Jupyter Blog - Serena Bonaretti

JupyterHub 6.0

2026-09-11T08:54:00+00:00

By Serena Bonaretti and Min Ragan-Kelley

JupyterHub is a collection of tools that provides standardized, user-friendly computing environments for education and research. Today, we’re pleased to announce the release of JupyterHub 6.0. This is a substantial release with several new features. The breaking changes ought to be minimal, and should affect very few deployments. There is a small database schema upgrade, so make sure to backup your database prior to upgrading, as usual.

Some changes to highlight:

Stricter named server rules: Named servers now have stricter rules on their names, and a new less-restricted “display name” field. This might be the most significant change affecting you! See the documentation for more details.
Python 3.10 and database upgrade: JupyterHub 6.0 now requires Python 3.10 and includes a database schema upgrade. Make sure to back up your database before upgrading.
Updates to metrics: The old StatsD metrics have been removed, in favor of the Prometheus metrics that most people are already using. For Spawner (and spawn hook) authors, JupyterHub 6.0 also introduces SpawnException, which provides more granular information for classifying spawn failures in metrics, to help deployments separate errors from rejections.
Internal communication changes: JupyterHub now uses aiohttp for internal HTTP requests, which may require tuning for large deployments since internal HTTP requests. This update has facilitated using unix sockets for internal communication on single-node deployments.
Improved granularity in API access: JupyterHub provides new API endpoints for working with single servers, along with more flexible ways to define permissions, such as start:servers scope and extra_user_scopes.
See the documentation for more information.

Beyond these main changes, there have been lots of other improvements and bugfixes. You can find more details in the upgrading documentation and changelog.

Thank you to our contributors

The following people contributed discussions, new ideas, code and documentation contributions, and review: agoose77, anny0811, ateucher, betatim, bl-aire, clhedrick, consideRatio, dometto, dragz, erawn, ericvd-ucb, jezwilkinson, jnywong, jrdnbradford, kellyrowland, kreuzert, LUC4R4T0R, manics, mayli, milliams, minrk, mynkyu, nsychev, Paul2708, Rameshkumar31595, rgaiacs, shaneknapp, sixonenines, tbizouerne, Tech-Abhang, wilkinvr, willingc, yuvipanda

Becoming the new JupyterHub and Jupyter Book community manager

2026-06-15T07:01:00+00:00

There are moments in life when things, simply, converge. You work and study, study and work, and then, simply, it happens. It starts one day when somebody tells you “there is this new thing called Jupyter Notebook, you should give it a try!”. So you install it, play around with it. It works like a charm. It’s easy, fast, nearly playful. It’s beautiful–and if you grow up in Italy, beauty always matters. Then, you have the urge to tell others about it. You organize a workshop at a conference in musculoskeletal image analysis and introduce marvelous novel tools for computational research: “You know? Nowadays we can do open science easily because somebody has created Jupyter Notebook, Binder, new Python libraries!” And then, together with some friends, you write a proposal for a Jupyter Community Workshop, and–unbelievable!–you get it. Initially, you think you are just organizing a workshop. But somehow, the number of people interested becomes larger and larger, and you find yourself co-founding and coordinating what is now the Open and Reproducible Musculoskeletal Imaging Research (ORMIR) Community. In ORMIR’s Python packages, the examples are in Jupyter Notebook, the documentation is in Jupyter Book. Sooner or later, we will have our JupyterHubs too; it’s just a matter of time. In parallel, life gives you some professional challenges. So you start teaching Python online, one-to-one. You look for material that fits your style, but you cannot find any. So you start teaching it your way, using Jupyter Notebook in JupyterLab and it works. Students find it easy, independent of their background. The teaching material seems to work, so you transcribe it into a book entitled Learn Python with Jupyter, and you make it open. At the same time, you are obsessed with keeping up to date. You go to JupyterCon, meet the people, watch their YouTube videos, participate in online meetings. And then, there is a job posting for a community manager. And you are like: “Nah, they will never hire me”, so you set it aside. But then somebody out of the blue tells you to apply because “100% of lottery winners played!”. So you give it a chance, and here I am: my name is Serena Bonaretti and I am the new JupyterHub and Jupyter Book community manager!

