Ask HN: Fastest way to turn a Jupyter notebook into a website these days?

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  • dockerized-jupyter-notebook

    This Docker container provides a Jupyter Notebook environment with some useful tools pre-installed. It's based on Ubuntu latest version and includes Node.js, Pandoc, Git, and several Python libraries.

  • your task is very very broad

    you mention you don't want to deal with AWS, if it's because of ad-hoc installation concerns and nothing else you can just run your notebooks in ready-made solutions like Google Colab, or Jupyter-book in Github ( https://github.com/executablebooks/jupyter-book ))

    that would cover a lot of use cases right away without next to no learning curve

    If you don't want to deal with AWS or similar, in that case:

    - if it's a static notebook then you can obviously render it and serve the web content (might seem obvious but needs to be considered)

    - if it's dynamic but has light hardware requirements, you can try jupyterlite which runs in the browser and should do a pyodine (webassembly CPython kernel) can do: https://jupyterlite.readthedocs.io/en/latest/try/lab/

    - otherwise, you can try exposing a dockerised jupyter env ( as in https://github.com/MKAbuMattar/dockerized-jupyter-notebook/b... ) or even better a nixified one ( https://github.com/tweag/jupyenv )

    there might be other approaches I'm missing, but I think that's pretty much it that doesn't entail some proprietary solution or an ad-hoc installation as you've been doing

  • Scout Monitoring

    Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.

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  • voila

    VoilĂ  turns Jupyter notebooks into standalone web applications

  • datapane

    Build and share data reports in 100% Python

  • You can build web apps from Jupyter using Datapane [0]. I'm one of the founders, so let me know if I can help at all.

    You can either export a static site [1] (and host on GH pages or S3), or, if you need backend logic, you can add Python functions [2] and serve on your favourite host (we use Fly).

    We have specific Jupyter integration to automatically convert your notebook into an app [3].

    [0] https://github.com/datapane/datapane

    [1] https://docs.datapane.com/reference/reports/#datapane.proces...

    [2] https://docs.datapane.com/apps/overview/

    [3] https://docs.datapane.com/reports/jupyter-integration/#conve...

  • awesome-jupyter

    A curated list of awesome Jupyter projects, libraries and resources

  • https://github.com/markusschanta/awesome-jupyter#hosted-note...

  • mercury

    Convert Jupyter Notebooks to Web Apps

  • https://github.com/mljar/mercury

    Im working also on cloud offering so you will upload notebooks and set domain. It is available in alpha at https://cloud.runmercury.com

  • jupyter-book

    Create beautiful, publication-quality books and documents from computational content.

  • your task is very very broad

    you mention you don't want to deal with AWS, if it's because of ad-hoc installation concerns and nothing else you can just run your notebooks in ready-made solutions like Google Colab, or Jupyter-book in Github ( https://github.com/executablebooks/jupyter-book ))

    that would cover a lot of use cases right away without next to no learning curve

    If you don't want to deal with AWS or similar, in that case:

    - if it's a static notebook then you can obviously render it and serve the web content (might seem obvious but needs to be considered)

    - if it's dynamic but has light hardware requirements, you can try jupyterlite which runs in the browser and should do a pyodine (webassembly CPython kernel) can do: https://jupyterlite.readthedocs.io/en/latest/try/lab/

    - otherwise, you can try exposing a dockerised jupyter env ( as in https://github.com/MKAbuMattar/dockerized-jupyter-notebook/b... ) or even better a nixified one ( https://github.com/tweag/jupyenv )

    there might be other approaches I'm missing, but I think that's pretty much it that doesn't entail some proprietary solution or an ad-hoc installation as you've been doing

  • jupyenv

    Declarative and reproducible Jupyter environments - powered by Nix

  • your task is very very broad

    you mention you don't want to deal with AWS, if it's because of ad-hoc installation concerns and nothing else you can just run your notebooks in ready-made solutions like Google Colab, or Jupyter-book in Github ( https://github.com/executablebooks/jupyter-book ))

    that would cover a lot of use cases right away without next to no learning curve

    If you don't want to deal with AWS or similar, in that case:

    - if it's a static notebook then you can obviously render it and serve the web content (might seem obvious but needs to be considered)

    - if it's dynamic but has light hardware requirements, you can try jupyterlite which runs in the browser and should do a pyodine (webassembly CPython kernel) can do: https://jupyterlite.readthedocs.io/en/latest/try/lab/

    - otherwise, you can try exposing a dockerised jupyter env ( as in https://github.com/MKAbuMattar/dockerized-jupyter-notebook/b... ) or even better a nixified one ( https://github.com/tweag/jupyenv )

    there might be other approaches I'm missing, but I think that's pretty much it that doesn't entail some proprietary solution or an ad-hoc installation as you've been doing

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

    InfluxDB logo
  • panel

    Panel: The powerful data exploration & web app framework for Python (by holoviz)

  • My suggestion is https://panel.holoviz.org/

    Fully open sourced, makes it easy to make reactive apps with small changes, can even configured as a graphical REPL.

  • livebook

    Automate code & data workflows with interactive Elixir notebooks

  • Bit of a tangent, but one of the things I really like about livebook[0] is that the documents are just markdown, and the codecells are literally just ```elixir``` blocks which make it quite nice to read and very version control friendly.

    [0] https://livebook.dev/

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