About this document
Nowadays, many institutions run a JupyterHub server, providing their members with easy access to Jupyter-based virtual environments (a.k.a. notebook servers), preinstalled with a stack of computational software, tailored to the typical needs of the institution’s members. Meanwhile, since a few years ago, Binder lets any user on the internet define, run, and share temporary virtual environments equipped with an arbitrary software stack (examples).
In Fall 2017, Binder was revamped as BinderHub, a lightweight layer on top of JupyterHub. The next step in this convergence is to bring together the best of both worlds: think persistent authenticated Binder; or repo2docker enabled JupyterHub. For now, let’s call them versatile JupyterHub deployments.
This document brainstorms this convergence process: it sets up the ground with a scenario and assumptions for a typical institution-wide JupyterHub deployment, proposes specifications from a user perspective, and describes some typical use cases that would be enabled by such specifications. It further discusses security aspects and what remains to be implemented, before concluding with more advanced features and open questions.
This document started as a collection of private notes reflecting on in-development JupyterHub deployment at Paris-Saclay and EGI respectively, with some additional contributions. They were largely informed by many discussions at March 2018’s JupyterHub coding sprint in Orsay that involved dev-ops of those deployments and two of the main JupyterHub and BinderHub devs: Min Ragan Kelley and Chris Holdgraf. It was also inspired by some of cocalc features. Silly ideas reflecting here are mine, hard work is theirs; thank you all!!!
This document is meant for brainstorming; please hop in and edit.
An institution – typically a university, a national lab, a transnational research infrastructure such as the European XFEL, or transational infrastructure provider like EGI – wishes to provide its members and users with a Jupyter service.
The service lets user spawn and access personal or collaborative virtual environments: namely a web interface to a light weight virtual machine, in which they can use Jupyter notebooks, run calculations, etc. In the remainder of this document we will use JupyterHub’s terminology and call such virtual environments notebook servers.
To cater for a large variety of use cases in teaching and research, the main aim of the upcoming specifications is to make the service as versatile as possible. In particular, it should empower the users to customize the service (available software stack, storage setup, …), without a need for administrator intervention.
The institution has access to:
An authentication service (Single Sign-On)
Examples: Paris-Sud’ Adonis internal SSO, the federated “Recherche et Enseignement Supérieur” authentication service of Renater, EGI CheckIn, …
Examples: a local cluster, access to a externalized cloud (GC, AWS, Azure, …)
A shared volume service using the above authentication service
E.g. a local NextCloud service, or …
(Optional) a forge
Examples: a local gitlab service, github, … if private repositories are needed, the forge presumably will need the same authentication service
Specifications / User Story
Main page of the service
After authentication, the user faces a page that is similar to binder’s main page:
A form to describe and launch the desired persistent notebook server.
For the sake of simplicity, the form could optionally start hidden, and be replaced by two items: “Choose preconfigured notebook server” / “Create my own notebook server”.
Links to documentation
Warnings about potential security concerns, to inform the user choices.
Alternatively, such warnings could be displayed in a later security confirmation dialog “with the given configuration, a malicious image/collaborator could …; do you trust it? Proceed/Edit Configuration/Cancel/Don’t ask again”
Institutional credits (service provided by …)
The form consists of:
- The usual binder items:
- the description of the computing environment: a repo-to-docker-style git repo+branch+…
- the file/url to open on startup
- a UI to get a URL/badge referencing the machine
- Persistence and access options:
server_name: name to give to the server
If server_name is not specified, create a random server name?
mount options: [[mount point, volume url], […]]
This assumes that the user has appropriate credentials to access the given volumes through the authentication service
collaborators=[….]: (optional) a white list of other users of this jupyterhub that can access this server
a flag allowing public ‘temporary read-only’ access (meaning that the container and all changes are thrown away at the end of the session; and that any ‘mounted’ data sources are read-only during the session)
- credentials: whether to pass the user credentials into the container (as environment variable, or file)
- resources scaling (optional): memory, number of processors, duration (time or time of inactivity after which the server will be automatically stopped / destroyed)
Behavior upon clicking Launch:
- If a notebook server with the given name already exists and the parameters are not changed (or not set): connect to that server, restarting it if needed
- If the parameters have been changed, update the existing server when possible? Or should the user just delete the server?
