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10
docs/source/getting-started/authenticators-users-basics.md
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@@ -9,7 +9,6 @@ with an account and password on the system will be allowed to login.
You can restrict which users are allowed to login with a set,
`Authenticator.allowed_users`:
```python
c.Authenticator.allowed_users = {'mal', 'zoe', 'inara', 'kaylee'}
```
@@ -28,6 +27,7 @@ A set of initial admin users, `admin_users` can configured be as follows:
```python
c.Authenticator.admin_users = {'mal', 'zoe'}
```
Users in the admin set are automatically added to the user `allowed_users` set,
if they are not already present.
@@ -44,8 +44,8 @@ c.PAMAuthenticator.admin_groups = {'wheel'}
Since the default `JupyterHub.admin_access` setting is False, the admins
do not have permission to log in to the single user notebook servers
owned by *other users*. If `JupyterHub.admin_access` is set to True,
then admins have permission to log in *as other users* on their
owned by _other users_. If `JupyterHub.admin_access` is set to True,
then admins have permission to log in _as other users_ on their
respective machines, for debugging. **As a courtesy, you should make
sure your users know if admin_access is enabled.**
@@ -115,5 +115,5 @@ To set a global password, add this to the config file:
c.DummyAuthenticator.password = "some_password"
```
[PAM]: https://en.wikipedia.org/wiki/Pluggable_authentication_module
[OAuthenticator]: https://github.com/jupyterhub/oauthenticator
[pam]: https://en.wikipedia.org/wiki/Pluggable_authentication_module
[oauthenticator]: https://github.com/jupyterhub/oauthenticator

12
docs/source/getting-started/config-basics.md
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@@ -56,7 +56,7 @@ To display all command line options that are available for configuration:
```
Configuration using the command line options is done when launching JupyterHub.
For example, to start JupyterHub on ``10.0.1.2:443`` with https, you
For example, to start JupyterHub on `10.0.1.2:443` with https, you
would enter:
```bash
@@ -88,13 +88,13 @@ meant as illustration, are:
## Run the proxy separately
This is *not* strictly necessary, but useful in many cases. If you
This is _not_ strictly necessary, but useful in many cases. If you
use a custom proxy (e.g. Traefik), this also not needed.
Connections to user servers go through the proxy, and *not* the hub
itself. If the proxy stays running when the hub restarts (for
Connections to user servers go through the proxy, and _not_ the hub
itself. If the proxy stays running when the hub restarts (for
maintenance, re-configuration, etc.), then use connections are not
interrupted. For simplicity, by default the hub starts the proxy
interrupted. For simplicity, by default the hub starts the proxy
automatically, so if the hub restarts, the proxy restarts, and user
connections are interrupted. It is easy to run the proxy separately,
connections are interrupted. It is easy to run the proxy separately,
for information see [the separate proxy page](../reference/separate-proxy).

7
docs/source/getting-started/faq.md
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@@ -1,6 +1,5 @@
# Frequently asked questions
### How do I share links to notebooks?
In short, where you see `/user/name/notebooks/foo.ipynb` use `/hub/user-redirect/notebooks/foo.ipynb` (replace `/user/name` with `/hub/user-redirect`).
@@ -11,9 +10,9 @@ Your first instinct might be to copy the URL you see in the browser,
e.g. `hub.jupyter.org/user/yourname/notebooks/coolthing.ipynb`.
However, let's break down what this URL means:
`hub.jupyter.org/user/yourname/` is the URL prefix handled by *your server*,
`hub.jupyter.org/user/yourname/` is the URL prefix handled by _your server_,
which means that sharing this URL is asking the person you share the link with
to come to *your server* and look at the exact same file.
to come to _your server_ and look at the exact same file.
In most circumstances, this is forbidden by permissions because the person you share with does not have access to your server.
What actually happens when someone visits this URL will depend on whether your server is running and other factors.
@@ -22,7 +21,7 @@ A typical situation is that you have some shared or common filesystem,
such that the same path corresponds to the same document
(either the exact same document or another copy of it).
Typically, what folks want when they do sharing like this
is for each visitor to open the same file *on their own server*,
is for each visitor to open the same file _on their own server_,
so Breq would open `/user/breq/notebooks/foo.ipynb` and
Seivarden would open `/user/seivarden/notebooks/foo.ipynb`, etc.

38
docs/source/getting-started/institutional-faq.md
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@@ -18,14 +18,14 @@ to the use-cases of large organizations.
