dockerized-jupyter-notebook
awesome-jupyter
dockerized-jupyter-notebook | awesome-jupyter | |
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2 | 5 | |
1 | 3,786 | |
- | - | |
4.7 | 4.1 | |
6 months ago | 5 days ago | |
Jupyter Notebook | ||
MIT License | Creative Commons Attribution Share Alike 4.0 |
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dockerized-jupyter-notebook
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Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
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
awesome-jupyter
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Spreadsheet errors can have disastrous consequences – yet we keep making them
What are some Software Development methods for reducing errors:
1. AUTOMATED TESTS; test assertions
To write spreadsheet tests:
A. Write your own test assertion library for their macro language; write assertEqual() in VBscript and Apps Script.
B. Use another language with a test library and a test runner; e.g. Python and the `assert` keyword, unittest.TestCase().assertEqual() or pytest.
C. Test the spreadsheet GUI with something like AutoHotKey.
From https://news.ycombinator.com/item?id=35896192 :
> The Scientific Method is testing, so testing (tests, assertions, fixtures) should be core to any scientific workflow system.
> awesome-jupyter#testing: https://github.com/markusschanta/awesome-jupyter#testing
> ml-tooling/best-of-jupyter lists papermill/papermill under "Interactive Widgets/Visualization" https://github.com/ml-tooling/best-of-jupyter#interactive-wi...
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Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
https://github.com/markusschanta/awesome-jupyter#hosted-note...
- Ask HN: Jupyter Tutorials
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How many of us live paycheck to paycheck?
In the open source world, look at the sort of stuff you can learn for free...... https://github.com/markusschanta/awesome-jupyter
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How to create a dashboard in Python with Jupyter Notebook
> Rendering/Publishing/Conversion https://github.com/markusschanta/awesome-jupyter#renderingpu... :
> ContainDS Dashboards - JupyterHub extension to host authenticated scripts or notebooks in any framework (Voilà, Streamlit, Plotly Dash etc)
Streamlit lists Bokeh, Jupyter Voila , Panel, and Plotly Dash as Alternative dashboard approaches:
What are some alternatives?
livebook - Automate code & data workflows with interactive Elixir notebooks
jupyter-book - Create beautiful, publication-quality books and documents from computational content.
awesome-notebooks - A powerful data & AI notebook templates catalog: prompts, plugins, models, workflow automation, analytics, code snippets - following the IMO framework to be searchable and reusable in any context.
mercury - Convert Jupyter Notebooks to Web Apps
frontends-team-compass - A repository for team interaction, syncing, and handling meeting notes across the JupyterLab ecosystem.
datascience - Curated list of Python resources for data science.
theme-material-darcula - A Jupyterlab theme inspired by JetBrains IDE's Darcula scheme and Material Design. Now with support for all JupyterLab 2.x, 3.x, and 4.x versions!
awesome-maps-data - Browse Awesome Maps + Data, where vast amounts of information are beautifully mapped and visualized!
zoose-gitpod - Run Zoose Quantum in the browser with no installation