From 9196daf7781f84100d06b3704558ba69b3794a46 Mon Sep 17 00:00:00 2001 From: Manu S Joseph Date: Thu, 7 Dec 2023 09:11:31 +0100 Subject: [PATCH 1/3] Corrected all training data deletion function documentation in user guides --- docs/user_guides/fs/feature_view/training-data.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/user_guides/fs/feature_view/training-data.md b/docs/user_guides/fs/feature_view/training-data.md index 538609842..6d56ab652 100644 --- a/docs/user_guides/fs/feature_view/training-data.md +++ b/docs/user_guides/fs/feature_view/training-data.md @@ -101,7 +101,7 @@ To clean up unused training data, you can delete all training data or for a part feature_view.delete_training_dataset(version=1) # delete all training datasets -feature_view.delete_training_dataset() +feature_view.delete_all_training_datasets() ``` It is also possible to keep the metadata and delete only the materialised files. Then you can recreate the deleted files by just specifying a version, and you get back the exact same dataset again. This is useful when you are running out of storage. ```python From 4e7840d5db63987dbee38783ad9f116a54875a87 Mon Sep 17 00:00:00 2001 From: Manu S Joseph Date: Thu, 7 Dec 2023 15:28:49 +0100 Subject: [PATCH 2/3] Correcting parameter name in delete_training_dataset as per hsfs documentation --- docs/user_guides/fs/feature_view/training-data.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/user_guides/fs/feature_view/training-data.md b/docs/user_guides/fs/feature_view/training-data.md index 6d56ab652..2e90e48b0 100644 --- a/docs/user_guides/fs/feature_view/training-data.md +++ b/docs/user_guides/fs/feature_view/training-data.md @@ -98,7 +98,7 @@ X_train, X_val, X_test, y_train, y_val, y_test = feature_view.get_train_validati To clean up unused training data, you can delete all training data or for a particular version. Note that all metadata of training data and materialised files stored in HopsFS will be deleted and cannot be recreated anymore. ```python # delete a training data version -feature_view.delete_training_dataset(version=1) +feature_view.delete_training_dataset(training_dataset_version=1) # delete all training datasets feature_view.delete_all_training_datasets() From 59bd3d59c623b87d47d1d2fc2367ced3b766208e Mon Sep 17 00:00:00 2001 From: Manu S Joseph Date: Thu, 7 Dec 2023 16:13:31 +0100 Subject: [PATCH 3/3] corrected paramter names for purge_training_data and recreate_training_dataset --- docs/user_guides/fs/feature_view/training-data.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/user_guides/fs/feature_view/training-data.md b/docs/user_guides/fs/feature_view/training-data.md index 2e90e48b0..8e431799b 100644 --- a/docs/user_guides/fs/feature_view/training-data.md +++ b/docs/user_guides/fs/feature_view/training-data.md @@ -106,14 +106,14 @@ feature_view.delete_all_training_datasets() It is also possible to keep the metadata and delete only the materialised files. Then you can recreate the deleted files by just specifying a version, and you get back the exact same dataset again. This is useful when you are running out of storage. ```python # delete files of a training data version -feature_view.purge_training_data(version=1) +feature_view.purge_training_data(training_dataset_version=1) # delete files of all training datasets feature_view.purge_all_training_data() ``` To recreate a training dataset: ```python -feature_view.recreate_training_dataset(version=1) +feature_view.recreate_training_dataset(training_dataset_version =1) ``` ## Tags