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%.

本文介绍了事务Python的愿景，其提供了DART而无需对用户程序或Python kernel进行任何代码修改，通过在对象级别上非侵入性地监视应用程序状态并确定最少量的信息，足以重构整个应用程序。我们对公共PyTorch和scikit-learn应用程序的概念证明实现进行的评估表明，DART的开销范围为1.5％-15.6％。

持久化机器学习的事务性Python：愿景、挑战和可行性