Three Tools for Executing Jupyter Notebooks

This page summarizes the projects mentioned and recommended in the original post on dev.to
Jupyter Workflow Machine Learning Notebooks Data Science

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  • ploomber

    The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️

    • NBClient supports running notebooks via CLI for the most basic use cases. However, for more sophisticated execution options, consider the Ploomber!

  • papermill

    📚 Parameterize, execute, and analyze notebooks

    • Papermill Documentation

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  • projects

    Sample projects using Ploomber. (by ploomber)

    • Ploomber is the complete solution for notebook execution. It builds on top of papermill and extends it to allow writing multi-stage workflows where each task is a notebook. Meanwhile, it automatically manages orchestration. Hence you can run notebooks in parallel without having to write extra code.

  • nbclient

    A client library for executing notebooks. Formally nbconvert's ExecutePreprocessor

    • NBClient Source Code

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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