In this new role, I am very much looking forward to working with the JupyterHub and Jupyter Book communities! I will collaborate with maintainers to understand and document projects’ priorities and define the best processes to achieve their vision. I will support contributors in finding their way into the projects and make the real-world impact that they hope to achieve. I will dialogue with users to make sure that the tools we create are useful for their work. And I will share our activities monthly and what we learn with anybody interested, so that we can all benefit from this journey!

And you? How did you start using the tools of the Jupyter ecosystem? Do you work in JupyterHub? Have you ever written a Jupyter Book? Would you like to be a contributor? Are you an enthusiastic user? Write me on Discord (Jupyter Book/MyST) or Zulip (JupyterHub). The JupyterHub and Jupyter Book communities and I are looking forward to getting to know you!

Introducing “Learn Python with Jupyter”

2023-04-28T06:14:00+00:00

Learning a programming language is about learning a language. When learning a new language (such as French, Korean, etc.), we commonly learn two key components: a way of thinking–which is typically connected to a culture–and grammar–that is, how to arrange elements of speech (verbs, nouns, etc.) to create coherent sentences. Each aspect usually has a dedicated book. We study how to greet people, order a coffee, and ask for directions on the streets–that is, how to behave and think in that language–in course books. At the same time, we deepen and refine our grammar knowledge in grammar books, where chapters explain elements of speech providing detailed rules, list of exceptions, and short disjoint examples.

Books to learn programming languages typically follow a grammar book format. Each chapter covers a different aspect of coding grammar (such as data structures, loops, etc.) with brief examples that frequently use mathematics. Finding course books that gradually teach to think in a coding language is rare.

Learn Python with Jupyter is intended as a course book to help develop computational thinking while learning to code in Python. Similar to language course books, each chapter starts with a story (or a few long examples) to introduce a new topic, while providing context and favoring pattern memorization (Fig. 1A). In coding, stories are possible thanks to Jupyter notebooks, which allow seamless integration of narrative and code in the same file. Each story is accompanied by code pronunciation, as pronunciation affects thinking (e.g., = should be pronounced as “is assigned”, not as “equals”). Code and concepts presented in the story are then thoroughly described and definitions are highlighted in colored boxes (Fig. 1B). Learners have the possibility to elaborate on the presented topic with theoretical quizzes and exercises (True or false, Match the sentence halves, etc.) as well as coding exercises of increasing difficulty (Fig. 1C). Chapters finish with bullet-point recaps reformulating the latest concepts (Fig. 1D), and with a “More in depth session” addressing error management, good practice, and coding suggestions (Fig. 1E).

The order of topics presented in Learn Python with Jupyter supports development of computational thinking by progressing from spoken language to abstract symbols and constructs. The very first chapters (1–5) introduce strings as English words, followed by lists of strings, if/else conditions, and basic methods for list manipulation. The following chapters (6–12) explain slicing as an abstract alternative to list methods and the for loop as an automatic way to slice list elements. These chapters mark the transition from concrete to abstract thinking, after which more complex topics are explained. The remaining chapters (13–38) introduce numbers, more coding syntax and constructs (e.g., while loop, dictionaries, functions, etc.), and typical aspects of coding, such as “divide and conquer”, “debugging”, etc.

Figure 1. Chapter sections in Learn Python with Jupyter

Learn Python with Jupyter aims at enabling absolute beginners–who have never been exposed to any programming language–to learn coding. This aim is supported by the conviction that everybody, regardless of social circumstances or gender, has the right to become digitally literate and acquire awareness of the increasingly digitalized world we live in. The book is free and is currently halfway finished. A new chapter is released on learnpythonwithjupyter.com every 4–6 weeks along with the related Jupyter notebook.

