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11
docs/source/admin/log-messages.md
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@@ -8,19 +8,20 @@ log messages, what they mean and what are the most common causes that generated
### Example
Your log might display lines that seem cryptic
Your logs might be littered with lines that look scary
```
[W 2022-03-10 17:25:19.774 JupyterHub base:1349] Failing suspected API request to not-running server: /hub/user/<user-name>/api/metrics/v1
```
### Most likely cause
### Cause
The possible reason may be that the user's server has stopped running
but they still have a browser tab open. For example, you might have 3 tabs open and you shut
This likely means that the user's server has stopped running but they
still have a browser tab open. For example, you might have 3 tabs open and you shut
the server down via one.
The other possible reason could be that you closed your laptop and the server was culled for inactivity, then reopened the laptop!
Another possible reason could be that you closed your laptop and the server was culled for inactivity, then reopened the laptop!
However, the client-side code (JupyterLab, Classic Notebook, etc) doesn't interpret the shut-down server and continues to make some API requests.
JupyterHub's architecture means that the proxy routes all requests that
don't go to a running user server to the hub process itself. The hub
process then explicitly returns a failure response, so the client knows

59
docs/source/admin/upgrading.rst
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@@ -7,18 +7,18 @@ document describes how to do these upgrades.
If you are using :ref:`a JupyterHub distribution <index/distributions>`, you
should consult the distribution's documentation on how to upgrade. This
document is if you have set up your own JupyterHub without using a
document is useful if you have set up your own JupyterHub without using a
distribution.
It is long because is pretty detailed! Most likely, upgrading
JupyterHub is painless, quick and with minimal user interruption.
The steps are discussed in detail, so if you get stuck at any step you can always refer to this guide. Most likely,
upgrading JupyterHub is painless, quick and with minimal user interruption.
Read the Changelog
==================
The `changelog <../changelog.html>`_ contains information on what has
changed with the new JupyterHub release, and any deprecation warnings.
Read these notes to familiarize yourself with the coming changes. There
The `changelog <../changelog.md>`_ contains information on what has
changed with the new JupyterHub release and any deprecation warnings.
Read these notes to familiarize yourself with the upcoming changes. There
might be new releases of authenticators & spawners you are using, so
read the changelogs for those too!
@@ -30,10 +30,10 @@ is managed by JupyterHub, your users will see service disruption during
the upgrade process. You should notify them, and pick a time to do the
upgrade where they will be least disrupted.
If you are using a different proxy, or running ``configurable-http-proxy``
If you are using a different proxy or running ``configurable-http-proxy``
independent of JupyterHub, your users will be able to continue using notebook
servers they had already launched, but will not be able to launch new servers
nor sign in.
or sign in.
Backup database & config
@@ -41,37 +41,36 @@ Backup database & config
Before doing an upgrade, it is critical to back up:
#. Your JupyterHub database (sqlite by default, or MySQL / Postgres
if you used those). If you are using sqlite (the default), you
should backup the ``jupyterhub.sqlite`` file.
#. Your JupyterHub database (SQLite by default, or MySQL / Postgres
if you used those). If you are using SQLite (the default), you
should back up the ``jupyterhub.sqlite`` file.
#. Your ``jupyterhub_config.py`` file.
#. Your user's home directories. This is unlikely to be affected directly by
a JupyterHub upgrade, but we recommend a backup since user data is very
critical.
#. Your users' home directories. This is unlikely to be affected directly by
a JupyterHub upgrade, but we recommend a backup since user data is critical.
Shutdown JupyterHub
===================
Shut down JupyterHub
====================
Shutdown the JupyterHub process. This would vary depending on how you
Shut down the JupyterHub process. This would vary depending on how you
have set up JupyterHub to run. Most likely, it is using a process
supervisor of some sort (``systemd`` or ``supervisord`` or even ``docker``).
Use the supervisor specific command to stop the JupyterHub process.
Use the supervisor-specific command to stop the JupyterHub process.
Upgrade JupyterHub packages
===========================
There are two environments where the ``jupyterhub`` package is installed:
#. The *hub environment*, which is where the JupyterHub server process
#. The *hub environment*: where the JupyterHub server process
runs. This is started with the ``jupyterhub`` command, and is what
people generally think of as JupyterHub.
#. The *notebook user environments*. This is where the user notebook
#. The *notebook user environments*: where the user notebook
servers are launched from, and is probably custom to your own
installation. This could be just one environment (different from the
hub environment) that is shared by all users, one environment
per user, or same environment as the hub environment. The hub
per user, or the same environment as the hub environment. The hub
launched the ``jupyterhub-singleuser`` command in this environment,
which in turn starts the notebook server.
@@ -92,10 +91,8 @@ with:
conda install -c conda-forge jupyterhub==<version>
Where ``<version>`` is the version of JupyterHub you are upgrading to.
You should also check for new releases of the authenticator & spawner you
are using. You might wish to upgrade those packages too along with JupyterHub,
are using. You might wish to upgrade those packages, too, along with JupyterHub
or upgrade them separately.
Upgrade JupyterHub database
@@ -109,7 +106,7 @@ database. From the hub environment, in the same directory as your
jupyterhub upgrade-db
This should find the location of your database, and run necessary upgrades
This should find the location of your database, and run the necessary upgrades
for it.
SQLite database disadvantages
@@ -118,11 +115,11 @@ SQLite database disadvantages
SQLite has some disadvantages when it comes to upgrading JupyterHub. These
are:
- ``upgrade-db`` may not work, and you may need delete your database
- ``upgrade-db`` may not work, and you may need to delete your database
and start with a fresh one.
- ``downgrade-db`` **will not** work if you want to rollback to an
earlier version, so backup the ``jupyterhub.sqlite`` file before
upgrading
upgrading.
What happens if I delete my database?
-------------------------------------
@@ -137,10 +134,10 @@ resides only in the Hub database includes:
If the following conditions are true, you should be fine clearing the
Hub database and starting over:
- users specified in config file, or login using an external
- users specified in the config file, or login using an external
authentication provider (Google, GitHub, LDAP, etc)
- user servers are stopped during upgrade
- don't mind causing users to login again after upgrade
- user servers are stopped during the upgrade
- don't mind causing users to log in again after the upgrade
Start JupyterHub
================
@@ -148,7 +145,7 @@ Start JupyterHub
Once the database upgrade is completed, start the ``jupyterhub``
process again.
#. Log-in and start the server to make sure things work as
#. Log in and start the server to make sure things work as
expected.
#. Check the logs for any errors or deprecation warnings. You
might have to update your ``jupyterhub_config.py`` file to

23
docs/source/contributing/community.md Normal file
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@@ -0,0 +1,23 @@
# Community communication channels
We use different channels of communication for different purposes. Whichever one you use will depend on what kind of communication you want to engage in.
## Discourse (recommended)
We use [Discourse](https://discourse.jupyter.org) for online discussions and support questions. Everyone in the Jupyter community is welcome to bring ideas and questions there.
All our past and current discussions on Discourse are archived and searchable. This is why we recommend you first go to Discourse, so that discussions remain useful and accessible to the whole community.
## Gitter
We use [our Gitter channel](https://gitter.im/jupyterhub/jupyterhub) for online, real-time text chat; a place for more ephemeral discussions. When you're not on Discourse, you can stop here to have other discussions on the fly.
## Github Issues
Github issues are used for most long-form project discussions, bug reports and feature requests.
Issues related to a specific authenticator or spawner should be opened in the appropriate repository for the authenticator or spawner. If you are using a specific JupyterHub distribution (such as [Zero to JupyterHub on Kubernetes](http://github.com/jupyterhub/zero-to-jupyterhub-k8s) or [The Littlest JupyterHub](http://github.com/jupyterhub/the-littlest-jupyterhub/)), you should open issues directly in their repository.
If you cannot find a repository to open your issue in, do not worry! Open the issue in the [main JupyterHub repository](https://github.com/jupyterhub/jupyterhub/) and our community will help you figure it out.
**NOTE**: Our community is distributed across the world in various timezones, so please be patient if you do not get a response immediately!

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docs/source/contributing/community.rst
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@@ -1,30 +0,0 @@
.. _contributing/community:
================================
Community communication channels
================================
We use `Discourse <https://discourse.jupyter.org>` for online discussion.
Everyone in the Jupyter community is welcome to bring ideas and questions there.
In addition, we use `Gitter <https://gitter.im>`_ for online, real-time text chat,
a place for more ephemeral discussions.
The primary Gitter channel for JupyterHub is `jupyterhub/jupyterhub <https://gitter.im/jupyterhub/jupyterhub>`_.
Gitter isn't archived or searchable, so we recommend going to discourse first
to make sure that discussions are most useful and accessible to the community.
Remember that our community is distributed across the world in various
timezones, so be patient if you do not get an answer immediately!
GitHub issues are used for most long-form project discussions, bug reports
and feature requests. Issues related to a specific authenticator or
spawner should be directed to the appropriate repository for the
authenticator or spawner. If you are using a specific JupyterHub
distribution (such as `Zero to JupyterHub on Kubernetes <http://github.com/jupyterhub/zero-to-jupyterhub-k8s>`_
or `The Littlest JupyterHub <http://github.com/jupyterhub/the-littlest-jupyterhub/>`_),
you should open issues directly in their repository. If you can not
find a repository to open your issue in, do not worry! Create it in the `main
JupyterHub repository <https://github.com/jupyterhub/jupyterhub/>`_ and our
community will help you figure it out.
A `mailing list <https://groups.google.com/forum/#!forum/jupyter>`_ for all
of Project Jupyter exists, along with one for `teaching with Jupyter
<https://groups.google.com/forum/#!forum/jupyter-education>`_.

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docs/source/contributing/tests.rst
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@@ -4,19 +4,16 @@
Testing JupyterHub and linting code
===================================
Unit test help validate that JupyterHub works the way we think it does,
and continues to do so when changes occur. They also help communicate
precisely what we expect our code to do.
Unit tests help confirm that JupyterHub works as intended, including after modifications are made. Additionally, they help in clarifying our expectations for our code.
JupyterHub uses `pytest <https://pytest.org>`_ for all our tests. You
can find them under ``jupyterhub/tests`` directory in the git repository.
JupyterHub uses `pytest <https://pytest.org>`_ for all the tests. You
can find them under the `jupyterhub/tests <https://github.com/jupyterhub/jupyterhub/tree/main/jupyterhub/tests>`_ directory in the git repository.
Running the tests
==================
#. Make sure you have completed :ref:`contributing/setup`. You should be able
to start ``jupyterhub`` from the commandline & access it from your
web browser. This ensures that the dev environment is properly set
#. Make sure you have completed :ref:`contributing/setup`. Once completed, you should be able
to run ``jupyterhub`` on your command line and access JupyterHub from your browser at http://localhost:8000. Being able to run and access `jupyterhub` should mean that the dev environment is properly set
up for tests to run.
#. You can run all tests in JupyterHub
@@ -57,7 +54,7 @@ Running the tests
pytest -v jupyterhub/tests/test_api.py::test_shutdown
See the `pytest usage documentation <https://pytest.readthedocs.io/en/latest/usage.html>`_ for more details.
For more information, refer to the `pytest usage documentation <https://pytest.readthedocs.io/en/latest/usage.html>`_.
Test organisation
=================
@@ -98,8 +95,7 @@ And fixtures to add functionality or spawning behavior:
- ``bad_spawn``: enables the BadSpawner (a spawner that fails immediately)
- ``slow_bad_spawn``: enables the SlowBadSpawner (a spawner that fails after a short delay)
See the `pytest fixtures documentation <https://pytest.readthedocs.io/en/latest/fixture.html>`_
for how to use the existing fixtures, and how to create new ones.
For information on using the existing fixtures and creating new ones, refer to the `pytest fixtures documentation <https://pytest.readthedocs.io/en/latest/fixture.html>`_
Troubleshooting Test Failures
@@ -108,8 +104,7 @@ Troubleshooting Test Failures
All the tests are failing
-------------------------
Make sure you have completed all the steps in :ref:`contributing/setup` successfully, and
can launch ``jupyterhub`` from the terminal.
Make sure you have completed all the steps in :ref:`contributing/setup` successfully, and are able to access JupyterHub from your browser at http://localhost:8000 after starting ``jupyterhub`` in your command line.
Code formatting and linting
@@ -117,13 +112,13 @@ Code formatting and linting
JupyterHub has adopted automatic code formatting and linting.
As long as your code is valid, the pre-commit hook should take care of how it should look.
You can invoke the pre-commit hook by hand at any time with:
You can invoke the pre-commit hook manually at any time with:
.. code:: bash
pre-commit run
which should run any autoformatting on your code and tell you about any errors it couldn't fix automatically.
This should run any auto formatting on your code and tell you about any errors it couldn't fix automatically.
You may also install `black integration <https://github.com/psf/black#editor-integration>`_
into your text editor to format code automatically.