- Otherwise, create and launch
Behavior upon following a server URL/badge:
- Display the authentication page (if not yet authenticated)
- Display a security confirmation dialog as above (if origin is not within the jupyterhub), with a description of the configuration and origin.
- As above after clicking “Launch”
Some use cases
Local binder (better name? [Binder@home?])
- Luc, a researcher, discovered a nice computing environment on Binder. Maybe a notebook demonstrating an interesting workflow to analyze data. He wants to use it more intensively on his own data.
- Lucy has found a notebook & binder environment published with a paper, and she wants to re-execute the notebook to reproduce the published results and start her research in the field. However, no binder (compute) resources are available in the cloud. The computation takes 20 minutes on a standard PC and she would like to run this calculation on her local server.
They recreate the same environment on their local server (for example by just changing the server name in the binder URL).
More advanced scenario to explore: Lucy would like to use her Desktop PC because that resource is readily available and idles 99% of the time.
Easy sharing of computational environments
- Sarah, a power user, is using some specialized stack of software on a daily basis; maybe she authored some of it. She wants her colleagues in her lab to try out that software.
- Paul organizes a training session for his colleagues.
- Alice has authored notebooks that she wants to share with her colleagues. Maybe the notebooks automatize some complicated processes and present them in the form of interactive web applications built using Jupyter widgets (demo). Think of a wizard to setup parameters and data, run a computation, and visualize the results.
They prepare a notebook server with the appropriate software stack installed configured and access to the user’s shared home directory. Maybe they provide some document. They then just have to share the URL with their colleagues. No lengthy software installation. The colleagues can then start working right away, in their own environment, using their own data, saving their work in their home directory.
In all cases, the explicit description of the computing environment (and the use of open source software!) eases:
- the publication of the same computational environment / notebooks elsewhere, e.g. on a public Binder;
- the installation the same software on the user’s personal computer.
Scenario: Alice and Bob want to collaborate on some data analysis.
They create a shared volume. Then either:
- They set up each their own notebook server, and let them share the same volume.
- Alice sets up a single server, with Bob as collaborator. Within the server, they are considered as the same user.
At this stage, they should not edit the same notebook simultaneously. However the stable version of JupyterLab, due sometime in 2018, should enable real-time collaboration in both setups, thanks to a CRDT file format for notebooks.
Scenario: using the server for a class’ computer labs and assignments
- Full customizability of the computing environment by the teacher;
- Support for live manipulation of the class notes;
- Support for submission, collection and auto-grading of assignments;
- Access from the computer labs or outside (home, …);
- Possibility to either use the server, needing only a web browser (no software installation required; supports phones, tablets, …), or install and run the software locally.
- A JupyterHub instance, configured as above, accessible from the teachers and students;
- A forge such as gitlab or github, accessible from JupyterHub
- A shared drive service (e.g. next cloud/nsf/…), serving home directories, and letting teachers setup shared volumes
- A shared authentication (e.g. SSO), so that notebook servers in JupyterHub can access the shared drive.
- Some web server
Procedure for the teacher(s):
- Set up a shared volume for the whole class
Prepare a computing environment in a git repository on the forge.
Typically includes: computational software, [nbgrader] + configuration, …
- Prepare the course material typically in a git repository on the forge (the same one or another)
- Use JupyterHub’s form UI to setup (and test) a full description of the student’s notebook servers, with mounting of the home directory (or subdirectory thereof?) and shared volume. Possibly add the teacher(s) as collaborator(s) ??? Get the corresponding URL.
