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What are checkpoints in a Jupyter notebook

By Nick Barth

Updated on March 6, 2024

Checkpoints in Jupyter notebook serve as a type of "save point" for your work. When you create a checkpoint, Jupyter saves the current state of the notebook, including all code, output, and markdown. It allows you to revert back to this state if needed.

How to create a checkpoint:

  • While working in a Jupyter notebook, you can create a checkpoint by clicking on `File` > `Save and Checkpoint` or by using the floppy disk icon (save icon).
  • Jupyter automatically creates a checkpoint at regular intervals. This means that your most recent work has likely been preserved since your last manual save.

How to revert to a checkpoint:

If you have made changes that you want to discard, or if an error has occurred and you want to go back to a previous state, you can revert to a checkpoint by:

  • Clicking on `File` > `Revert to Checkpoint`, and then selecting the checkpoint to which you want to revert.
  • Keep in mind that reverting to a checkpoint will replace the current content of the notebook with the content from the checkpoint.

Checkpoints in Deepnote:

Deepnote is a collaborative data science platform that also uses checkpointing. In Deepnote, checkpoints are particularly useful because they can be used to track changes in collaborative projects and manage different versions of notebooks.

Using checkpoints in Deepnote:

  • Create a checkpoint: Just like in Jupyter, you can save your progress in Deepnote. It creates snapshots of your environment, including the notebook, datasets, and any other files.
  • View checkpoint history: You can view the history and revert to previous checkpoints as needed. This can be done through the Deepnote interface, where you can browse through the different saved versions.
  • Collaborate safely: When working with others, checkpoints in Deepnote make sure changes are recorded, and it's easy to see who made which changes. This makes collaboration smoother and less prone to conflicts.

In summary, checkpoints in both Jupyter notebook and Deepnote offer a way to safeguard your work by saving the current state of your notebook. For beginners, they provide a simple yet effective means to prevent data loss and manage different stages of their data analysis process.

Nick Barth

Product Engineer

Nick has been interested in data science ever since he recorded all his poops in spreadsheet, and found that on average, he pooped 1.41 times per day. When he isn't coding, or writing content, he spends his time enjoying various leisurely pursuits.

Follow Nick on LinkedIn and GitHub

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