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docs/source/events/index.rst
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@@ -1,5 +1,5 @@
Eventlogging and Telemetry
==========================
Event logging and Telemetry
===========================
JupyterHub can be configured to record structured events from a running server using Jupyter's `Telemetry System`_. The types of events that JupyterHub emits are defined by `JSON schemas`_ listed at the bottom of this page_.
@@ -15,8 +15,8 @@ Event logging is handled by its ``Eventlog`` object. This leverages Python's sta
To begin recording events, you'll need to set two configurations:
1. ``handlers``: tells the EventLog *where* to route your events. This trait is a list of Python logging handlers that route events to
2. ``allows_schemas``: tells the EventLog *which* events should be recorded. No events are emitted by default; all recorded events must be listed here.
1. ``handlers``: tells the EventLog *where* to route your events; this trait is a list of Python logging handlers that route events to the destination
2. ``allows_schemas``: tells the EventLog *which* events should be recorded; no events are emitted by default; all recorded events must be listed here
Here's a basic example:

6
docs/source/getting-started/config-basics.md
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@@ -1,6 +1,6 @@
# Configuration Basics
The section contains basic information about configuring settings for a JupyterHub
This section contains basic information about configuring settings for a JupyterHub
deployment. The [Technical Reference](../reference/index)
documentation provides additional details.
@@ -49,7 +49,7 @@ that Jupyter uses.
## Configure using command line options
To display all command line options that are available for configuration:
To display all command line options that are available for configuration run the following command:
```bash
jupyterhub --help-all
@@ -81,7 +81,7 @@ specific [authenticators](./authenticators-users-basics) and
[spawners](./spawners-basics) can be set in the configuration file.
This enables JupyterHub to be used with a variety of authentication methods or
process control and deployment environments. [Some examples](../reference/config-examples),
meant as illustration, are:
meant as illustrations, are:
- Using GitHub OAuth instead of PAM with [OAuthenticator](https://github.com/jupyterhub/oauthenticator)
- Spawning single-user servers with Docker, using the [DockerSpawner](https://github.com/jupyterhub/dockerspawner)

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docs/source/getting-started/services-basics.md
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@@ -24,7 +24,7 @@ Hub via the REST API.
## API Token basics
### Create an API token
### Step 1: Generate an API token
To run such an external service, an API token must be created and
provided to the service.
@@ -43,12 +43,12 @@ generating an API token is available from the JupyterHub user interface:
![API TOKEN success page](../images/token-request-success.png)
### Pass environment variable with token to the Hub
### Step 2: Pass environment variable with token to the Hub
In the case of `cull_idle_servers`, it is passed as the environment
variable called `JUPYTERHUB_API_TOKEN`.
### Use API tokens for services and tasks that require external access
### Step 3: Use API tokens for services and tasks that require external access
While API tokens are often associated with a specific user, API tokens
can be used by services that require external access for activities
@@ -62,7 +62,7 @@ c.JupyterHub.services = [
]
```
### Restart JupyterHub
### Step 4: Restart JupyterHub
Upon restarting JupyterHub, you should see a message like below in the
logs:
@@ -78,16 +78,15 @@ single-user servers, and only cookies can be used for authentication.
0.8 supports using JupyterHub API tokens to authenticate to single-user
servers.
## Configure the idle culler to run as a Hub-Managed Service
## How to configure the idle culler to run as a Hub-Managed Service
Install the idle culler:
### Step 1: Install the idle culler:
```
pip install jupyterhub-idle-culler
```
In `jupyterhub_config.py`, add the following dictionary for the
`idle-culler` Service to the `c.JupyterHub.services` list:
### Step 2: In `jupyterhub_config.py`, add the following dictionary for the `idle-culler` Service to the `c.JupyterHub.services` list:
```python
c.JupyterHub.services = [
@@ -127,7 +126,7 @@ It now needs the scopes:
- `admin:servers` to start/stop servers
```
## Run `cull-idle` manually as a standalone script
## How to run `cull-idle` manually as a standalone script
Now you can run your script by providing it
the API token and it will authenticate through the REST API to

8
docs/source/getting-started/spawners-basics.md
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@@ -1,12 +1,12 @@
# 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 aspects of that server that can be configured, and a lot
A Spawner starts each single-user notebook server. Since the single-user server is an instance of `jupyter notebook`, an entire separate
multi-process application, many aspects of that server can be configured and there are 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
notebook directory is the highest level directory users will be able to access in the notebook
notebook directory is the highest-level directory users will be able to access in the notebook
dashboard. In this example, the root notebook directory is set to `~/notebooks`, where `~` is
expanded to the user's home directory.
@@ -20,7 +20,7 @@ You can also specify extra command line arguments to the notebook server with:
c.Spawner.args = ['--debug', '--profile=PHYS131']
```
This could be used to set the users default page for the single user server:
This could be used to set the user's default page for the single-user server:
```python
c.Spawner.args = ['--NotebookApp.default_url=/notebooks/Welcome.ipynb']

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docs/source/index.rst
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@@ -2,31 +2,29 @@
JupyterHub
==========
`JupyterHub`_ is the best way to serve `Jupyter notebook`_ for multiple users.
It can be used in a class of students, a corporate data science group or scientific
`JupyterHub`_ is the best way to serve `Jupyter notebook`_ for multiple users.
Because JupyterHub manages a separate Jupyter environment for each user,
it can be used in a class of students, a corporate data science group, or a scientific
research group. It is a multi-user **Hub** that spawns, manages, and proxies multiple
instances of the single-user `Jupyter notebook`_ server.
To make life easier, JupyterHub has distributions. Be sure to
JupyterHub offers distributions for different use cases. Be sure to
take a look at them before continuing with the configuration of the broad
original system of `JupyterHub`_. Today, you can find two main cases:
original system of `JupyterHub`_. As of now, you can find two main cases:
1. If you need a simple case for a small amount of users (0-100) and single server
take a look at
`The Littlest JupyterHub <https://github.com/jupyterhub/the-littlest-jupyterhub>`__ distribution.
2. If you need to allow for even more users, a dynamic amount of servers can be used on a cloud,
take a look at the `Zero to JupyterHub with Kubernetes <https://github.com/jupyterhub/zero-to-jupyterhub-k8s>`__ .
1. `The Littlest JupyterHub <https://github.com/jupyterhub/the-littlest-jupyterhub>`__ distribution is suitable if you need a small number of users (1-100) and a single server with a simple environment.
2. `Zero to JupyterHub with Kubernetes <https://github.com/jupyterhub/zero-to-jupyterhub-k8s>`__ allows you to deploy dynamic servers on the cloud if you need even more users.
Four subsystems make up JupyterHub:
* a **Hub** (tornado process) that is the heart of JupyterHub
* a **configurable http proxy** (node-http-proxy) that receives the requests from the client's browser
* multiple **single-user Jupyter notebook servers** (Python/IPython/tornado) that are monitored by Spawners
* an **authentication class** that manages how users can access the system
* a **Configurable HTTP Proxy** (node-http-proxy) that receives the requests from the client's browser
* multiple **Single-User Jupyter Notebook Servers** (Python/IPython/tornado) that are monitored by Spawners
* an **Authentication Class** that manages how users can access the system
Besides these central pieces, you can add optional configurations through a `config.py` file and manage users kernels on an admin panel. A simplification of the whole system can be seen in the figure below:
Besides these central pieces, you can add optional configurations through a `config.py` file and manage users' environments through an admin panel. A simplification of the whole system can be seen in the figure below:
.. image:: images/jhub-fluxogram.jpeg
:alt: JupyterHub subsystems
@@ -56,17 +54,17 @@ Contents
Distributions
-------------
A JupyterHub **distribution** is tailored towards a particular set of
Each JupyterHub **distribution** is tailored toward a particular set of
use cases. These are generally easier to set up than setting up
JupyterHub from scratch, assuming they fit your use case.
The two popular ones are:
* `Zero to JupyterHub on Kubernetes <http://z2jh.jupyter.org>`_, for
running JupyterHub on top of `Kubernetes <https://k8s.io>`_. This
can scale to large number of machines & users.
* `The Littlest JupyterHub <http://tljh.jupyter.org>`_, for an easy
to set up & run JupyterHub supporting 1-100 users on a single machine.
* `Zero to JupyterHub on Kubernetes <http://z2jh.jupyter.org>`_, for
running JupyterHub on top of `Kubernetes <https://k8s.io>`_. This
can scale to a large number of machines & users.
Installation Guide
------------------
@@ -119,8 +117,8 @@ RBAC Reference
Contributing
------------
We want you to contribute to JupyterHub in ways that are most exciting
& useful to you. We value documentation, testing, bug reporting & code equally,
We welcome you to contribute to JupyterHub in ways that are most exciting
& useful to you. We value documentation, testing, bug reporting & code equally
and are glad to have your contributions in whatever form you wish :)
Our `Code of Conduct <https://github.com/jupyter/governance/blob/HEAD/conduct/code_of_conduct.md>`_