- Possibly prepare a variant thereof for teachers of the class.
Set up a web page for the class, with hyperlink(s) to the above URL.
There can typically be an hyperlink for each session pointing directly to the exercises for that particular session.
Fetching the class material:
- Option 1: manually download from the web (wget in the terminal, or web upload, …) or shared volume
- Option 2: use nbgitpuller from the command line
- Option 3: use nbgrader, either from the command line or with the UI to get the files from the shared volume
- Option 4: automatize the above using a notebook server extension such as that for nbgitpuller
- Use nbgrader, either from the command line or with the UI to push the files to the shared volume
To explore: integration with local e-learning platforms like Moodle, typically using LTI, in particular for class management and grades reporting. There already exists an LTI authenticator for Jupyter.
A malicious image description, image, or collaborator can:
- Take any action within the image being built or within the notebook server
- Waste computing resources (cpu/memory);
- With internet: connect to any website, without specific priviledges (e.g. participate to a DOS attack); abuse computing resources, e.g. for bitcoining. The image building almost certainly needs internet access.
- With persistent storage and internet access: access and leak information from the storage; e.g. private information from the user;
- With read-write persistent storage: corrupt the storage (e.g. the user’s home directory)
- With credentials: take any action on behalf of the user in any service that use the same authentication.
Most of the features are already there. Here are some of the missing steps:
- Extending binder’s form as described above;
- Implementing the logic to mount shared volumes;
Instructions / scripts for setting up a local docker registry;
The current Binder installation tutorial assumes that a docker registry is already available; e.g. that provided by google cloud services.
For a smaller setup using the same host for both building images and running notebook servers, no docker registry is needed. In this case, JupyterHub could just run repo2docker locally before launching the notebook server. repo2docker however does not implement image caching; so a simplified version of the image cache mechanism of Binder needs to be implemented.
It should be noted that there is basically no coupling between JupyterHub/Binder and Jupyter. The former is merely a general purpose web service for provisioning web-based virtual environments. For example, JupyterHub/Binder has also been used to serve R-Studio based virtual environments. Reciprocally, there are alternative services to do such provisioning from which to get inspiration, like Simulagora.
Advanced features & open questions
The main form could contain an additional item:
- URL input / dropdown menu to choose another jupyterhub instances to redirect to.
- A user finds a nice image on binder; he wants to customize it to run it on his institution’s jupyterhub; possibly adding persistent storage to it. Or reciprocally: a user wants to share on binder a local image of his.
- An institution wants to advertise other jupyterhub instances; this could e.g. be used by a single entry point for federating a collection of instances (e.g. all French academic JupyterHub’s).
Marketplace of images
With the URL mechanism, any (power) user can prepare a dedicated image and share it with his collaborators. Images can be more or less specific: from just a computing environment to a fully specified machine, with mount points, …
Thanks to the above there is no need for a tightly coupled Marketplace. Nevertheless it may be useful to have one location (or more) for collecting and publicizing links to popular images. Some minimal coupling may be needed if one would want to sort the images according to their popularity.
Note: at this stage, a user cannot produce an image by setting up a machine “by hand” and save its state. The construction must be fully scripted. On the plus side, this encourages users to script their images, making them more reproducible.
National and international initiatives such as the European Open Science Cloud may help providing such a catalog of relevant Jupyter notebooks/images.
Default volume configuration
- Choose good defaults, if at all possible compatible with binder. Main question: where should the files provided by the binder environment be copied? In a subdirectory of the persistent home? In the home directory, with the persistent home being mounted in a subdirectory thereof?
Intensive use and resource management / accounting
The above has been written with casual use in mind. For extensive use, some form of accounting and controlling of the resources used would be needed. For example, for LAL’s cloud we may want to have some form of bridge between the OpenStack dashboard and the hub. UI to be designed. Could the user provision a machine using the dashboard, and then specify on JupyterHub that the container shall be run on that machine?
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