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extra heading # to institutional faq for sidebar

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Chris Holdgraf
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docs/source/getting-started/institutional-faq.md
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@@ -3,9 +3,9 @@
This page contains common questions from users of JupyterHub,
broken down by their roles within organizations.
# For all
## For all
## Is it appropriate for adoption within a larger institutional context?
### Is it appropriate for adoption within a larger institutional context?
Yes! JupyterHub has been used at-scale for large pools of users, as well
as complex and high-performance computing. For example, UC Berkeley uses
@@ -14,7 +14,7 @@ JupyterHub for its Data Science Education Program courses (serving over
to scalable cloud computing with Dask. JupyterHub is stable customizable
to the use-cases of large organizations.
## I keep hearing about Jupyter Notebook, JupyterLab, and now JupyterHub. What’s the difference?
### I keep hearing about Jupyter Notebook, JupyterLab, and now JupyterHub. What’s the difference?
Here is a quick breakdown of these three tools:
@@ -29,23 +29,23 @@ Here is a quick breakdown of these three tools:
It also connects them with infrastructure those users wish to access. It can provide
remote access to Jupyter Notebooks and Jupyter Lab for many people.
# For management
## For management
## Briefly, what problem does JupyterHub solve for us?
### Briefly, what problem does JupyterHub solve for us?
JupyterHub provides a shared platform for data science and collaboration.
It allows users to utilize familiar data science workflows (such as the scientific python stack,
the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also allows administrators
some control over access to resources, security, environments, and authentication.
## Is JupyterHub mature? Why should we trust it?
### Is JupyterHub mature? Why should we trust it?
Yes - the core JupyterHub application recently
reached 1.0 status, and is considered stable and performant for most institutions.
JupyterHub has also been deployed (along with other tools) to work on
scalable infrastructure, large datasets, and high-performance computing.
## Who else uses JupyterHub?
### Who else uses JupyterHub?
JupyterHub is used at a variety of institutions in academia,
industry, and government research labs. It is most-commonly used by two kinds of groups:
@@ -68,7 +68,7 @@ Here are a sample of organizations that use JupyterHub:
See the [Gallery of JupyterHub deployments](../gallery-jhub-deployments.md) for
a more complete list of JupyterHub deployments at institutions.
## How does JupyterHub compare with hosted products, like Google Colaboratory, RStudio.cloud, or Anaconda Enterprise?
### How does JupyterHub compare with hosted products, like Google Colaboratory, RStudio.cloud, or Anaconda Enterprise?
JupyterHub puts you in control of your data, infrastructure, and coding environment.
In addition, it is vendor neutral, which reduces lock-in to a particular vendor or service.
@@ -83,9 +83,9 @@ computational hardware. It can also be deployed anywhere - on enterprise cloud i
High-Performance-Computing machines, on local hardware, or even on a single laptop, which
is not possible with most other tools for shared interactive computing.
# For IT
## For IT
## How would I set up JupyterHub on institutional hardware?
### How would I set up JupyterHub on institutional hardware?
That depends on what kind of hardware you've got. JupyterHub is flexible enough to be deployed
on a variety of hardware, including in-room hardware, on-prem clusters, cloud infrastructure,
@@ -103,7 +103,7 @@ that we currently suggest are:
lightweight computational resources.
## Does JupyterHub run well in the cloud?
### Does JupyterHub run well in the cloud?
Yes - most deployments of JupyterHub are run via cloud infrastructure and on a variety of cloud providers.
Depending on the distribution of JupyterHub that you'd like to use, you can also connect your JupyterHub
@@ -117,7 +117,7 @@ project for distributed computing.
The Z2JH Helm Chart also has some functionality built in for auto-scaling your cluster up and down
as more resources are needed - allowing you to utilize the benefits of a flexible cloud-based deployment.
## Is JupyterHub secure?
### Is JupyterHub secure?
The short answer: yes. JupyterHub as a standalone application has been battle-tested at an institutional
level for several years, and makes a number of "default" security decisions that are reasonable for most
@@ -138,7 +138,7 @@ If you are worried about security, don't hesitate to reach out to the JupyterHub
individuals with experience running secure JupyterHub deployments.
## Does JupyterHub provide computing or data infrastructure?
### Does JupyterHub provide computing or data infrastructure?
No - JupyterHub manages user sessions and can *control* computing infrastructure, but it does not provide these
things itself. You are expected to run JupyterHub on your own infrastructure (local or in the cloud). Moreover,
@@ -146,7 +146,7 @@ JupyterHub has no internal concept of "data", but is designed to be able to comm
(again, either locally or remotely) for use within interactive computing sessions.
