Mito Is a JupyterLab Extension to Make Python Data Analysis Easy
Running in Production
English - May 03, 2021 04:00 - 1 hour - 61.6 MB - ★★★★★ - 8 ratingsTechnology News Tech News deployment elixir flask hosting node rubyonrails phoenix production python techstacks Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
Nate Rush goes over building a JupyterLab extension with Python. The managed Jupyter Lab instances run on a Kubernetes cluster on EKS.
In this episode of Running in Production, Nate Rush goes over building a
JupyterLab extension with Python. It runs locally in a Jupyter Notebook but
they also host JupyterHub instances on a Kubernetes cluster running in EKS.
It’s been running in production since late 2020.
Nate talks about building a cross platform Python installer, building and
publishing a JupyterLab extension, leveraging Pandas, building the front-end
with React / Typescript, hosting a version of their product on a Kubernetes
cluster, the value in automating the hard stuff and more.
Topics Include
1:15 – What is a Jupyter Notebook and how do JupyterLab extensions work?
3:35 – 2 developers are currently building it
6:50 – You can run it in an offline notebook or through their hosted service
8:48 – Handling local installations across Windows, macOS and Linux
15:58 – How users launch their Jupyter Notebook locally with this extension
17:16 – How do you create a JupyterLab extension?
21:10 – The back-end is built upon Pandas
23:31 – How do front-end assets make their way into the extension?
28:50 – Webpack is being used to bundle up the front-end assets
29:36 – Using GitHub Actions to handle the deploy process for the JupyterLab extension
35:20 – Front-end packages to help build a spreadsheet driven UI
37:37 – Discovering front-end performance issues and errors through logging
45:12 – Hosting JupyterHub instances on Kubernetes with EKS on AWS
47:32 – The hosted version is free, but it’s on its way to being phased out
51:57 – The workflow for deploying the Kubernetes set up and running 3-10 worker nodes
54:56 – Backups of the Kubernetes cluster are handled with Velero
56:13 – Route53 is hosting their DNS records
57:29 – Best tips? They really love Typescript and also automate as much as possible
1:00:58 – Don’t hesitate to ask for help and focus on what your users want
1:06:34 – Check out https://trymito.io
Links
📄 References
http://paulgraham.com/ds.html
https://docs.anaconda.com/anaconda/navigator/
https://jupyter.org/hub
The Phoenix Project book
⚙️ Tech Stack
jupyter →
python →
react →
aws →
kubernetes →
webpack →
github-actions →
s3 →
route53 →
segment →
mixpanel →
🛠 Libraries Used
https://pandas.pydata.org/
https://github.com/pytest-dev/pytest
https://github.com/DevExpress/testcafe
https://www.ag-grid.com/
https://velero.io/
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