Technical demonstration reveals live session state migration in computational notebooks, highlighting methods to prevent data loss.
Computational notebooks (e.g., Jupyter, Google Colab) are widely used for interactive data science and machine learning. However, existing notebook systems lack the functionality of reliably and efficiently persisting thenotebook session state consisting of user-defined variables (e.g., processed datasets, ML models), hence the termination of a session often leads to loss of work.
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Li et al. (2024) studied this question.
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