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docs/source/rbac/tech-implementation.md
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@@ -1,8 +1,8 @@
# Technical Implementation
Roles are stored in the database, where they are associated with users, services, etc., and can be added or modified as explained in {ref}`define-role-target` section. Users, services, groups, and tokens can gain, change, and lose roles. This is currently achieved via `jupyterhub_config.py` (see {ref}`define-role-target`) and will be made available via API in future. The latter will allow for changing a token's role, and thereby its permissions, without the need to issue a new token.
[Roles](roles) are stored in the database, where they are associated with users, services, and groups. Roles can be added or modified as explained in the {ref}`define-role-target` section. Users, services, groups, and tokens can gain, change, and lose roles. This is currently achieved via `jupyterhub_config.py` (see {ref}`define-role-target`) and will be made available via API in the future. The latter will allow for changing a user's role, and thereby its permissions, without the need to restart JupyterHub.
Roles and scopes utilities can be found in `roles.py` and `scopes.py` modules. Scope variables take on five different formats which is reflected throughout the utilities via specific nomenclature:
Roles and scopes utilities can be found in `roles.py` and `scopes.py` modules. Scope variables take on five different formats that are reflected throughout the utilities via specific nomenclature:
```{admonition} **Scope variable nomenclature**
:class: tip
@@ -11,22 +11,22 @@ Roles and scopes utilities can be found in `roles.py` and `scopes.py` modules. S
- _expanded scopes_ \
Set of fully expanded scopes without abbreviations (i.e., resolved metascopes, filters, and subscopes). E.g., `{"users:activity!user=charlie", "read:users:activity!user=charlie"}`.
- _parsed scopes_ \
Dictionary represenation of expanded scopes. E.g., `{"users:activity": {"user": ["charlie"]}, "read:users:activity": {"users": ["charlie"]}}`.
Dictionary representation of expanded scopes. E.g., `{"users:activity": {"user": ["charlie"]}, "read:users:activity": {"users": ["charlie"]}}`.
- _intersection_ \
Set of expanded scopes as intersection of 2 expanded scope sets.
- _identify scopes_ \
Set of expanded scopes needed for identify (whoami) endpoints.
Set of expanded scopes needed for identity (whoami) endpoints.
```
(resolving-roles-scopes-target)=
## Resolving roles and scopes
**Resolving roles** refers to determining which roles a user, service, or group has, extracting the list of scopes from each role and combining them into a single set of scopes.
**Resolving roles** involves determining which roles a user, service, or group has, extracting the list of scopes from each role and combining them into a single set of scopes.
**Resolving scopes** involves expanding scopes into all their possible subscopes (_expanded scopes_), parsing them into format used for access evaluation (_parsed scopes_) and, if applicable, comparing two sets of scopes (_intersection_). All procedures take into account the scope hierarchy, {ref}`vertical <vertical-filtering-target>` and {ref}`horizontal filtering <horizontal-filtering-target>`, limiting or elevated permissions (`read:<resource>` or `admin:<resource>`, respectively), and metascopes.
**Resolving scopes** involves expanding scopes into all their possible subscopes (_expanded scopes_), parsing them into the format used for access evaluation (_parsed scopes_) and, if applicable, comparing two sets of scopes (_intersection_). All procedures take into account the scope hierarchy, {ref}`vertical <vertical-filtering-target>` and {ref}`horizontal filtering <horizontal-filtering-target>`, limiting or elevated permissions (`read:<resource>` or `admin:<resource>`, respectively), and metascopes.
Roles and scopes are resolved on several occasions, for example when requesting an API token with specific scopes or making an API request. The following sections provide more details.
Roles and scopes are resolved on several occasions, for example when requesting an API token with specific scopes or when making an API request. The following sections provide more details.
(requesting-api-token-target)=
@@ -43,25 +43,24 @@ Prior to 3.0, tokens stored _roles_,
which meant their scopes were resolved on each request.
:::
API tokens grant access to JupyterHub's APIs. The RBAC framework allows for requesting tokens with specific permissions.
API tokens grant access to JupyterHub's APIs. The Role Based Access Control (RBAC) framework allows for the requesting of tokens with specific permissions.
RBAC is involved in several stages of the OAuth token flow.
When requesting a token via the tokens API (`/users/:name/tokens`), or the token page (`/hub/token`),
if no scopes are requested, the token is issued with the permissions stored on the default `token` role
(providing the requester is allowed to create the token).
(provided the requester is allowed to create the token).
OAuth tokens are also requested via OAuth flow
If the token is requested with any scopes, the permissions of requesting entity are checked against the requested permissions to ensure the token would not grant its owner additional privileges.
If, due to modifications of permissions of the token or token owner,
at API request time a token has any scopes that its owner does not,
those scopes are removed.
If a token has any scopes that its owner does not possess
at the time of making the API request, those scopes are removed.
The API request is resolved without additional errors using the scope _intersection_;
the Hub logs a warning in this case (see {ref}`Figure 2 <api-request-chart>`).
Resolving a token's scope (yellow box in {ref}`Figure 1 <token-request-chart>`) corresponds to resolving all the token's owner roles (including the roles associated with their groups) and the token's own scopes into a set of scopes. The two sets are compared (Resolve the scopes box in orange in {ref}`Figure 1 <token-request-chart>`), taking into account the scope hierarchy.
Resolving a token's scope (yellow box in {ref}`Figure 1 <token-request-chart>`) corresponds to resolving all the roles of the token's owner (including the roles associated with their groups) and the token's own scopes into a set of scopes. The two sets are compared (Resolve the scopes box in orange in {ref}`Figure 1 <token-request-chart>`), taking into account the scope hierarchy.
If the token's scopes are a subset of the token owner's scopes, the token is issued with the requested scopes; if not, JupyterHub will raise an error.
{ref}`Figure 1 <token-request-chart>` below illustrates the steps involved. The orange rectangles highlight where in the process the roles and scopes are resolved.
@@ -75,10 +74,10 @@ Figure 1. Resolving roles and scopes during API token request
### Making an API request
With the RBAC framework, each authenticated JupyterHub API request is guarded by a scope decorator that specifies which scopes are required to gain the access to the API.
With the RBAC framework, each authenticated JupyterHub API request is guarded by a scope decorator that specifies which scopes are required in order to gain the access to the API.
When an API request is performed, the requesting API token's scopes are again intersected with its owner's (yellow box in {ref}`Figure 2 <api-request-chart>`) to ensure the token does not grant more permissions than its owner has at the request time (e.g., due to changing/losing roles).
If the owner's roles do not include some scopes of the token's scopes, only the _intersection_ of the token's and owner's scopes will be used. For example, using a token with scope `users` whose owner's role scope is `read:users:name` will result in only the `read:users:name` scope being passed on. In the case of no _intersection_, an empty set of scopes will be used.
When an API request is made, the requesting API token's scopes are again intersected with its owner's (yellow box in {ref}`Figure 2 <api-request-chart>`) to ensure that the token does not grant more permissions than its owner has at the request time (e.g., due to changing/losing roles).
If the owner's roles do not include some scopes of the token, only the _intersection_ of the token's and owner's scopes will be used. For example, using a token with scope `users` whose owner's role scope is `read:users:name` will result in only the `read:users:name` scope being passed on. In the case of no _intersection_, an empty set of scopes will be used.
The passed scopes are compared to the scopes required to access the API as follows:
@@ -86,7 +85,7 @@ The passed scopes are compared to the scopes required to access the API as follo
- if that is not the case, another check is utilized to determine if subscopes of the required API scopes can be found in the passed scope set:
- if found, the RBAC framework employs the {ref}`filtering <vertical-filtering-target>` procedures to refine the API response to access only resource attributes corresponding to the passed scopes. For example, providing a scope `read:users:activity!group=class-C` for the _GET /users_ API will return a list of user models from group `class-C` containing only the `last_activity` attribute for each user model
- if found, the RBAC framework employs the {ref}`filtering <vertical-filtering-target>` procedures to refine the API response to access only resource attributes corresponding to the passed scopes. For example, providing a scope `read:users:activity!group=class-C` for the `GET /users` API will return a list of user models from group `class-C` containing only the `last_activity` attribute for each user model
- if not found, the access to API is denied