Report on the Jupyter Community Workshop

2022-11-30T16:40:00+00:00

“Building the Jupyter Community in Musculoskeletal Imaging Research”

Authors: Serena Bonaretti, Donnie Cameron, Michael Kuczynski, and Gianluca Iori, on behalf of all the participants to the workshop

On June 9–11, 2022, 25 researchers in Quantitative Musculoskeletal (MSK) Imaging met in Maastricht (The Netherlands) for a three-day Jupyter Community Workshop (JCW). Our aim was twofold: to learn tools for open and reproducible research in medical imaging — such as Python, Jupyter Notebook, and ITK—and to create open source software for MSK imaging research. During the three days, we had parallel sessions of tutorials and coding, we hosted speakers to give us inspiration and teach us how to do open and reproducible research, and we discussed the next steps for our community. You can find our workshop page here. In this blog post, we’ll give a detailed breakdown of what happened at the workshop.

Tutorials

As some of us did not have experience with the Jupyter/Python ecosystem, we organized tutorials at various levels of difficulty:

Introduction to the Jupyter ecosystem and Python, and to Pandas and Matplotlib (tutorial material here);
Introduction to the biomedical image analysis and visualization toolkit ITK (tutorial material here).

Computational projects

We split into three groups based on our research interest, and we worked on three computational projects, all of which are now close to publication:

Ciclope: a Python package that processes micro computed tomography images to generate micro finite element models. The software allows the user to create reproducible and fully open-source pipelines for simulating the mechanical behavior of trabecular bone using the finite element method. The Jupyter notebook examples created within the workshop illustrate a complete pipeline from 3D image preprocessing to finite element model generation, solution, and postprocessing.
ORMIR_XCT: a Python package for computing joint space parameters and trabecular bone microarchitecture in second generation high resolution peripheral quantitative computed tomography (HR-pQCT, XtremeCT2, Scanco Medical) images. This package contains all necessary scripts to convert Scanco image data (AIM/ISQ) and perform standard bone and joint analyses that are currently performed using the scanner manufacturer’s built-in software. A Jupyter notebook was developed during this workshop to demonstrate this package’s functionality by perform joint space analysis of a sample finger joint.
MuscleBIDS: a Python package for reading and writing a standardized data format for muscle MR imaging that is based on BIDS. Given the diversity of imaging data formats that exists internationally, such standardized formats are crucial for obtaining reproducible and comparable results. In addition to continuing the development of the tool to support more image contrasts and scanner vendors, a Jupyter notebook was produced, showcasing the current functionality of the package.

Invited speakers

We were excited to have two extraordinary speakers in person at our workshop:

Lorena Barba, who demonstrated the crucial role of open science in solving global problems (e.g. during the COVID pandemic) and showed current and future infrastructure for conducting open science (find her presentation here);
Chris Holdgraf, who talked about current efforts in developing and deploying Jupyter in research and education with different tools and adaptations depending on field requirements, e.g. JupyterLab, Jupyter Book (with MyST), and JupyterHub (find his presentation here).

Community discussion (i.e. what we learned and what’s next)

We dedicated the last session of the workshop to a free exchange of ideas, focusing on what we learned from this experience and defining our next steps:

Workshop organization. The choice of the venue was successful: staying in a modern, tech-style venue with space for work and play got us into the right mindset for coding and learning. On the other hand, we should have organized tutorials and coding sessions sequentially, as the parallel organization did not favor those who were eager to both learn and contribute to the computational projects. In addition, some new members of the community had difficulties finding a way to contribute to the computational projects, which had already been started by some labs;
Community organization. We understood that we needed to structure our community in a more formal way. Thus, we established a Technical Advisory Board, who are going to create guidelines for software homogenization and standardization, and a Community Advisory Board, whose task is to organize and share information about the community, and structure material for newcomers so that they can easily integrate into the community. Finally, we also decided to formally name our community the ‘Open and Reproducible Musculoskeletal Imaging Research’ (ORMIR) community;
Next steps. The Community and Technical Advisory Boards are already working to improve the community website, and create learning material and coding guidelines. We applied for a grant to continue developing our community, and we are looking for more funding opportunities to expand and maintain our projects.

In conclusion, the Jupyter Community Workshop was a powerful collective learning experience that inspired us to start, develop, and continue our journey towards open and reproducible research in quantitative musculoskeletal imaging.

This event would not have been possible without the generous support provided by Bloomberg and Amazon Web Services. If your organization would like to support programs like the Jupyter Community Workshops please contact NumFOCUS at info@numfocus.org