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Top 19 jupyterhub Open-Source Projects
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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.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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best-of-jupyter
🏆 A ranked list of awesome Jupyter Notebook, Hub and Lab projects (extensions, kernels, tools). Updated weekly.
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foundations-of-numerical-computing
Lecture Slides, Exercises, and Deployment Materials for "Foundations of Numerical Computing"
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team-compass
A repository for team interaction, syncing, and handling meeting notes across the JupyterHub ecosystem. (by jupyterhub)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
See https://github.com/jupyter/docker-stacks
Project mention: Spreadsheet errors can have disastrous consequences – yet we keep making them | news.ycombinator.com | 2024-01-25What 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...
Project mention: ChromeOS is Linux with Google’s desktop environment | news.ycombinator.com | 2023-10-05For students, unless there are allocated server resources with network access, it SHOULD/MUST scale down to one local offline ARM64 node (because school districts haven't afforded containers on a managed k8s cloud for students at scale fwiu, though universities do with e.g. JupyterHub and BinderHub [4] and Colab).
For Chromebook sysadmins, Instructors, and Students learning about how {Linux*, ChromiumOS, Android, Git, Bash, ZSH, Python, and e.g. PyData Tools supported by NumFOCUS} are developed, for example;
When you git commit to a git branch, and then `git push` that branch to GitHub, and create a Pull Request, GitHub Actions runs the (container,command) tasks defined in the YAML files in the .github/workflows/ directory of the repo; so `git push` to a PR branch runs the CI job and the results are written back as cards in the Pull Request thread on the GitHub Project; saving to the server runs the (container,command) Actions with that revision of the git repo.
Somewhat-equivalent GitOps CI Continuous Integration workflows (without Bazel or Blaze or gtest or gn, or GitHub Enterprise or GitHub Free due to the kids' intererests) that might be supported at least in analogue by Education and Chromebooks: k8s with podman-desktop in a VM, Gitea Actions (nektos/act; like Github Actions), devpod
devpod: https://github.com/loft-sh/devpod :
> Codespaces but open-source, client-only and unopinionated: Works with any IDE and lets you use any cloud, kubernetes or just localhost docker. (with devcontainer.json, like Github Codespaces)
devcontainer.json is supported by a number of tools; e.g. VScode, IntelliJ,: https://containers.dev/supporting
repo2docker has buildpacks (like Heroku and Google AppEngine).
repo2docker buildpacks should probably work with devcontainer.json too?
repo2docker docs > Usage > "REES: Reproducible Execution Environment" describes what all repo2docker will build a container from: https://repo2docker.readthedocs.io/en/latest/usage.html
jupyterhub/repo2docker builds a Dockerfile (Containerfile) from git repo (or a Figshare/Zenodo DOI) that minimally has at least an /environment.yml and /example.py (and probably also at least a /README.md to start with), and installs a current, updated version of jupyter notebook along with whatever's in e.g. /environment.yml per the REES spec. [1][2][3]
[1] repo2docker/buildpacks/base.py: https://github.com/jupyterhub/repo2docker/blob/main/repo2doc...
[2] "Make base_image configurable" https://github.com/jupyterhub/repo2docker/commit/20b08152578...
[3] repo2docker/buildpacks/conda/environment.py-3.11.yml:
Project mention: Spreadsheet errors can have disastrous consequences – yet we keep making them | news.ycombinator.com | 2024-01-25What 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...
Kubeflow is an ML platform like Sagemaker or Databricks that you can self-host in a Kubernetes cluster.
Installing/deploying it is as complicated as it sounds, but we've put together an infrastructure project that lets you '1-click' install it even in tiny environments.
The GH repo (also linked in blog) allows you to start Kubeflow in a codespace or small device using a docker container -- this is both good for trying it out and developing it into your own internal ML platform.
https://github.com/treebeardtech/kubeflow-helm
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
jupyterhub related posts
- Spreadsheet errors can have disastrous consequences – yet we keep making them
- ChromeOS is Linux with Google’s desktop environment
- Linux or Windows for coding??
- Connecting IPython notebook to spark master running in different machines
- awslabs/data-on-eks: DoEKS is a tool to build, deploy and scale Data Platforms on Amazon EKS
- Looking for a solution to sandbox python remotely.
- Ask HN: Jupyter Tutorials
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A note from our sponsor - InfluxDB
www.influxdata.com | 29 Apr 2024
Index
What are some of the best open-source jupyterhub projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | docker-stacks | 7,751 |
| 2 | Jupyter Notebook (IPython) | 7,576 |
| 3 | awesome-jupyter | 3,764 |
| 4 | repo2docker | 1,582 |
| 5 | zero-to-jupyterhub-k8s | 1,470 |
| 6 | nbgrader | 1,255 |
| 7 | the-littlest-jupyterhub | 988 |
| 8 | best-of-jupyter | 836 |
| 9 | jupyterhub-deploy-docker | 670 |
| 10 | kubespawner | 521 |
| 11 | data-on-eks | 500 |
| 12 | nebari | 256 |
| 13 | configurable-http-proxy | 227 |
| 14 | foundations-of-numerical-computing | 81 |
| 15 | team-compass | 61 |
| 16 | datahub | 61 |
| 17 | matlab-integration-for-jupyter | 27 |
| 18 | kubeflow-bootstrap | 19 |
| 19 | pangeo-binder | 18 |
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