## How do I manage users?
### How do I manage users?
JupyterHub offers a few options for managing your users. Upon setting up a JupyterHub, you can choose what
kind of **authentication** you'd like to use. For example, you can have users sign up with an institutional
@@ -158,7 +158,7 @@ Moreover, the *active* users on a JupyterHub can be found on the administrator's
gives you the abiltiy to stop or restart kernels, inspect user filesystems, and even take over user
sessions to assist them with debugging.
## How do I manage software environments?
### How do I manage software environments?
A key benefit of JupyterHub is the ability for an administrator to define the environment(s) that users
have access to. There are many ways to do this, depending on what kind of infrastructure you're using for
@@ -170,7 +170,7 @@ an environment by installing packages to a shared folder that exists on the path
own list of Docker images that users can select from, and can also control things like the amount of
RAM available to users, or the types of machines that their sessions will use in the cloud.
## How does JupyterHub manage computational resources?
### How does JupyterHub manage computational resources?
For interactive computing sessions, JupyterHub controls computational resources via a **spawner**.
Spawners define how a new user session is created, and are customized for particular kinds of
@@ -183,14 +183,14 @@ scalable or high-performance resources from within their JupyterHub sessions. Th
how those resources are controlled is taken care of by the non-JupyterHub application.
## Can JupyterHub be used with my high-performance computing resources?
### Can JupyterHub be used with my high-performance computing resources?
Yes - JupyterHub can provide access to many kinds of computing infrastructure.
Especially when combined with other open-source schedulers such as Dask, you can manage fairly
complex computing infrastructure from the interactive sessions of a JupyterHub. For example
[see the Dask HPC page](https://docs.dask.org/en/latest/setup/hpc.html).
## How much resources do user sessions take?
### How much resources do user sessions take?
This is highly configurable by the administrator. If you wish for your users to have simple
data analytics environments for prototyping and light data exploring, you can restrict their
@@ -198,15 +198,15 @@ memory and CPU based on the resources that you have available. If you'd like you
to serve as a gateway to high-performance compute or data resources, you may increase the
resources available on user machines, or connect them with computing infrastructure elsewhere.
## Can I customize the look and feel of a JupyterHub?
### Can I customize the look and feel of a JupyterHub?
JupyterHub provides some customization of the graphics displayed to users. The most common
modification is to add custom branding to the JupyterHub login page, loading pages, and
various elements that persist across all pages (such as headers).
# For Technical Leads
## For Technical Leads
## Will JupyterHub “just work” with our team's interactive computing setup?
### Will JupyterHub “just work” with our team's interactive computing setup?
Depending on the complexity of your setup, you'll have different experiences with "out of the box"
distributions of JupyterHub. If all of the resources you need will fit on a single VM, then
@@ -219,7 +219,7 @@ In general, the base JupyterHub deployment is not the bottleneck for setup, it i
your JupyterHub with the various services and tools that you wish to provide to your users.
## How well does JupyterHub scale? What are JupyterHub's limitations?
### How well does JupyterHub scale? What are JupyterHub's limitations?
JupyterHub works well at both a small scale (e.g., a single VM or machine) as well as a
high scale (e.g., a scalable Kubernetes cluster). It can be used for teams as small a 2, and
@@ -228,7 +228,7 @@ infrastructure on which it is deployed. JupyterHub has been designed to be light
flexible, so you can tailor your JupyterHub deployment to your needs.
## Is JupyterHub resilient? What happens when a machine goes down?
### Is JupyterHub resilient? What happens when a machine goes down?
For JupyterHubs that are deployed in a containerized environment (e.g., Kubernetes), it is
possible to configure the JupyterHub to be fairly resistant to failures in the system.
@@ -238,13 +238,13 @@ seamlessly connect with the user database and the system will return to normal.
Again, the details of your JupyterHub deployment (e.g., whether it's deployed on a scalable cluster)
will affect the resiliency of the deployment.
## What interfaces does JupyterHub support?
### What interfaces does JupyterHub support?
Out of the box, JupyterHub supports a variety of popular data science interfaces for user sessions,
such as JupyterLab, Jupyter Notebooks, and RStudio. Any interface that can be served
via a web address can be served with a JupyterHub (with the right setup).
## Does JupyterHub make it easier for our team to collaborate?
### Does JupyterHub make it easier for our team to collaborate?
JupyterHub provides a standardized environment and access to shared resources for your teams.
This greatly reduces the cost associated with sharing analyses and content with other team
@@ -259,7 +259,7 @@ rendered as [voila dashboards](https://voila.readthedocs.io/en/stable/) with tho
familiar with programming, or create publicly-available interactive analyses to allow others to
interact with your work.
## Can I use JupyterHub with R/RStudio or other languages and environments?
### Can I use JupyterHub with R/RStudio or other languages and environments?
Yes, Jupyter is a polyglot project, and there are over 40 community-provided kernels for a variety
of languages (the most common being Python, Julia, and R). You can also use a JupyterHub to provide