10
docs/source/reference/config-ghoauth.md
View File

@@ -5,15 +5,15 @@ deployment with the following assumptions:
- Running JupyterHub on a single cloud server
- Using SSL on the standard HTTPS port 443
- Using GitHub OAuth (using oauthenticator) for login
- Using GitHub OAuth (using [OAuthenticator](https://oauthenticator.readthedocs.io/en/latest)) for login
- Using the default spawner (to configure other spawners, uncomment and edit
`spawner_class` as well as follow the instructions for your desired spawner)
- Users exist locally on the server
- Users' notebooks to be served from `~/assignments` to allow users to browse
for notebooks within other users' home directories
- You want the landing page for each user to be a `Welcome.ipynb` notebook in
their assignments directory.
- All runtime files are put into `/srv/jupyterhub` and log files in `/var/log`.
their assignments directory
- All runtime files are put into `/srv/jupyterhub` and log files in `/var/log`
The `jupyterhub_config.py` file would have these settings:
@@ -69,7 +69,7 @@ c.Spawner.args = ['--NotebookApp.default_url=/notebooks/Welcome.ipynb']
```
Using the GitHub Authenticator requires a few additional
environment variable to be set prior to launching JupyterHub:
environment variables to be set prior to launching JupyterHub:
```bash
export GITHUB_CLIENT_ID=github_id
@@ -79,3 +79,5 @@ export CONFIGPROXY_AUTH_TOKEN=super-secret
# append log output to log file /var/log/jupyterhub.log
jupyterhub -f /etc/jupyterhub/jupyterhub_config.py &>> /var/log/jupyterhub.log
```
Visit the [Github OAuthenticator reference](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.github.html) to see the full list of options for configuring Github OAuth with JupyterHub.

52
docs/source/reference/config-proxy.md
View File

@@ -14,7 +14,7 @@ satisfy the following:
- After testing, the server in question should be able to score at least an A on the
Qualys SSL Labs [SSL Server Test](https://www.ssllabs.com/ssltest/)
Let's start out with needed JupyterHub configuration in `jupyterhub_config.py`:
Let's start out with the needed JupyterHub configuration in `jupyterhub_config.py`:
```python
# Force the proxy to only listen to connections to 127.0.0.1 (on port 8000)
@@ -30,15 +30,15 @@ This can take a few minutes:
openssl dhparam -out /etc/ssl/certs/dhparam.pem 4096
```
## nginx
## Nginx
This **`nginx` config file** is fairly standard fare except for the two
`location` blocks within the main section for HUB.DOMAIN.tld.
To create a new site for jupyterhub in your nginx config, make a new file
To create a new site for jupyterhub in your Nginx config, make a new file
in `sites.enabled`, e.g. `/etc/nginx/sites.enabled/jupyterhub.conf`:
```bash
# top-level http config for websocket headers
# Top-level HTTP config for WebSocket headers
# If Upgrade is defined, Connection = upgrade
# If Upgrade is empty, Connection = close
map $http_upgrade $connection_upgrade {
@@ -51,7 +51,7 @@ server {
listen 80;
server_name HUB.DOMAIN.TLD;
# Tell all requests to port 80 to be 302 redirected to HTTPS
# Redirect the request to HTTPS
return 302 https://$host$request_uri;
}
@@ -75,7 +75,7 @@ server {
ssl_stapling_verify on;
add_header Strict-Transport-Security max-age=15768000;
# Managing literal requests to the JupyterHub front end
# Managing literal requests to the JupyterHub frontend
location / {
proxy_pass http://127.0.0.1:8000;
proxy_set_header X-Real-IP $remote_addr;
@@ -101,10 +101,10 @@ server {
If `nginx` is not running on port 443, substitute `$http_host` for `$host` on
the lines setting the `Host` header.
`nginx` will now be the front facing element of JupyterHub on `443` which means
`nginx` will now be the front-facing element of JupyterHub on `443` which means
it is also free to bind other servers, like `NO_HUB.DOMAIN.TLD` to the same port
on the same machine and network interface. In fact, one can simply use the same
server blocks as above for `NO_HUB` and simply add line for the root directory
server blocks as above for `NO_HUB` and simply add a line for the root directory
of the site as well as the applicable location call:
```bash
@@ -112,7 +112,7 @@ server {
listen 80;
server_name NO_HUB.DOMAIN.TLD;
# Tell all requests to port 80 to be 302 redirected to HTTPS
# Redirect the request to HTTPS
return 302 https://$host$request_uri;
}
@@ -143,12 +143,12 @@ Now restart `nginx`, restart the JupyterHub, and enjoy accessing
`https://HUB.DOMAIN.TLD` while serving other content securely on
`https://NO_HUB.DOMAIN.TLD`.
### SELinux permissions for nginx
### SELinux permissions for Nginx
On distributions with SELinux enabled (e.g. Fedora), one may encounter permission errors
when the nginx service is started.
when the Nginx service is started.
We need to allow nginx to perform network relay and connect to the jupyterhub port. The
We need to allow Nginx to perform network relay and connect to the JupyterHub port. The
following commands do that:
```bash
@@ -157,26 +157,26 @@ setsebool -P httpd_can_network_relay 1
setsebool -P httpd_can_network_connect 1
```
Replace 8000 with the port the jupyterhub server is running from.
Replace 8000 with the port the JupyterHub server is running from.
## Apache
As with nginx above, you can use [Apache](https://httpd.apache.org) as the reverse proxy.
First, we will need to enable the apache modules that we are going to need:
As with Nginx above, you can use [Apache](https://httpd.apache.org) as the reverse proxy.
First, we will need to enable the Apache modules that we are going to need:
```bash
a2enmod ssl rewrite proxy headers proxy_http proxy_wstunnel
```
Our Apache configuration is equivalent to the nginx configuration above:
Our Apache configuration is equivalent to the Nginx configuration above:
- Redirect HTTP to HTTPS
- Good SSL Configuration
- Support for websockets on any proxied URL
- Support for WebSocket on any proxied URL
- JupyterHub is running locally at http://127.0.0.1:8000
```bash
# redirect HTTP to HTTPS
# Redirect HTTP to HTTPS
Listen 80
<VirtualHost HUB.DOMAIN.TLD:80>
ServerName HUB.DOMAIN.TLD
@@ -188,26 +188,26 @@ Listen 443
ServerName HUB.DOMAIN.TLD
# enable HTTP/2, if available
# Enable HTTP/2, if available
Protocols h2 http/1.1
# HTTP Strict Transport Security (mod_headers is required) (63072000 seconds)
Header always set Strict-Transport-Security "max-age=63072000"
# configure SSL
# Configure SSL
SSLEngine on
SSLCertificateFile /etc/letsencrypt/live/HUB.DOMAIN.TLD/fullchain.pem
SSLCertificateKeyFile /etc/letsencrypt/live/HUB.DOMAIN.TLD/privkey.pem
SSLOpenSSLConfCmd DHParameters /etc/ssl/certs/dhparam.pem
# intermediate configuration from ssl-config.mozilla.org (2022-03-03)
# Please note, that this configuration might be out-dated - please update it accordingly using https://ssl-config.mozilla.org/
# Intermediate configuration from SSL-config.mozilla.org (2022-03-03)
# Please note, that this configuration might be outdated - please update it accordingly using https://ssl-config.mozilla.org/
SSLProtocol all -SSLv3 -TLSv1 -TLSv1.1
SSLCipherSuite ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES128-GCM-SHA256:ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-CHACHA20-POLY1305:ECDHE-RSA-CHACHA20-POLY1305:DHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384
SSLHonorCipherOrder off
SSLSessionTickets off
# Use RewriteEngine to handle websocket connection upgrades
# Use RewriteEngine to handle WebSocket connection upgrades
RewriteEngine On
RewriteCond %{HTTP:Connection} Upgrade [NC]
RewriteCond %{HTTP:Upgrade} websocket [NC]
@@ -224,7 +224,7 @@ Listen 443
</VirtualHost>
```
In case of the need to run the jupyterhub under /jhub/ or other location please use the below configurations:
In case of the need to run JupyterHub under /jhub/ or another location please use the below configurations:
- JupyterHub running locally at http://127.0.0.1:8000/jhub/ or other location
@@ -241,7 +241,7 @@ httpd.conf amendments:
jupyterhub_config.py amendments:
```bash
--The public facing URL of the whole JupyterHub application.
--This is the address on which the proxy will bind. Sets protocol, ip, base_url
--The public-facing URL of the whole JupyterHub application.
--This is the address on which the proxy will bind. Sets protocol, IP, base_url
c.JupyterHub.bind_url = 'http://127.0.0.1:8000/jhub/'
```

117
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View File

@@ -1,49 +1,43 @@
# Configuring user environments
Deploying JupyterHub means you are providing Jupyter notebook environments for
To deploy JupyterHub means you are providing Jupyter notebook environments for
multiple users. Often, this includes a desire to configure the user
environment in some way.
environment in a custom way.
Since the `jupyterhub-singleuser` server extends the standard Jupyter notebook
server, most configuration and documentation that applies to Jupyter Notebook
applies to the single-user environments. Configuration of user environments
typically does not occur through JupyterHub itself, but rather through system-
wide configuration of Jupyter, which is inherited by `jupyterhub-singleuser`.
typically does not occur through JupyterHub itself, but rather through the system-wide
configuration of Jupyter, which is inherited by `jupyterhub-singleuser`.
**Tip:** When searching for configuration tips for JupyterHub user
environments, try removing JupyterHub from your search because there are a lot
more people out there configuring Jupyter than JupyterHub and the
configuration is the same.
**Tip:** When searching for configuration tips for JupyterHub user environments, you might want to remove JupyterHub from your search because there are a lot more people out there configuring Jupyter than JupyterHub and the configuration is the same.
This section will focus on user environments, including:
This section will focus on user environments, which includes the following:
- Installing packages
- Configuring Jupyter and IPython
- Installing kernelspecs
- Using containers vs. multi-user hosts
- [Installing packages](#installing-packages)
- [Configuring Jupyter and IPython](#configuring-jupyter-and-ipython)
- [Installing kernelspecs](#installing-kernelspecs)
- [Using containers vs. multi-user hosts](#multi-user-hosts-vs-containers)
## Installing packages
To make packages available to users, you generally will install packages
system-wide or in a shared environment.
To make packages available to users, you will typically install packages system-wide or in a shared environment.
This installation location should always be in the same environment that
This installation location should always be in the same environment where
`jupyterhub-singleuser` itself is installed in, and must be _readable and
executable_ by your users. If you want users to be able to install additional
packages, it must also be _writable_ by your users.
executable_ by your users. If you want your users to be able to install additional
packages, the installation location must also be _writable_ by your users.
If you are using a standard system Python install, you would use:
If you are using a standard Python installation on your system, use the following command:
```bash
sudo python3 -m pip install numpy
```
to install the numpy package in the default system Python 3 environment
to install the numpy package in the default Python 3 environment on your system
(typically `/usr/local`).
You may also use conda to install packages. If you do, you should make sure
that the conda environment has appropriate permissions for users to be able to
run Python code in the env.
Alternatively, You may also use conda to install packages. To do this, ensure that the conda environment has appropriate users permissions needed to run Python code in the environment.
## Configuring Jupyter and IPython
@@ -51,15 +45,9 @@ run Python code in the env.
and [IPython](https://ipython.readthedocs.io/en/stable/development/config.html)
have their own configuration systems.
As a JupyterHub administrator, you will typically want to install and configure
environments for all JupyterHub users. For example, you wish for each student in
a class to have the same user environment configuration.
Jupyter and IPython support **"system-wide"** locations for configuration, which
is the logical place to put global configuration that you want to affect all
users. It's generally more efficient to configure user environments "system-wide",
and it's a good idea to avoid creating files in users' home directories.
As a JupyterHub administrator, you will typically want to install and configure environments for all JupyterHub users. For example, let's say you wish for each student in a class to have the same user environment configuration.
Jupyter and IPython support **"system-wide"** locations for configuration, which is the logical place to put global configuration that you want to affect all users. It's generally more efficient to configure user environments "system-wide", and it's a good practice to avoid creating files in the users' home directories.
The typical locations for these config files are:
- **system-wide** in `/etc/{jupyter|ipython}`
@@ -67,8 +55,7 @@ The typical locations for these config files are:
### Example: Enable an extension system-wide
For example, to enable the `cython` IPython extension for all of your users,
create the file `/etc/ipython/ipython_config.py`:
For example, to enable the `cython` IPython extension for all of your users, create the file `/etc/ipython/ipython_config.py`:
```python
c.InteractiveShellApp.extensions.append("cython")
@@ -77,21 +64,18 @@ c.InteractiveShellApp.extensions.append("cython")
### Example: Enable a Jupyter notebook configuration setting for all users
:::{note}
These examples configure the Jupyter ServerApp,
which is used by JupyterLab, the default in JupyterHub 2.0.
These examples configure the Jupyter ServerApp, which is used by JupyterLab, the default in JupyterHub 2.0.
If you are using the classing Jupyter Notebook server,
the same things should work,
with the following substitutions:
- Where you see `jupyter_server_config`, use `jupyter_notebook_config`
- Where you see `NotebookApp`, use `ServerApp`
- Search for `jupyter_server_config`, and replace with `jupyter_notebook_config`
- Search for `NotebookApp`, and replace with `ServerApp`
:::
To enable Jupyter notebook's internal idle-shutdown behavior (requires
notebook ≥ 5.4), set the following in the `/etc/jupyter/jupyter_server_config.py`
