Releases: sassoftware/python-sasctl
Releases · sassoftware/python-sasctl
v1.11.6
v1.11.5
Improvements
- Added model versioning methods to
model_repository.pyto handle model version endpoints. - Allow for user to set custom timeout length for score testing in
score_model_with_cas.
v1.11.4
Improvements
- Improved
upload_local_modelto allow for SAS Model Manager to properly intake local ASTORE models.
v1.11.3
Improvements
- Added
upload_local_modeltotasks.py, which can be used to upload local directories to SAS Model Manager without any file generation.
v1.11.2
Bugfixes
- Updated
calculate_model_statisticsfunction inwrite_json_files.pyto improve ROC tables as well as model card files.
v1.11.1
Improvements
- Improved functionality for
score_definition.pyandscore_execution.py, allowing for more general usage of thescore_model_with_castask- Also allowed for model name to be passed into functions instead of requiring model UUID
- Pickle files now loaded with
pd.read_pickle()instead ofpickle.load()in score code, allowing for more flexibility between python environments
Bugfixes
- Updated
pzmm_generate_complete_model_card.ipynbto have better preprocessing function
v1.11.0
Changes
- Added
score_definition.pyandscore_execution.pyto allow for score testing within SAS Model Manager- Included optional use of CAS Gateway for faster scoring. Only available in environments where Gateway scoring is properly set up.
- Added ability to include data pre-processing function within python score code using the
preprocess_functionargument.
Bugfixes
- Fixed issue where settings file was improperly imported in some score code files.
v1.10.7
Bugfixes
- Fixed a bug that caused an error when performing SSL verification without a CA bundle specified.
v1.10.6
Improvements
- Refactor
tasks.pyto utilizesasctl.pzmmfunctions. - Add
model_infoclass to better capture model information.
v1.10.5
Buxfixes
- Updated
write_json_files.pyto allow for better support for prediction models - Fixed issues relating to model card support.