Here is a quick breakdown of these three tools:
* **The Jupyter Notebook** is a document specification (the `.ipynb`) file that interweaves
- **The Jupyter Notebook** is a document specification (the `.ipynb`) file that interweaves
narrative text with code cells and their outputs. It is also a graphical interface
that allows users to edit these documents. There are also several other graphical interfaces
that allow users to edit the `.ipynb` format (nteract, Jupyter Lab, Google Colab, Kaggle, etc).
* **JupyterLab** is a flexible and extendible user interface for interactive computing. It
- **JupyterLab** is a flexible and extendible user interface for interactive computing. It
has several extensions that are tailored for using Jupyter Notebooks, as well as extensions
for other parts of the data science stack.
* **JupyterHub** is an application that manages interactive computing sessions for **multiple users**.
- **JupyterHub** is an application that manages interactive computing sessions for **multiple users**.
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.
@@ -50,20 +50,20 @@ scalable infrastructure, large datasets, and high-performance computing.
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:
* Small teams (e.g., data science teams, research labs, or collaborative projects) to provide a
- Small teams (e.g., data science teams, research labs, or collaborative projects) to provide a
shared resource for interactive computing, collaboration, and analytics.
* Large teams (e.g., a department, a large class, or a large group of remote users) to provide
- Large teams (e.g., a department, a large class, or a large group of remote users) to provide
access to organizational hardware, data, and analytics environments at scale.
Here are a sample of organizations that use JupyterHub:
* **Universities and colleges**: UC Berkeley, UC San Diego, Cal Poly SLO, Harvard University, University of Chicago,
- **Universities and colleges**: UC Berkeley, UC San Diego, Cal Poly SLO, Harvard University, University of Chicago,
University of Oslo, University of Sheffield, Université Paris Sud, University of Versailles
* **Research laboratories**: NASA, NCAR, NOAA, the Large Synoptic Survey Telescope, Brookhaven National Lab,
- **Research laboratories**: NASA, NCAR, NOAA, the Large Synoptic Survey Telescope, Brookhaven National Lab,
Minnesota Supercomputing Institute, ALCF, CERN, Lawrence Livermore National Laboratory
* **Online communities**: Pangeo, Quantopian, mybinder.org, MathHub, Open Humans
* **Computing infrastructure providers**: NERSC, San Diego Supercomputing Center, Compute Canada
* **Companies**: Capital One, SANDVIK code, Globus
- **Online communities**: Pangeo, Quantopian, mybinder.org, MathHub, Open Humans
- **Computing infrastructure providers**: NERSC, San Diego Supercomputing Center, Compute Canada
- **Companies**: Capital One, SANDVIK code, Globus
See the [Gallery of JupyterHub deployments](../gallery-jhub-deployments.md) for
a more complete list of JupyterHub deployments at institutions.
@@ -95,14 +95,13 @@ The most common way to set up a JupyterHub is to use a JupyterHub distribution,
and opinionated ways to set up a JupyterHub on particular kinds of infrastructure. The two distributions
that we currently suggest are:
* [Zero to JupyterHub for Kubernetes](https://z2jh.jupyter.org) is a scalable JupyterHub deployment and
- [Zero to JupyterHub for Kubernetes](https://z2jh.jupyter.org) is a scalable JupyterHub deployment and
guide that runs on Kubernetes. Better for larger or dynamic user groups (50-10,000) or more complex
compute/data needs.
* [The Littlest JupyterHub](https://tljh.jupyter.org) is a lightweight JupyterHub that runs on a single
- [The Littlest JupyterHub](https://tljh.jupyter.org) is a lightweight JupyterHub that runs on a single
single machine (in the cloud or under your desk). Better for smaller usergroups (4-80) or more
lightweight computational resources.
### Does JupyterHub run well in the cloud?
Yes - most deployments of JupyterHub are run via cloud infrastructure and on a variety of cloud providers.
@@ -123,9 +122,9 @@ The short answer: yes. JupyterHub as a standalone application has been battle-te
level for several years, and makes a number of "default" security decisions that are reasonable for most
users.
* For security considerations in the base JupyterHub application,
- For security considerations in the base JupyterHub application,
[see the JupyterHub security page](https://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html)
* For security considerations when deploying JupyterHub on Kubernetes, see the
- For security considerations when deploying JupyterHub on Kubernetes, see the
[JupyterHub on Kubernetes security page](https://zero-to-jupyterhub.readthedocs.io/en/latest/security.html).
