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Fix documentation

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David Brochart
2021-05-04 18:36:50 +02:00
parent 3fec19d191
commit 49e10fdbe9
7 changed files with 34 additions and 34 deletions
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20
docs/source/getting-started/authenticators-users-basics.md
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@@ -22,7 +22,7 @@ Admin users of JupyterHub, `admin_users`, can add and remove users from
the user `allowed_users` set. `admin_users` can take actions on other users'
behalf, such as stopping and restarting their servers.
A set of initial admin users, `admin_users` can configured be as follows:
A set of initial admin users, `admin_users` can be configured as follows:
```python
c.Authenticator.admin_users = {'mal', 'zoe'}
@@ -32,9 +32,9 @@ Users in the admin set are automatically added to the user `allowed_users` set,
if they are not already present.
Each authenticator may have different ways of determining whether a user is an
administrator. By default JupyterHub use the PAMAuthenticator which provide the
`admin_groups` option and can determine administrator status base on a user
groups. For example we can let any users in the `wheel` group be admin:
administrator. By default JupyterHub uses the PAMAuthenticator which provides the
`admin_groups` option and can set administrator status based on a user
group. For example we can let any user in the `wheel` group be admin:
```python
c.PAMAuthenticator.admin_groups = {'wheel'}
@@ -42,9 +42,9 @@ c.PAMAuthenticator.admin_groups = {'wheel'}
## Give admin access to other users' notebook servers (`admin_access`)
Since the default `JupyterHub.admin_access` setting is False, the admins
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,
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.**
@@ -53,8 +53,8 @@ sure your users know if admin_access is enabled.**
Users can be added to and removed from the Hub via either the admin
panel or the REST API. When a user is **added**, the user will be
automatically added to the allowed users set and database. Restarting the Hub
will not require manually updating the allowed users set in your config file,
automatically added to the `allowed_users` set and database. Restarting the Hub
will not require manually updating the `allowed_users` set in your config file,
as the users will be loaded from the database.
After starting the Hub once, it is not sufficient to **remove** a user
@@ -107,8 +107,8 @@ with any provider, is also available.
## Use DummyAuthenticator for testing
The :class:`~jupyterhub.auth.DummyAuthenticator` is a simple authenticator that
allows for any username/password unless if a global password has been set. If
The `DummyAuthenticator` is a simple authenticator that
allows for any username/password unless a global password has been set. If
set, it will allow for any username as long as the correct password is provided.
To set a global password, add this to the config file:

10
docs/source/getting-started/config-basics.md
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@@ -44,7 +44,7 @@ jupyterhub -f /etc/jupyterhub/jupyterhub_config.py
```
The IPython documentation provides additional information on the
[config system](http://ipython.readthedocs.io/en/stable/development/config)
[config system](http://ipython.readthedocs.io/en/stable/development/config.html)
that Jupyter uses.
## Configure using command line options
@@ -63,11 +63,11 @@ would enter:
jupyterhub --ip 10.0.1.2 --port 443 --ssl-key my_ssl.key --ssl-cert my_ssl.cert
```
All configurable options may technically be set on the command-line,
All configurable options may technically be set on the command line,
though some are inconvenient to type. To set a particular configuration
parameter, `c.Class.trait`, you would use the command line option,
`--Class.trait`, when starting JupyterHub. For example, to configure the
`c.Spawner.notebook_dir` trait from the command-line, use the
`c.Spawner.notebook_dir` trait from the command line, use the
`--Spawner.notebook_dir` option:
```bash
@@ -89,11 +89,11 @@ meant as illustration, are:
## Run the proxy separately
This is _not_ strictly necessary, but useful in many cases. If you
use a custom proxy (e.g. Traefik), this also not needed.
use a custom proxy (e.g. Traefik), this is 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
maintenance, re-configuration, etc.), then use connections are not
maintenance, re-configuration, etc.), then user connections are not
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,

2
docs/source/getting-started/faq.md
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@@ -26,7 +26,7 @@ so Breq would open `/user/breq/notebooks/foo.ipynb` and
Seivarden would open `/user/seivarden/notebooks/foo.ipynb`, etc.
JupyterHub has a special URL that does exactly this!
It's called `/hub/user-redirect/...` and after the visitor logs in,
It's called `/hub/user-redirect/...`.
So if you replace `/user/yourname` in your URL bar
with `/hub/user-redirect` any visitor should get the same
URL on their own server, rather than visiting yours.

16
docs/source/getting-started/institutional-faq.md
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@@ -11,7 +11,7 @@ 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
JupyterHub for its Data Science Education Program courses (serving over
3,000 students). The Pangeo project uses JupyterHub to provide access
to scalable cloud computing with Dask. JupyterHub is stable customizable
to scalable cloud computing with Dask. JupyterHub is stable and customizable
to the use-cases of large organizations.
### I keep hearing about Jupyter Notebook, JupyterLab, and now JupyterHub. What’s the difference?
@@ -34,7 +34,7 @@ Here is a quick breakdown of these three tools:
### 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,
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.
@@ -55,7 +55,7 @@ industry, and government research labs. It is most-commonly used by two kinds of
- 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:
Here is a sample of organizations that use JupyterHub:
- **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
@@ -123,7 +123,7 @@ level for several years, and makes a number of "default" security decisions that
users.
- For security considerations in the base JupyterHub application,
[see the JupyterHub security page](https://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html)
[see the JupyterHub security page](https://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html).
- 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).
@@ -183,7 +183,7 @@ how those resources are controlled is taken care of by the non-JupyterHub applic
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
complex computing infrastructures 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?
@@ -192,7 +192,7 @@ This is highly configurable by the administrator. If you wish for your users to
data analytics environments for prototyping and light data exploring, you can restrict their
memory and CPU based on the resources that you have available. If you'd like your JupyterHub
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.
resources available on user machines, or connect them with computing infrastructures elsewhere.
### Can I customize the look and feel of a JupyterHub?
@@ -217,7 +217,7 @@ your JupyterHub with the various services and tools that you wish to provide to
### 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
high scale (e.g., a scalable Kubernetes cluster). It can be used for teams as small as 2, and
for user bases as large as 10,000. The scalability of JupyterHub largely depends on the
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.
@@ -249,7 +249,7 @@ share their results with one another.
JupyterHub also provides a computational framework to share computational narratives between
different levels of an organization. For example, data scientists can share Jupyter Notebooks
rendered as [voila dashboards](https://voila.readthedocs.io/en/stable/) with those who are not
rendered as [Voilà dashboards](https://voila.readthedocs.io/en/stable/) with those who are not
familiar with programming, or create publicly-available interactive analyses to allow others to
interact with your work.

6
docs/source/getting-started/networking-basics.md
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@@ -43,7 +43,7 @@ port.
By default, this REST API listens on port 8001 of `localhost` only.
The Hub service talks to the proxy via a REST API on a secondary port. The
API URL can be configured separately and override the default settings.
API URL can be configured separately to override the default settings.
### Set api_url
@@ -82,13 +82,13 @@ c.JupyterHub.hub_ip = '10.0.1.4'
c.JupyterHub.hub_port = 54321
```
**Added in 0.8:** The `c.JupyterHub.hub_connect_ip` setting is the ip address or
**Added in 0.8:** The `c.JupyterHub.hub_connect_ip` setting is the IP address or
hostname that other services should use to connect to the Hub. A common
configuration for, e.g. docker, is:
```python
c.JupyterHub.hub_ip = '0.0.0.0' # listen on all interfaces
c.JupyterHub.hub_connect_ip = '10.0.1.4' # ip as seen on the docker network. Can also be a hostname.
c.JupyterHub.hub_connect_ip = '10.0.1.4' # IP as seen on the docker network. Can also be a hostname.
```
## Adjusting the hub's URL

4
docs/source/getting-started/services-basics.md
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@@ -2,7 +2,7 @@
When working with JupyterHub, a **Service** is defined as a process
that interacts with the Hub's REST API. A Service may perform a specific
or action or task. For example, shutting down individuals' single user
action or task. For example, shutting down individuals' single user
notebook servers that have been idle for some time is a good example of
a task that could be automated by a Service. Let's look at how the
[jupyterhub_idle_culler][] script can be used as a Service.
@@ -114,7 +114,7 @@ interact with it.
This will run the idle culler service manually. It can be run as a standalone
script anywhere with access to the Hub, and will periodically check for idle
servers and shut them down via the Hub's REST API. In order to shutdown the
servers, the token given to cull-idle must have admin privileges.
servers, the token given to `cull-idle` must have admin privileges.
Generate an API token and store it in the `JUPYTERHUB_API_TOKEN` environment
variable. Run `jupyterhub_idle_culler` manually.

6
docs/source/getting-started/spawners-basics.md
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@@ -1,8 +1,8 @@
# Spawners and single-user notebook servers
Since the single-user server is an instance of `jupyter notebook`, an entire separate
multi-process application, there are many aspect of that server can configure, and a lot of ways
to express that configuration.
multi-process application, there are many aspects of that server that can be configured, and a lot
of ways to express that configuration.
At the JupyterHub level, you can set some values on the Spawner. The simplest of these is
`Spawner.notebook_dir`, which lets you set the root directory for a user's server. This root
@@ -14,7 +14,7 @@ expanded to the user's home directory.
c.Spawner.notebook_dir = '~/notebooks'
```
You can also specify extra command-line arguments to the notebook server with:
You can also specify extra command line arguments to the notebook server with:
```python
c.Spawner.args = ['--debug', '--profile=PHYS131']