Deploying Jupyter Notebooks

Teja Srivastasa has an example of deploying a Jupyter notebook for production use on AWS:

No one can deny how large the online support community for data science is. Today, it’s possible to teach yourself Python and other programming languages in a matter of weeks. And if you’re ever in doubt, there’s a StackOverflow thread or something similar waiting to give you the perfect piece of code to help you.

But when it came to pushing it to production, we found very little documentation online. Most data scientists seem to work on Python notebooks in a silo. They process large volumes of data and analyze it — but within the confines of Jupyter Notebooks. And most of the resources we’ve found while growing as data scientists revolve around Jupyter Notebooks.

Another option might be to use JupyterHub.

Related Posts

Literate Programming And Notebooks

David Smith sums up a debate on notebooks versus literate programming: There’s no video yet available of Joel’s talk, but you can guess the theme of that opening slide, and walking through the slides conveys the message well, I think. Yuhui Xie, author and creator of the rmarkdown package, provides a detailed summary and response to Joel’s talk, […]

Read More

Scheduling Jupyter Notebooks

Matthew Seal, et al, explain how they schedule runs of Jupyter notebooks: On the surface, notebooks pose a lot of challenges: they’re frequently changed, their cell outputs need not match the code, they’re difficult to test, and there’s no easy way to dynamically configure their execution. Furthermore, you need a notebook server to run them, […]

Read More

Categories

February 2018
MTWTFSS
« Jan Mar »
 1234
567891011
12131415161718
19202122232425
262728