Transactional Python for Durable Machine Learning: Vision, Challenges,
  and Feasibility
Supawit Chockchowwat Zhaoheng Li Yongjoo Park
In machine learning (ML), Python serves as a convenient abstraction for
working with key libraries such as PyTorch, scikit-learn, and others. Unlike
DBMS, however, Python applications may lose important data, such as trained
models and extracted features, due to machine failures or human errors, leading
to a waste of time and resources. Specifically, they lack four essential
properties that could make ML more reliable and user-friendly -- durability,
atomicity, replicability, and time-versioning (DART).
  This paper presents our vision of Transactional Python that provides DART
without any code modifications to user programs or the Python kernel, by
non-intrusively monitoring application states at the object level and
determining a minimal amount of information sufficient to reconstruct a whole
application. Our evaluation of a proof-of-concept implementation with public
PyTorch and scikit-learn applications shows that DART can be offered with
overheads ranging 1.5%--15.6%.