file:
To enable Jupyter notebook's internal idle-shutdown behavior (requires notebook ≥ 5.4), set the following in the `/etc/jupyter/jupyter_server_config.py` file:
```python
# shutdown the server after no activity for an hour
@@ -104,16 +88,14 @@ c.MappingKernelManager.cull_interval = 2 * 60
## Installing kernelspecs
You may have multiple Jupyter kernels installed and want to make sure that
they are available to all of your users. This means installing kernelspecs
either system-wide (e.g. in /usr/local/) or in the `sys.prefix` of JupyterHub
You may have multiple Jupyter kernels installed and want to make sure that they are available to all of your users. This means installing kernelspecs either system-wide (e.g. in /usr/local/) or in the `sys.prefix` of JupyterHub
itself.
Jupyter kernelspec installation is system wide by default, but some kernels
Jupyter kernelspec installation is system-wide by default, but some kernels
may default to installing kernelspecs in your home directory. These will need
to be moved system-wide to ensure that they are accessible.
You can see where your kernelspecs are with:
To see where your kernelspecs are, you can use the following command:
```bash
jupyter kernelspec list
@@ -121,8 +103,7 @@ jupyter kernelspec list
### Example: Installing kernels system-wide
Assuming I have a Python 2 and Python 3 environment that I want to make
sure are available, I can install their specs system-wide (in /usr/local) with:
Let's assume that I have a Python 2 and Python 3 environment that I want to make sure are available, I can install their specs **system-wide** (in /usr/local) using the following command:
```bash
/path/to/python3 -m ipykernel install --prefix=/usr/local
@@ -141,31 +122,25 @@ How you configure user environments for each category can differ a bit
depending on what Spawner you are using.
The first category is a **shared system (multi-user host)** where
each user has a JupyterHub account and a home directory as well as being
each user has a JupyterHub account, a home directory as well as being
a real system user. In this example, shared configuration and installation
must be in a 'system-wide' location, such as `/etc/` or `/usr/local`
must be in a 'system-wide' location, such as `/etc/`, or `/usr/local`
or a custom prefix such as `/opt/conda`.
When JupyterHub uses **container-based** Spawners (e.g. KubeSpawner or
DockerSpawner), the 'system-wide' environment is really the container image
which you are using for users.
DockerSpawner), the 'system-wide' environment is really the container image used for users.
In both cases, you want to _avoid putting configuration in user home
directories_ because users can change those configuration settings. Also,
home directories typically persist once they are created, so they are
difficult for admins to update later.
directories_ because users can change those configuration settings. Also, home directories typically persist once they are created, thereby making it difficult for admins to update later.
## Named servers
By default, in a JupyterHub deployment each user has exactly one server.
By default, in a JupyterHub deployment, each user has one server only.
JupyterHub can, however, have multiple servers per user.
This is most useful in deployments where users can configure the environment
in which their server will start (e.g. resource requests on an HPC cluster),
so that a given user can have multiple configurations running at the same time,
without having to stop and restart their one server.
This is mostly useful in deployments where users can configure the environment in which their server will start (e.g. resource requests on an HPC cluster), so that a given user can have multiple configurations running at the same time, without having to stop and restart their own server.
To allow named servers:
To allow named servers, include this code snippet in your config file:
```python
c.JupyterHub.allow_named_servers = True
@@ -181,15 +156,15 @@ as well as the admin page:
![named servers on the admin page](../images/named-servers-admin.png)
Named servers can be accessed, created, started, stopped, and deleted
from these pages. Activity tracking is now per-server as well.
from these pages. Activity tracking is now per server as well.
The number of named servers per user can be limited by setting a constant value:
To limit the number of **named server** per user by setting a constant value, include this code snippet in your config file:
```python
c.JupyterHub.named_server_limit_per_user = 5
```
or a callable/awaitable based on the handler object:
Alternatively, to use a callable/awaitable based on the handler object, include this code snippet in your config file:
```python
def named_server_limit_per_user_fn(handler):
@@ -207,12 +182,13 @@ If `named_server_limit_per_user` is set to `0`, no limit is enforced.
(classic-notebook-ui)=
## Switching back to classic notebook
## Switching back to the classic notebook
By default the single-user server launches JupyterLab,
By default, the single-user server launches JupyterLab,
which is based on [Jupyter Server][].
This is the default server when running JupyterHub ≥ 2.0.
You can switch to using the legacy Jupyter Notebook server by setting the `JUPYTERHUB_SINGLEUSER_APP` environment variable
To switch to using the legacy Jupyter Notebook server, you can set the `JUPYTERHUB_SINGLEUSER_APP` environment variable
(in the single-user environment) to:
```bash
@@ -223,19 +199,20 @@ export JUPYTERHUB_SINGLEUSER_APP='notebook.notebookapp.NotebookApp'
[jupyter notebook]: https://jupyter-notebook.readthedocs.io
:::{versionchanged} 2.0
JupyterLab is now the default singleuser UI, if available,
JupyterLab is now the default single-user UI, if available,
which is based on the [Jupyter Server][],
no longer the legacy [Jupyter Notebook][] server.
JupyterHub prior to 2.0 launched the legacy notebook server (`jupyter notebook`),
and Jupyter server could be selected by specifying
and the Jupyter server could be selected by specifying the following:
```python
# jupyterhub_config.py
c.Spawner.cmd = ["jupyter-labhub"]
```
or for an otherwise customized Jupyter Server app,
set the environment variable:
Alternatively, for an otherwise customized Jupyter Server app,
set the environment variable using the following command:
```bash
export JUPYTERHUB_SINGLEUSER_APP='jupyter_server.serverapp.ServerApp'

54
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@@ -30,19 +30,19 @@ Some relevant points:
Here are some key definitions to keep in mind when we are talking about OAuth.
You can also read more detail [here](https://www.oauth.com/oauth2-servers/definitions/).
- **provider** the entity responsible for managing identity and authorization,
- **provider**: The entity responsible for managing identity and authorization,
always a web server.
JupyterHub is _always_ an oauth provider for JupyterHub's components.
When OAuthenticator is used, an external service, such as GitHub or KeyCloak, is also an oauth provider.
- **client** An entity that requests OAuth **tokens** on a user's behalf,
- **client**: An entity that requests OAuth **tokens** on a user's behalf,
generally a web server of some kind.
OAuth **clients** are services that _delegate_ authentication and/or authorization
to an OAuth **provider**.
JupyterHub _services_ or single-user _servers_ are OAuth **clients** of the JupyterHub **provider**.
When OAuthenticator is used, JupyterHub is itself _also_ an OAuth **client** for the external oauth **provider**, e.g. GitHub.
- **browser** A user's web browser, which makes requests and stores things like cookies
- **token** The secret value used to represent a user's authorization. This is the final product of the OAuth process.
- **code** A short-lived temporary secret that the **client** exchanges
- **browser**: A user's web browser, which makes requests and stores things like cookies.
- **token**: The secret value used to represent a user's authorization. This is the final product of the OAuth process.
- **code**: A short-lived temporary secret that the **client** exchanges
for a **token** at the conclusion of oauth,
in what's generally called the "oauth callback handler."
@@ -56,8 +56,8 @@ A single oauth flow generally goes like this:
1. A **browser** makes an HTTP request to an oauth **client**.
2. There are no credentials, so the client _redirects_ the browser to an "authorize" page on the oauth **provider** with some extra information:
- the oauth **client id** of the client itself
- the **redirect uri** to be redirected back to after completion
- the oauth **client id** of the client itself.
- the **redirect uri** to be redirected back to after completion.
- the **scopes** requested, which the user should be presented with to confirm.
This is the "X would like to be able to Y on your behalf. Allow this?" page you see on all the "Login with ..." pages around the Internet.
3. During this authorize step,
@@ -77,8 +77,8 @@ That's the end of the requests made between the **browser** and the **provider**
At this point:
- The browser is authenticated with the _provider_
- The user's authorized permissions are recorded in an _oauth code_
- The browser is authenticated with the _provider_.
- The user's authorized permissions are recorded in an _oauth code_.
- The _provider_ knows that the given oauth client's requested permissions have been granted, but the client doesn't know this yet.
- All requests so far have been made directly by the browser.
No requests have originated at the client or provider.
@@ -86,8 +86,8 @@ At this point:
### OAuth Client Handles Callback Request
Now we get to finish the OAuth process.
Let's dig into what the oauth client does when it handles
the oauth callback request with the
Let's dig into what the OAuth client does when it handles
the OAuth callback request.
- The OAuth client receives the _code_ and makes an API request to the _provider_ to exchange the code for a real _token_.
This is the first direct request between the OAuth _client_ and the _provider_.
@@ -113,24 +113,24 @@ So that's _one_ OAuth process.
## Full sequence of OAuth in JupyterHub
Let's go through the above oauth process in JupyterHub,
Let's go through the above OAuth process in JupyterHub,
with specific examples of each HTTP request and what information is contained.
For bonus points, we are using the double-oauth example of JupyterHub configured with GitHubOAuthenticator.
For bonus points, we are using the double-OAuth example of JupyterHub configured with GitHubOAuthenticator.
To disambiguate, we will call the OAuth process where JupyterHub is the **provider** "internal oauth,"
and the one with JupyterHub as a **client** "external oauth."
To disambiguate, we will call the OAuth process where JupyterHub is the **provider** "internal OAuth,"
and the one with JupyterHub as a **client** "external OAuth."
Our starting point:
- a user's single-user server is running. Let's call them `danez`
- jupyterhub is running with GitHub as an oauth provider (this means two full instances of oauth),
- Danez has a fresh browser session with no cookies yet
- Jupyterhub is running with GitHub as an OAuth provider (this means two full instances of OAuth),
- Danez has a fresh browser session with no cookies yet.
First request:
- browser->single-user server running JupyterLab or Jupyter Classic
- `GET /user/danez/notebooks/mynotebook.ipynb`
- no credentials, so single-user server (as an oauth **client**) starts internal oauth process with JupyterHub (the **provider**)
- no credentials, so single-user server (as an OAuth **client**) starts internal OAuth process with JupyterHub (the **provider**)
- response: 302 redirect -> `/hub/api/oauth2/authorize`
with:
- client-id=`jupyterhub-user-danez`
@@ -138,9 +138,9 @@ First request:
Second request, following redirect:
- browser->jupyterhub
- browser->JupyterHub
- `GET /hub/api/oauth2/authorize`
- no credentials, so jupyterhub starts external oauth process _with GitHub_
- no credentials, so JupyterHub starts external OAuth process _with GitHub_
- response: 302 redirect -> `https://github.com/login/oauth/authorize`
with:
- client-id=`jupyterhub-client-uuid`
@@ -154,8 +154,8 @@ c.JupyterHub.authenticator_class = 'github'
```
That means authenticating a request to the Hub itself starts
a _second_, external oauth process with GitHub as a provider.
This external oauth process is optional, though.
a _second_, external OAuth process with GitHub as a provider.
This external OAuth process is optional, though.
If you were using the default username+password PAMAuthenticator,
this redirect would have been to `/hub/login` instead, to present the user
with a login form.
@@ -171,7 +171,7 @@ Here, GitHub prompts for login and asks for confirmation of authorization
After successful authorization
(either by looking up a pre-existing authorization,
or recording it via form submission)
GitHub issues an **oauth code** and redirects to `/hub/oauth_callback?code=github-code`
GitHub issues an **OAuth code** and redirects to `/hub/oauth_callback?code=github-code`
Next request:
@@ -194,9 +194,9 @@ The second:
- request made with access **token** in the `Authorization` header
- response is the user model, including username, email, etc.
Now the external oauth callback request completes with:
Now the external OAuth callback request completes with:
- set cookie on `/hub/` path, recording jupyterhub authentication so we don't need to do external oauth with GitHub again for a while
- set cookie on `/hub/` path, recording jupyterhub authentication so we don't need to do external OAuth with GitHub again for a while
- redirect -> `/hub/api/oauth2/authorize`
🎉 At this point, we have completed our first OAuth flow! 🎉
@@ -211,14 +211,14 @@ Now, we get our first repeated request:
2. automatically accepts authorization (shortcut taken when a user is visiting their own server)
- redirect -> `/user/danez/oauth_callback?code=jupyterhub-code`
Here, we start the same oauth callback process as before, but at Danez's single-user server for the _internal_ oauth
Here, we start the same OAuth callback process as before, but at Danez's single-user server for the _internal_ OAuth.
- browser->single-user server
- `GET /user/danez/oauth_callback`
(in handler)
Inside the internal oauth callback handler,
Inside the internal OAuth callback handler,
Danez's server makes two API requests to JupyterHub:
The first:

