoogleStorageModelSaver(BaseModelGoogleStorageSaver, TensorFlowS3ModelSaver):
-#     def __init__(self, bucket_name='model-result', cloud_dir_prefix='',
-#                  local_model_result_folder_path='~/project/model_result', checkpoint_model=False):
-#         """TensorFlow 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.tf_local_saver = TensorFlowLocalModelSaver(local_model_result_folder_path, checkpoint_model)
-#
-#         raise NotImplementedError
diff --git a/aitoolbox/experiment/local_save/local_model_save.py b/aitoolbox/experiment/local_save/local_model_save.py
@@ -4,7 +4,6 @@
 import datetime
 
 from aitoolbox.experiment.local_save.folder_create import ExperimentFolder
-from aitoolbox.utils.file_system import zip_folder
 
 
 class AbstractLocalModelSaver(ABC):
@@ -180,24 +179,6 @@ def save_model(self, model, project_name, experiment_name, experiment_timestamp=
         return model_name, model_local_path
 
 
-# class TensorFlowLocalModelSaver(AbstractLocalModelSaver, BaseLocalModelSaver):
-#     def __init__(self, local_model_result_folder_path='~/project/model_result',
-#                  checkpoint_model=False):
-#         """TensorFlow experiment local model saver
-#
-#         Args:
-#             local_model_result_folder_path (str): root local path where project folder will be created
-#             checkpoint_model (bool): if the model is coming from the mid-training checkpoint
-#         """
-#         BaseLocalModelSaver.__init__(self, local_model_result_folder_path, checkpoint_model)
-#
-#         raise NotImplementedError
-#
-#     def save_model(self, model, project_name, experiment_name, experiment_timestamp=None, epoch=None,
-#                    protect_existing_folder=True):
-#         raise NotImplementedError
-
-
 class LocalSubOptimalModelRemover:
     def __init__(self, metric_name, num_best_kept=2):
         """Removes the tracked saved models which become suboptimal when new models are trained in subsequent epochs