The longer answer: it depends on your deployment. Because JupyterHub is very flexible, it can be used
@@ -137,15 +136,13 @@ If you are worried about security, don't hesitate to reach out to the JupyterHub
[Jupyter Community Forum](https://discourse.jupyter.org/c/jupyterhub). This community of practice has many
individuals with experience running secure JupyterHub deployments.
### Does JupyterHub provide computing or data infrastructure?
No - JupyterHub manages user sessions and can *control* computing infrastructure, but it does not provide these
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,
JupyterHub has no internal concept of "data", but is designed to be able to communicate with data repositories
(again, either locally or remotely) for use within interactive computing sessions.
### How do I manage users?
JupyterHub offers a few options for managing your users. Upon setting up a JupyterHub, you can choose what
@@ -154,7 +151,7 @@ email address, or choose a username / password when they first log-in, or offloa
another service such as an organization's OAuth.
The users of a JupyterHub are stored locally, and can be modified manually by an administrator of the JupyterHub.
Moreover, the *active* users on a JupyterHub can be found on the administrator's page. This page
Moreover, the _active_ users on a JupyterHub can be found on the administrator's page. This page
gives you the abiltiy to stop or restart kernels, inspect user filesystems, and even take over user
sessions to assist them with debugging.
@@ -182,7 +179,6 @@ connect with other infrastructure tools (like Dask or Spark). This allows users
scalable or high-performance resources from within their JupyterHub sessions. The logic of
how those resources are controlled is taken care of by the non-JupyterHub application.
### Can JupyterHub be used with my high-performance computing resources?
Yes - JupyterHub can provide access to many kinds of computing infrastructure.
@@ -218,7 +214,6 @@ the technologies your JupyterHub will use (e.g., dev-ops knowledge with cloud co
In general, the base JupyterHub deployment is not the bottleneck for setup, it is connecting
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?
JupyterHub works well at both a small scale (e.g., a single VM or machine) as well as a
@@ -227,7 +222,6 @@ for user bases as large as 10,000. The scalability of JupyterHub largely depends
infrastructure on which it is deployed. JupyterHub has been designed to be lightweight and
flexible, so you can tailor your JupyterHub deployment to your needs.
### Is JupyterHub resilient? What happens when a machine goes down?
For JupyterHubs that are deployed in a containerized environment (e.g., Kubernetes), it is

8
docs/source/getting-started/networking-basics.md
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@@ -11,7 +11,7 @@ This section will help you with basic proxy and network configuration to:
The Proxy's main IP address setting determines where JupyterHub is available to users.
By default, JupyterHub is configured to be available on all network interfaces
(`''`) on port 8000. *Note*: Use of `'*'` is discouraged for IP configuration;
(`''`) on port 8000. _Note_: Use of `'*'` is discouraged for IP configuration;
instead, use of `'0.0.0.0'` is preferred.
Changing the Proxy's main IP address and port can be done with the following
@@ -74,7 +74,7 @@ The Hub service listens only on `localhost` (port 8081) by default.
The Hub needs to be accessible from both the proxy and all Spawners.
When spawning local servers, an IP address setting of `localhost` is fine.
If *either* the Proxy *or* (more likely) the Spawners will be remote or
If _either_ the Proxy _or_ (more likely) the Spawners will be remote or
isolated in containers, the Hub must listen on an IP that is accessible.
```python
@@ -93,9 +93,9 @@ c.JupyterHub.hub_connect_ip = '10.0.1.4' # ip as seen on the docker network. Ca
## Adjusting the hub's URL
The hub will most commonly be running on a hostname of its own. If it
The hub will most commonly be running on a hostname of its own. If it
is not – for example, if the hub is being reverse-proxied and being
exposed at a URL such as `https://proxy.example.org/jupyter/` – then
you will need to tell JupyterHub the base URL of the service. In such
you will need to tell JupyterHub the base URL of the service. In such
a case, it is both necessary and sufficient to set
`c.JupyterHub.base_url = '/jupyter/'` in the configuration.

4
docs/source/getting-started/services-basics.md
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@@ -16,8 +16,8 @@ document will:
- clarify that API tokens can be used to authenticate to
single-user servers as of [version 0.8.0](../changelog)
- show how the [jupyterhub_idle_culler][] script can be:
- used in a Hub-managed service
- run as a standalone script
- used in a Hub-managed service
- run as a standalone script
Both examples for `jupyterhub_idle_culler` will communicate tasks to the
Hub via the REST API.