68
docs/source/reference/spawners.md
View File

@@ -4,9 +4,9 @@ A [Spawner][] starts each single-user notebook server.
The Spawner represents an abstract interface to a process,
and a custom Spawner needs to be able to take three actions:
- start the process
- poll whether the process is still running
- stop the process
- start a process
- poll whether a process is still running
- stop a process
## Examples
@@ -15,9 +15,9 @@ Some examples include:
- [DockerSpawner](https://github.com/jupyterhub/dockerspawner) for spawning user servers in Docker containers
- `dockerspawner.DockerSpawner` for spawning identical Docker containers for
each users
each user
- `dockerspawner.SystemUserSpawner` for spawning Docker containers with an
environment and home directory for each users
environment and home directory for each user
- both `DockerSpawner` and `SystemUserSpawner` also work with Docker Swarm for
launching containers on remote machines
- [SudoSpawner](https://github.com/jupyterhub/sudospawner) enables JupyterHub to
@@ -34,7 +34,7 @@ Some examples include:
### Spawner.start
`Spawner.start` should start the single-user server for a single user.
`Spawner.start` should start a single-user server for a single user.
Information about the user can be retrieved from `self.user`,
an object encapsulating the user's name, authentication, and server info.
@@ -69,13 +69,13 @@ via relaxing the `Spawner.start_timeout` config value.
#### Note on IPs and ports
`Spawner.ip` and `Spawner.port` attributes set the _bind_ url,
`Spawner.ip` and `Spawner.port` attributes set the _bind_ URL,
which the single-user server should listen on
(passed to the single-user process via the `JUPYTERHUB_SERVICE_URL` environment variable).
The _return_ value is the ip and port (or full url) the Hub should _connect to_.
The _return_ value is the IP and port (or full URL) the Hub should _connect to_.
These are not necessarily the same, and usually won't be in any Spawner that works with remote resources or containers.
The default for Spawner.ip, and Spawner.port is `127.0.0.1:{random}`,
The default for `Spawner.ip`, and `Spawner.port` is `127.0.0.1:{random}`,
which is appropriate for Spawners that launch local processes,
where everything is on localhost and each server needs its own port.
For remote or container Spawners, it will often make sense to use a different value,
@@ -111,7 +111,7 @@ class MySpawner(Spawner):
#### Exception handling
When `Spawner.start` raises an Exception, a message can be passed on to the user via the exception via a `.jupyterhub_html_message` or `.jupyterhub_message` attribute.
When `Spawner.start` raises an Exception, a message can be passed on to the user via the exception using a `.jupyterhub_html_message` or `.jupyterhub_message` attribute.
When the Exception has a `.jupyterhub_html_message` attribute, it will be rendered as HTML to the user.
@@ -121,11 +121,11 @@ If both attributes are not present, the Exception will be shown to the user as u
### Spawner.poll
`Spawner.poll` should check if the spawner is still running.
`Spawner.poll` checks if the spawner is still running.
It should return `None` if it is still running,
and an integer exit status, otherwise.
For the local process case, `Spawner.poll` uses `os.kill(PID, 0)`
In the case of local processes, `Spawner.poll` uses `os.kill(PID, 0)`
to check if the local process is still running. On Windows, it uses `psutil.pid_exists`.
### Spawner.stop
@@ -141,7 +141,7 @@ A JSON-able dictionary of state can be used to store persisted information.
Unlike start, stop, and poll methods, the state methods must not be coroutines.
For the single-process case, the Spawner state is only the process ID of the server:
In the case of single processes, the Spawner state is only the process ID of the server:
```python
def get_state(self):
@@ -267,8 +267,8 @@ Spawners mainly do one thing: launch a command in an environment.
The command-line is constructed from user configuration:
- Spawner.cmd (default: `['jupterhub-singleuser']`)
- Spawner.args (cli args to pass to the cmd, default: empty)
- Spawner.cmd (default: `['jupyterhub-singleuser']`)
- Spawner.args (CLI args to pass to the cmd, default: empty)
where the configuration:
@@ -283,7 +283,7 @@ would result in spawning the command:
my-singleuser-wrapper --debug --flag
```
The `Spawner.get_args()` method is how Spawner.args is accessed,
The `Spawner.get_args()` method is how `Spawner.args` is accessed,
and can be used by Spawners to customize/extend user-provided arguments.
Prior to 2.0, JupyterHub unconditionally added certain options _if specified_ to the command-line,
@@ -297,36 +297,36 @@ Additional variables can be specified via the `Spawner.environment` configuratio
The process environment is returned by `Spawner.get_env`, which specifies the following environment variables:
- JUPYTERHUB*SERVICE_URL - the \_bind* url where the server should launch its http server (`http://127.0.0.1:12345`).
This includes Spawner.ip and Spawner.port; _new in 2.0, prior to 2.0 ip,port were on the command-line and only if specified_
- JUPYTERHUB*SERVICE_URL - the \_bind* URL where the server should launch its HTTP server (`http://127.0.0.1:12345`).
This includes `Spawner.ip` and `Spawner.port`; _new in 2.0, prior to 2.0 IP, port were on the command-line and only if specified_
- JUPYTERHUB_SERVICE_PREFIX - the URL prefix the service will run on (e.g. `/user/name/`)
- JUPYTERHUB_USER - the JupyterHub user's username
- JUPYTERHUB_SERVER_NAME - the server's name, if using named servers (default server has an empty name)
- JUPYTERHUB_API_URL - the full url for the JupyterHub API (http://17.0.0.1:8001/hub/api)
- JUPYTERHUB_BASE_URL - the base url of the whole jupyterhub deployment, i.e. the bit before `hub/` or `user/`,
as set by c.JupyterHub.base_url (default: `/`)
- JUPYTERHUB_API_URL - the full URL for the JupyterHub API (http://17.0.0.1:8001/hub/api)
- JUPYTERHUB_BASE_URL - the base URL of the whole jupyterhub deployment, i.e. the bit before `hub/` or `user/`,
as set by `c.JupyterHub.base_url` (default: `/`)
- JUPYTERHUB_API_TOKEN - the API token the server can use to make requests to the Hub.
This is also the OAuth client secret.
- JUPYTERHUB_CLIENT_ID - the OAuth client ID for authenticating visitors.
- JUPYTERHUB_OAUTH_CALLBACK_URL - the callback URL to use in oauth, typically `/user/:name/oauth_callback`
- JUPYTERHUB_OAUTH_CALLBACK_URL - the callback URL to use in OAuth, typically `/user/:name/oauth_callback`
- JUPYTERHUB_OAUTH_ACCESS_SCOPES - the scopes required to access the server (called JUPYTERHUB_OAUTH_SCOPES prior to 3.0)
- JUPYTERHUB_OAUTH_CLIENT_ALLOWED_SCOPES - the scopes the service is allowed to request.
If no scopes are requested explicitly, these scopes will be requested.
Optional environment variables, depending on configuration:
- JUPYTERHUB*SSL*[KEYFILE|CERTFILE|CLIENT_CI] - SSL configuration, when internal_ssl is enabled
- JUPYTERHUB_ROOT_DIR - the root directory of the server (notebook directory), when Spawner.notebook_dir is defined (new in 2.0)
- JUPYTERHUB_DEFAULT_URL - the default URL for the server (for redirects from /user/:name/),
if Spawner.default_url is defined
(new in 2.0, previously passed via cli)
- JUPYTERHUB_DEBUG=1 - generic debug flag, sets maximum log level when Spawner.debug is True
(new in 2.0, previously passed via cli)
- JUPYTERHUB*SSL*[KEYFILE|CERTFILE|CLIENT_CI] - SSL configuration, when `internal_ssl` is enabled
- JUPYTERHUB_ROOT_DIR - the root directory of the server (notebook directory), when `Spawner.notebook_dir` is defined (new in 2.0)
- JUPYTERHUB_DEFAULT_URL - the default URL for the server (for redirects from `/user/:name/`),
if `Spawner.default_url` is defined
(new in 2.0, previously passed via CLI)
- JUPYTERHUB_DEBUG=1 - generic debug flag, sets maximum log level when `Spawner.debug` is True
(new in 2.0, previously passed via CLI)
- JUPYTERHUB_DISABLE_USER_CONFIG=1 - disable loading user config,
sets maximum log level when Spawner.debug is True (new in 2.0,
previously passed via cli)
sets maximum log level when `Spawner.debug` is True (new in 2.0,
previously passed via CLI)
- JUPYTERHUB*[MEM|CPU]*[LIMIT_GUARANTEE] - the values of cpu and memory limits and guarantees.
- JUPYTERHUB*[MEM|CPU]*[LIMIT_GUARANTEE] - the values of CPU and memory limits and guarantees.
These are not expected to be enforced by the process,
but are made available as a hint,
e.g. for resource monitoring extensions.
@@ -338,7 +338,7 @@ guarantees on resources, such as CPU and memory. To provide a consistent
experience for sysadmins and users, we provide a standard way to set and
discover these resource limits and guarantees, such as for memory and CPU.
For the limits and guarantees to be useful, **the spawner must implement
support for them**. For example, LocalProcessSpawner, the default
support for them**. For example, `LocalProcessSpawner`, the default
spawner, does not support limits and guarantees. One of the spawners
that supports limits and guarantees is the `systemdspawner`.
@@ -367,7 +367,7 @@ limits or guarantees are provided, and no environment values are set.
`c.Spawner.cpu_limit`: In supported spawners, you can set
`c.Spawner.cpu_limit` to limit the total number of cpu-cores that a
single-user notebook server can use. These can be fractional - `0.5` means 50%
of one CPU core, `4.0` is 4 cpu-cores, etc. This value is also set in the
of one CPU core, `4.0` is 4 CPU-cores, etc. This value is also set in the
single-user notebook server's environment variable `CPU_LIMIT`. The limit does
not claim that you will be able to use all the CPU up to your limit as other
higher priority applications might be taking up CPU.

2
docs/source/reference/templates.md
View File

@@ -84,5 +84,5 @@ template (for example, `login.html`) with:
```
Extending `page.html` puts the message on all pages, but note that
extending `page.html` take precedence over an extension of a specific
extending `page.html` takes precedence over an extension of a specific
page (unlike the variable-based approach above).

86
docs/source/reference/urls.md
View File

@@ -2,13 +2,13 @@
This document describes how JupyterHub routes requests.
This does not include the [REST API](./rest.md) urls.
This does not include the [REST API](./rest.md) URLs.
In general, all URLs can be prefixed with `c.JupyterHub.base_url` to
run the whole JupyterHub application on a prefix.
All authenticated handlers redirect to `/hub/login` to login users
prior to being redirected back to the originating page.
All authenticated handlers redirect to `/hub/login` to log-in users
before being redirected back to the originating page.
The returned request should preserve all query parameters.
## `/`
@@ -25,12 +25,12 @@ This is an authenticated URL.
This handler redirects users to the default URL of the application,
which defaults to the user's default server.
That is, it redirects to `/hub/spawn` if the user's server is not running,
or the server itself (`/user/:name`) if the server is running.
That is, the handler redirects to `/hub/spawn` if the user's server is not running,
or to the server itself (`/user/:name`) if the server is running.
This default url behavior can be customized in two ways:
This default URL behavior can be customized in two ways:
To redirect users to the JupyterHub home page (`/hub/home`)
First, to redirect users to the JupyterHub home page (`/hub/home`)
instead of spawning their server,
set `redirect_to_server` to False:
@@ -40,7 +40,7 @@ c.JupyterHub.redirect_to_server = False
This might be useful if you have a Hub where you expect
users to be managing multiple server configurations
and automatic spawning is not desirable.
but automatic spawning is not desirable.
Second, you can customise the landing page to any page you like,
such as a custom service you have deployed e.g. with course information:
@@ -57,7 +57,7 @@ By default, the Hub home page has just one or two buttons
for starting and stopping the user's server.
If named servers are enabled, there will be some additional
tools for management of named servers.
tools for management of the named servers.
_Version added: 1.0_ named server UI is new in 1.0.
@@ -65,34 +65,34 @@ _Version added: 1.0_ named server UI is new in 1.0.
This is the JupyterHub login page.
If you have a form-based username+password login,
such as the default PAMAuthenticator,
such as the default [PAMAuthenticator](https://en.wikipedia.org/wiki/Pluggable_authentication_module),
this page will render the login form.
![A login form](../images/login-form.png)
If login is handled by an external service,
e.g. with OAuth, this page will have a button,
declaring "Login with ..." which users can click
to login with the chosen service.
declaring "Log in with ..." which users can click
to log in with the chosen service.
![A login redirect button](../images/login-button.png)
If you want to skip the user-interaction to initiate logging in
via the button, you can set
If you want to skip the user interaction and initiate login
via the button, you can set:
```python
c.Authenticator.auto_login = True
```
This can be useful when the user is "already logged in" via some mechanism,
but a handshake via redirects is necessary to complete the authentication with JupyterHub.
This can be useful when the user is "already logged in" via some mechanism.
However, a handshake via `redirects` is necessary to complete the authentication with JupyterHub.
## `/hub/logout`
Visiting `/hub/logout` clears cookies from the current browser.
Visiting `/hub/logout` clears [cookies](https://en.wikipedia.org/wiki/HTTP_cookie) from the current browser.
Note that **logging out does not stop a user's server(s)** by default.
If you would like to shutdown user servers on logout,
If you would like to shut down user servers on logout,
you can enable this behavior with:
```python
@@ -105,8 +105,8 @@ does not mean the user is no longer actively using their server from another mac
## `/user/:username[/:servername]`
If a user's server is running, this URL is handled by the user's given server,
not the Hub.
The username is the first part and, if using named servers,
not by the Hub.
The username is the first part, and if using named servers,
the server name is the second part.
If the user's server is _not_ running, this will be redirected to `/hub/user/:username/...`
@@ -120,11 +120,11 @@ if the specified server were running).
Handling this URL is the most complicated condition in JupyterHub,
because there can be many states:
1. server is not active
1. the server is not active
a. user matches
b. user doesn't match
2. server is ready
3. server is pending, but not ready
2. the server is ready
3. the server is pending, but not ready
If the server is pending spawn,
the browser will be redirected to `/hub/spawn-pending/:username/:servername`
@@ -140,39 +140,37 @@ Some checks are performed and a delay is added before redirecting back to `/user
If something is really wrong, this can result in a redirect loop.
Visiting this page will never result in triggering the spawn of servers
without additional user action (i.e. clicking the link on the page)
without additional user action (i.e. clicking the link on the page).
![Visiting a URL for a server that's not running](../images/not-running.png)
_Version changed: 1.0_
Prior to 1.0, this URL itself was responsible for spawning servers,
and served the progress page if it was pending,
redirected to running servers, and
This was useful because it made sure that requested servers were restarted after they stopped,
but could also be harmful because unused servers would continuously be restarted if e.g.
an idle JupyterLab frontend were open pointed at it,
which constantly makes polling requests.
Prior to 1.0, this URL itself was responsible for spawning servers.
If the progress page was pending, the URL redirected it to running servers.
This was useful because it made sure that the requested servers were restarted after they stopped.
However, it could also be harmful because unused servers would continuously be restarted if e.g.
an idle JupyterLab frontend that constantly makes polling requests was openly pointed at it.
### Special handling of API requests
Requests to `/user/:username[/:servername]/api/...` are assumed to be
from applications connected to stopped servers.
These are failed with 503 and an informative JSON error message
indicating how to spawn the server.
This is meant to help applications such as JupyterLab
These requests fail with a `503` status code and an informative JSON error message
that indicates how to spawn the server.
This is meant to help applications such as JupyterLab,
that are connected to a server that has stopped.
_Version changed: 1.0_
JupyterHub 0.9 failed these API requests with status 404,
but 1.0 uses 503.
JupyterHub version 0.9 failed these API requests with status `404`,
but version 1.0 uses 503.
## `/user-redirect/...`
This URL is for sharing a URL that will redirect a user
The `/user-redirect/...` URL is for sharing a URL that will redirect a user
to a path on their own default server.
This is useful when users have the same file at the same URL on their servers,
This is useful when different users have the same file at the same URL on their servers,
and you want a single link to give to any user that will open that file on their server.
e.g. a link to `/user-redirect/notebooks/Index.ipynb`
@@ -194,7 +192,7 @@ that is intended to make it possible.
### `/hub/spawn[/:username[/:servername]]`
Requesting `/hub/spawn` will spawn the default server for the current user.
If `username` and optionally `servername` are specified,
If the `username` and optionally `servername` are specified,
then the specified server for the specified user will be spawned.
Once spawn has been requested,
the browser is redirected to `/hub/spawn-pending/...`.
@@ -207,7 +205,7 @@ and a POST request will trigger the actual spawn and redirect.
_Version added: 1.0_
1.0 adds the ability to specify username and servername.
1.0 adds the ability to specify `username` and `servername`.
Prior to 1.0, only `/hub/spawn` was recognized for the default server.
_Version changed: 1.0_
@@ -247,7 +245,7 @@ against the [JupyterHub REST API](./rest.md).
Administrators can take various administrative actions from this page:
1. add/remove users
2. grant admin privileges
3. start/stop user servers
4. shutdown JupyterHub itself
- add/remove users
- grant admin privileges
- start/stop user servers
- shutdown JupyterHub itself

26
docs/source/reference/websecurity.md
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@@ -5,7 +5,7 @@ The **Security Overview** section helps you learn about:
- the design of JupyterHub with respect to web security
- the semi-trusted user
- the available mitigations to protect untrusted users from each other
- the value of periodic security audits.
- the value of periodic security audits
This overview also helps you obtain a deeper understanding of how JupyterHub
works.
@@ -16,9 +16,9 @@ JupyterHub is designed to be a _simple multi-user server for modestly sized
groups_ of **semi-trusted** users. While the design reflects serving semi-trusted
users, JupyterHub is not necessarily unsuitable for serving **untrusted** users.
Using JupyterHub with **untrusted** users does mean more work by the
Using JupyterHub with **untrusted** users does mean more work for the
administrator. Much care is required to secure a Hub, with extra caution on
protecting users from each other as the Hub is serving untrusted users.
protecting users from each other, since the Hub serves untrusted users.
One aspect of JupyterHub's _design simplicity_ for **semi-trusted** users is that
the Hub and single-user servers are placed in a _single domain_, behind a
@@ -32,7 +32,7 @@ servers) as a single website (i.e. single domain).
To protect users from each other, a user must **never** be able to write arbitrary
HTML and serve it to another user on the Hub's domain. JupyterHub's
authentication setup prevents a user writing arbitrary HTML and serving it to
authentication setup prevents a user from writing arbitrary HTML and serving it to
another user because only the owner of a given single-user notebook server is
allowed to view user-authored pages served by the given single-user notebook
server.
@@ -47,7 +47,7 @@ ensure that:
- If the `PATH` is used to resolve the single-user executable (instead of
using an absolute path), a user **may not** create new files in any `PATH`
directory that precedes the directory containing `jupyterhub-singleuser`.
- A user may not modify environment variables (e.g. PATH, PYTHONPATH) for
- A user may not modify environment variables (e.g. `PATH`, `PYTHONPATH`) for
their single-user server.
- A user **may not** modify the configuration of the notebook server
(the `~/.jupyter` or `JUPYTER_CONFIG_DIR` directory).
@@ -58,7 +58,7 @@ If any additional services are run on the same domain as the Hub, the services
## Mitigate security issues
Several approaches to mitigating these issues with configuration
The several approaches to mitigating security issues with configuration
options provided by JupyterHub include:
### Enable subdomains
@@ -76,10 +76,10 @@ resolves the cross-site issues.
### Disable user config
If subdomains are not available or not desirable, JupyterHub provides a
If subdomains are unavailable or undesirable, JupyterHub provides a
configuration option `Spawner.disable_user_config`, which can be set to prevent
the user-owned configuration files from being loaded. After implementing this
option, PATHs and package installation and PATHs are the other things that the
option, `PATH`s and package installation are the other things that the
admin must enforce.
### Prevent spawners from evaluating shell configuration files
@@ -101,8 +101,8 @@ pose additional risk to the web application's security.
### Encrypt internal connections with SSL/TLS
By default, all communication on the server, between the proxy, hub, and single
-user notebooks is performed unencrypted. Setting the `internal_ssl` flag in
By default, all communications on the server, between the proxy, hub, and single
-user notebooks are performed unencrypted. Setting the `internal_ssl` flag in
`jupyterhub_config.py` secures the aforementioned routes. Turning this
feature on does require that the enabled `Spawner` can use the certificates
generated by the `Hub` (the default `LocalProcessSpawner` can, for instance).
@@ -119,8 +119,8 @@ extend to securing the `tcp` sockets as well.
## Security audits
We recommend that you do periodic reviews of your deployment's security. It's
good practice to keep JupyterHub, configurable-http-proxy, and nodejs
versions up to date.
good practice to keep [JupyterHub](https://readthedocs.org/projects/jupyterhub/), [configurable-http-proxy][], and [nodejs
versions](https://github.com/nodejs/Release) up to date.
A handy website for testing your deployment is
[Qualsys' SSL analyzer tool](https://www.ssllabs.com/ssltest/analyze.html).
@@ -129,7 +129,7 @@ A handy website for testing your deployment is
## Vulnerability reporting
If you believe you’ve found a security vulnerability in JupyterHub, or any
If you believe you have found a security vulnerability in JupyterHub, or any
Jupyter project, please report it to
[security@ipython.org](mailto:security@ipython.org). If you prefer to encrypt
your security reports, you can use [this PGP public

170
docs/source/troubleshooting.md
View File

@@ -1,35 +1,9 @@
# Troubleshooting
When troubleshooting, you may see unexpected behaviors or receive an error
message. This section provide links for identifying the cause of the
message. This section provides links for identifying the cause of the
problem and how to resolve it.
[_Behavior_](#behavior)
- JupyterHub proxy fails to start
- sudospawner fails to run
- What is the default behavior when none of the lists (admin, allowed,
allowed groups) are set?
- JupyterHub Docker container not accessible at localhost
[_Errors_](#errors)
- 500 error after spawning my single-user server
[_How do I...?_](#how-do-i)
- Use a chained SSL certificate
- Install JupyterHub without a network connection
- I want access to the whole filesystem, but still default users to their home directory
- How do I increase the number of pySpark executors on YARN?
- How do I use JupyterLab's prerelease version with JupyterHub?
- How do I set up JupyterHub for a workshop (when users are not known ahead of time)?
- How do I set up rotating daily logs?
- Toree integration with HDFS rack awareness script
- Where do I find Docker images and Dockerfiles related to JupyterHub?
[_Troubleshooting commands_](#troubleshooting-commands)
## Behavior
### JupyterHub proxy fails to start
@@ -40,9 +14,9 @@ If you have tried to start the JupyterHub proxy and it fails to start:
`c.JupyterHub.ip = '*'`; if it is, try `c.JupyterHub.ip = ''`
- Try starting with `jupyterhub --ip=0.0.0.0`
**Note**: If this occurs on Ubuntu/Debian, check that the you are using a
recent version of node. Some versions of Ubuntu/Debian come with a version
of node that is very old, and it is necessary to update node.
**Note**: If this occurs on Ubuntu/Debian, check that you are using a
recent version of [Node](https://nodejs.org). Some versions of Ubuntu/Debian come with a very old version
of Node and it is necessary to update Node.
### sudospawner fails to run
@@ -61,24 +35,24 @@ to the config file, `jupyterhub_config.py`.
### What is the default behavior when none of the lists (admin, allowed, allowed groups) are set?
When nothing is given for these lists, there will be no admins, and all users
who can authenticate on the system (i.e. all the unix users on the server with
who can authenticate on the system (i.e. all the Unix users on the server with
a password) will be allowed to start a server. The allowed username set lets you limit
this to a particular set of users, and admin_users lets you specify who
among them may use the admin interface (not necessary, unless you need to do
things like inspect other users' servers, or modify the user list at runtime).
things like inspect other users' servers or modify the user list at runtime).
### JupyterHub Docker container not accessible at localhost
### JupyterHub Docker container is not accessible at localhost
Even though the command to start your Docker container exposes port 8000
(`docker run -p 8000:8000 -d --name jupyterhub jupyterhub/jupyterhub jupyterhub`),
it is possible that the IP address itself is not accessible/visible. As a result
it is possible that the IP address itself is not accessible/visible. As a result,
when you try http://localhost:8000 in your browser, you are unable to connect
even though the container is running properly. One workaround is to explicitly
tell Jupyterhub to start at `0.0.0.0` which is visible to everyone. Try this
command:
`docker run -p 8000:8000 -d --name jupyterhub jupyterhub/jupyterhub jupyterhub --ip 0.0.0.0 --port 8000`
### How can I kill ports from JupyterHub managed services that have been orphaned?
### How can I kill ports from JupyterHub-managed services that have been orphaned?
I started JupyterHub + nbgrader on the same host without containers. When I try to restart JupyterHub + nbgrader with this configuration, errors appear that the service accounts cannot start because the ports are being used.
@@ -92,12 +66,12 @@ Where `<service_port>` is the port used by the nbgrader course service. This con
### Why am I getting a Spawn failed error message?
After successfully logging in to JupyterHub with a compatible authenticators, I get a 'Spawn failed' error message in the browser. The JupyterHub logs have `jupyterhub KeyError: "getpwnam(): name not found: <my_user_name>`.
After successfully logging in to JupyterHub with a compatible authenticator, I get a 'Spawn failed' error message in the browser. The JupyterHub logs have `jupyterhub KeyError: "getpwnam(): name not found: <my_user_name>`.
This issue occurs when the authenticator requires a local system user to exist. In these cases, you need to use a spawner
that does not require an existing system user account, such as `DockerSpawner` or `KubeSpawner`.
### How can I run JupyterHub with sudo but use my current env vars and virtualenv location?
### How can I run JupyterHub with sudo but use my current environment variables and virtualenv location?
When launching JupyterHub with `sudo jupyterhub` I get import errors and my environment variables don't work.
@@ -109,25 +83,11 @@ sudo MY_ENV=abc123 \
/srv/jupyterhub/jupyterhub
```
### How can I view the logs for JupyterHub or the user's Notebook servers when using the DockerSpawner?
Use `docker logs <container>` where `<container>` is the container name defined within `docker-compose.yml`. For example, to view the logs of the JupyterHub container use:
docker logs hub
By default, the user's notebook server is named `jupyter-<username>` where `username` is the user's username within JupyterHub's db. So if you wanted to see the logs for user `foo` you would use:
docker logs jupyter-foo
You can also tail logs to view them in real time using the `-f` option:
docker logs -f hub
## Errors
### 500 error after spawning my single-user server
### Error 500 after spawning my single-user server
You receive a 500 error when accessing the URL `/user/<your_name>/...`.
You receive a 500 error while accessing the URL `/user/<your_name>/...`.
This is often seen when your single-user server cannot verify your user cookie
with the Hub.
@@ -185,10 +145,10 @@ If you receive a 403 error, the API token for the single-user server is likely
invalid. Commonly, the 403 error is caused by resetting the JupyterHub
database (either removing jupyterhub.sqlite or some other action) while
leaving single-user servers running. This happens most frequently when using
DockerSpawner, because Docker's default behavior is to stop/start containers
which resets the JupyterHub database, rather than destroying and recreating
DockerSpawner because Docker's default behavior is to stop/start containers
that reset the JupyterHub database, rather than destroying and recreating
the container every time. This means that the same API token is used by the
server for its whole life, until the container is rebuilt.
server for its whole life until the container is rebuilt.
The fix for this Docker case is to remove any Docker containers seeing this
issue (typically all containers created before a certain point in time):
@@ -201,14 +161,14 @@ your server again.
##### Proxy settings (403 GET)
When your whole JupyterHub sits behind a organization proxy (_not_ a reverse proxy like NGINX as part of your setup and _not_ the configurable-http-proxy) the environment variables `HTTP_PROXY`, `HTTPS_PROXY`, `http_proxy` and `https_proxy` might be set. This confuses the jupyterhub-singleuser servers: When connecting to the Hub for authorization they connect via the proxy instead of directly connecting to the Hub on localhost. The proxy might deny the request (403 GET). This results in the singleuser server thinking it has a wrong auth token. To circumvent this you should add `<hub_url>,<hub_ip>,localhost,127.0.0.1` to the environment variables `NO_PROXY` and `no_proxy`.
When your whole JupyterHub sits behind an organization proxy (_not_ a reverse proxy like NGINX as part of your setup and _not_ the configurable-http-proxy) the environment variables `HTTP_PROXY`, `HTTPS_PROXY`, `http_proxy`, and `https_proxy` might be set. This confuses the Jupyterhub single-user servers: When connecting to the Hub for authorization they connect via the proxy instead of directly connecting to the Hub on localhost. The proxy might deny the request (403 GET). This results in the single-user server thinking it has the wrong auth token. To circumvent this you should add `<hub_url>,<hub_ip>,localhost,127.0.0.1` to the environment variables `NO_PROXY` and `no_proxy`.
### Launching Jupyter Notebooks to run as an externally managed JupyterHub service with the `jupyterhub-singleuser` command returns a `JUPYTERHUB_API_TOKEN` error
[JupyterHub services](https://jupyterhub.readthedocs.io/en/stable/reference/services.html) allow processes to interact with JupyterHub's REST API. Example use-cases include:
- **Secure Testing**: provide a canonical Jupyter Notebook for testing production data to reduce the number of entry points into production systems.
- **Grading Assignments**: provide access to shared Jupyter Notebooks that may be used for management tasks such grading assignments.
- **Grading Assignments**: provide access to shared Jupyter Notebooks that may be used for management tasks such as grading assignments.
- **Private Dashboards**: share dashboards with certain group members.
If possible, try to run the Jupyter Notebook as an externally managed service with one of the provided [jupyter/docker-stacks](https://github.com/jupyter/docker-stacks).
@@ -222,7 +182,7 @@ If you launch a Jupyter Notebook with the `jupyterhub-singleuser` command direct
Did you launch it manually?
```
If you plan on testing `jupyterhub-singleuser` independently from JupyterHub, then you can set the api token environment variable. For example, if were to run the single-user Jupyter Notebook on the host, then:
If you plan on testing `jupyterhub-singleuser` independently from JupyterHub, then you can set the API token environment variable. For example, if you were to run the single-user Jupyter Notebook on the host, then:
export JUPYTERHUB_API_TOKEN=my_secret_token
jupyterhub-singleuser
@@ -256,7 +216,7 @@ You would then set in your `jupyterhub_config.py` file the `ssl_key` and
#### Example
Your certificate provider gives you the following files: `example_host.crt`,
`Entrust_L1Kroot.txt` and `Entrust_Root.txt`.
`Entrust_L1Kroot.txt`, and `Entrust_Root.txt`.
Concatenate the files appending the chain cert and root cert to your host cert:
@@ -289,7 +249,7 @@ with npmbox:
python3 -m pip wheel jupyterhub
npmbox configurable-http-proxy
### I want access to the whole filesystem, but still default users to their home directory
### I want access to the whole filesystem and still default users to their home directory
Setting the following in `jupyterhub_config.py` will configure access to
the entire filesystem and set the default to the user's home directory.
@@ -308,7 +268,7 @@ similar to this one:
provides additional information. The [pySpark configuration documentation](https://spark.apache.org/docs/0.9.0/configuration.html)
is also helpful for programmatic configuration examples.
### How do I use JupyterLab's prerelease version with JupyterHub?
### How do I use JupyterLab's pre-release version with JupyterHub?
While JupyterLab is still under active development, we have had users
ask about how to try out JupyterLab with JupyterHub.
@@ -321,7 +281,7 @@ For instance:
python3 -m pip install jupyterlab
jupyter serverextension enable --py jupyterlab --sys-prefix
The important thing is that jupyterlab is installed and enabled in the
The important thing is that JupyterLab is installed and enabled in the
single-user notebook server environment. For system users, this means
system-wide, as indicated above. For Docker containers, it means inside
the single-user docker image, etc.
@@ -334,14 +294,14 @@ notebook servers to default to JupyterLab:
### How do I set up JupyterHub for a workshop (when users are not known ahead of time)?
1. Set up JupyterHub using OAuthenticator for GitHub authentication
2. Configure admin list to have workshop leaders be listed with administrator privileges.
2. Configure the admin list to have workshop leaders listed with administrator privileges.
Users will need a GitHub account to login and be authenticated by the Hub.
Users will need a GitHub account to log in and be authenticated by the Hub.
### How do I set up rotating daily logs?
You can do this with [logrotate](https://linux.die.net/man/8/logrotate),
or pipe to `logger` to use syslog instead of directly to a file.
or pipe to `logger` to use Syslog instead of directly to a file.
For example, with this logrotate config file:
@@ -362,6 +322,52 @@ Or use syslog:
jupyterhub | logger -t jupyterhub
### Toree integration with HDFS rack awareness script
The Apache Toree kernel will have an issue when running with JupyterHub if the standard HDFS
rack awareness script is used. This will materialize in the logs as a repeated WARN:
```bash
16/11/29 16:24:20 WARN ScriptBasedMapping: Exception running /etc/hadoop/conf/topology_script.py some.ip.address
ExitCodeException exitCode=1: File "/etc/hadoop/conf/topology_script.py", line 63
print rack
^
SyntaxError: Missing parentheses in call to 'print'
at `org.apache.hadoop.util.Shell.runCommand(Shell.java:576)`
```
In order to resolve this issue, there are two potential options.
1. Update HDFS core-site.xml, so the parameter "net.topology.script.file.name" points to a custom
script (e.g. /etc/hadoop/conf/custom_topology_script.py). Copy the original script and change the first line point
to a python two installation (e.g. /usr/bin/python).
2. In spark-env.sh add a Python 2 installation to your path (e.g. export PATH=/opt/anaconda2/bin:$PATH).
### Where do I find Docker images and Dockerfiles related to JupyterHub?
Docker images can be found at the [JupyterHub organization on DockerHub](https://hub.docker.com/u/jupyterhub/).
The Docker image [jupyterhub/singleuser](https://hub.docker.com/r/jupyterhub/singleuser/)
provides an example single-user notebook server for use with DockerSpawner.
Additional single-user notebook server images can be found at the [Jupyter
organization on DockerHub](https://hub.docker.com/r/jupyter/) and information
about each image at the [jupyter/docker-stacks repo](https://github.com/jupyter/docker-stacks).
### How can I view the logs for JupyterHub or the user's Notebook servers when using the DockerSpawner?
Use `docker logs <container>` where `<container>` is the container name defined within `docker-compose.yml`. For example, to view the logs of the JupyterHub container use:
docker logs hub
By default, the user's notebook server is named `jupyter-<username>` where `username` is the user's username within JupyterHub's db. So if you wanted to see the logs for user `foo` you would use:
docker logs jupyter-foo
You can also tail logs to view them in real-time using the `-f` option:
docker logs -f hub
## Troubleshooting commands
The following commands provide additional detail about installed packages,
@@ -385,35 +391,3 @@ jupyter kernelspec list
```bash
jupyterhub --debug
```
### Toree integration with HDFS rack awareness script
The Apache Toree kernel will an issue, when running with JupyterHub, if the standard HDFS
rack awareness script is used. This will materialize in the logs as a repeated WARN:
```bash
16/11/29 16:24:20 WARN ScriptBasedMapping: Exception running /etc/hadoop/conf/topology_script.py some.ip.address
ExitCodeException exitCode=1: File "/etc/hadoop/conf/topology_script.py", line 63
print rack
^
SyntaxError: Missing parentheses in call to 'print'
at `org.apache.hadoop.util.Shell.runCommand(Shell.java:576)`
```
In order to resolve this issue, there are two potential options.
1. Update HDFS core-site.xml, so the parameter "net.topology.script.file.name" points to a custom
script (e.g. /etc/hadoop/conf/custom_topology_script.py). Copy the original script and change the first line point
to a python two installation (e.g. /usr/bin/python).
2. In spark-env.sh add a Python 2 installation to your path (e.g. export PATH=/opt/anaconda2/bin:$PATH).
### Where do I find Docker images and Dockerfiles related to JupyterHub?
Docker images can be found at the [JupyterHub organization on DockerHub](https://hub.docker.com/u/jupyterhub/).
The Docker image [jupyterhub/singleuser](https://hub.docker.com/r/jupyterhub/singleuser/)
provides an example single user notebook server for use with DockerSpawner.
Additional single user notebook server images can be found at the [Jupyter
organization on DockerHub](https://hub.docker.com/r/jupyter/) and information
about each image at the [jupyter/docker-stacks repo](https://github.com/jupyter/docker-stacks).