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docs/source/_static/custom.css
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div#helm-chart-schema h2,
div#helm-chart-schema h3,
div#helm-chart-schema h4,
div#helm-chart-schema h5,
div#helm-chart-schema h6 {
font-family: courier new;
}
h3, h3 ~ * {
margin-left: 3% !important;
}
h4, h4 ~ * {
margin-left: 6% !important;
}
h5, h5 ~ * {
margin-left: 9% !important;
}
h6, h6 ~ * {
margin-left: 12% !important;
}
h7, h7 ~ * {
margin-left: 15% !important;
}
img.logo {
width:100%
}
.right-next {
float: right;
max-width: 45%;
overflow: auto;
text-overflow: ellipsis;
white-space: nowrap;
}
.right-next::after{
content: ' »';
}
.left-prev {
float: left;
max-width: 45%;
overflow: auto;
text-overflow: ellipsis;
white-space: nowrap;
}
.left-prev::before{
content: '« ';
}
.prev-next-bottom {
margin-top: 3em;
}
.prev-next-top {
margin-bottom: 1em;
}
/* Sidebar TOC and headers */
div.sphinxsidebarwrapper div {
margin-bottom: .8em;
}
div.sphinxsidebar h3 {
font-size: 1.3em;
padding-top: 0px;
font-weight: 800;
margin-left: 0px !important;
}
div.sphinxsidebar p.caption {
font-size: 1.2em;
margin-bottom: 0px;
margin-left: 0px !important;
font-weight: 900;
color: #767676;
}
div.sphinxsidebar ul {
font-size: .8em;
margin-top: 0px;
padding-left: 3%;
margin-left: 0px !important;
}
div.relations ul {
font-size: 1em;
margin-left: 0px !important;
}
div#searchbox form {
margin-left: 0px !important;
}
/* body elements */
.toctree-wrapper span.caption-text {
color: #767676;
font-style: italic;
font-weight: 300;
}
/* Added to avoid logo being too squeezed */
.navbar-brand {
height: 4rem !important;
}
/* hide redundant funky-formatted swagger-ui version */
.swagger-ui .info .title small {
display: none !important;
}

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{# Custom template for navigation.html
alabaster theme does not provide blocks for titles to
be overridden so this custom theme handles title and
toctree for sidebar
#}
<h3>{{ _('Table of Contents') }}</h3>
{{ toctree(includehidden=theme_sidebar_includehidden, collapse=theme_sidebar_collapse) }}
{% if theme_extra_nav_links %}
<hr />
<ul>
{% for text, uri in theme_extra_nav_links.items() %}
<li class="toctree-l1"><a href="{{ uri }}">{{ text }}</a></li>
{% endfor %}
</ul>
{% endif %}

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{# Custom template for relations.html
alabaster theme does not provide previous/next page by default
#}
<div class="relations">
<h3>Navigation</h3>
<ul>
<li><a href="{{ pathto(master_doc) }}">Documentation Home</a><ul>
{%- if prev %}
<li><a href="{{ prev.link|e }}" title="Previous">Previous topic</a></li>
{%- endif %}
{%- if next %}
<li><a href="{{ next.link|e }}" title="Next">Next topic</a></li>
{%- endif %}
</ul>
</ul>
</div>

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docs/source/admin/log-messages.md Normal file
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# Interpreting common log messages
When debugging errors and outages, looking at the logs emitted by
JupyterHub is very helpful. This document intends to describe some common
log messages, what they mean and what are the most common causes that generated them, as well as some possible ways to fix them.
## Failing suspected API request to not-running server
### Example
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
```
### Cause
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.
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
that the server is not running anymore. This is used by JupyterLab to
inform the user that the server is not running anymore, and provide an option
to restart it.
Most commonly, you'll see this in reference to the `/api/metrics/v1`
URL, used by [jupyter-resource-usage](https://github.com/jupyter-server/jupyter-resource-usage).
### Actions you can take
This log message is benign, and there is usually no action for you to take.
## JupyterHub Singleuser Version mismatch
### Example
```
jupyterhub version 1.5.0 != jupyterhub-singleuser version 1.3.0. This could cause failure to authenticate and result in redirect loops!
```
### Cause
JupyterHub requires the `jupyterhub` python package installed inside the image or
environment, the user server starts in. This message indicates that the version of
the `jupyterhub` package installed inside the user image or environment is not
the same as the JupyterHub server's version itself. This is not necessarily always a
problem - some version drift is mostly acceptable, and the only two known cases of
breakage are across the 0.7 and 2.0 version releases. In those cases, issues pop
up immediately after upgrading your version of JupyterHub, so **always check the JupyterHub
changelog before upgrading!**. The primary problems this _could_ cause are:
1. Infinite redirect loops after the user server starts
2. Missing expected environment variables in the user server once it starts
3. Failure for the started user server to authenticate with the JupyterHub server -
note that this is _not_ the same as _user authentication_ failing!
However, for the most part, unless you are seeing these specific issues, the log
message should be counted as a warning to get the `jupyterhub` package versions
aligned, rather than as an indicator of an existing problem.
### Actions you can take
Upgrade the version of the `jupyterhub` package in your user environment or image
so that it matches the version of JupyterHub running your JupyterHub server! If you
are using the [zero-to-jupyterhub](https://z2jh.jupyter.org) helm chart, you can find the appropriate
version of the `jupyterhub` package to install in your user image [here](https://jupyterhub.github.io/helm-chart/)

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docs/source/admin/upgrading.rst
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.. _admin/upgrading:
====================
Upgrading JupyterHub
====================
@@ -7,35 +5,36 @@ Upgrading JupyterHub
JupyterHub offers easy upgrade pathways between minor versions. This
document describes how to do these upgrades.
If you are using :ref:`a JupyterHub distribution <index/distributions>`, you
If you use :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 applicable if you have set up your own JupyterHub without using a
distribution.
It is long because is pretty detailed! Most likely, upgrading
This documentation is lengthy because it is quite 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.
Read the Changelog
==================
The `changelog <../changelog.html>`_ contains information on what has
changed with the new JupyterHub release, and any deprecation warnings.
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 coming changes. There
might be new releases of authenticators & spawners you are using, so
might be new releases of the authenticators & spawners you use, so
read the changelogs for those too!
Notify your users
=================
If you are using the default configuration where ``configurable-http-proxy``
If you use the default configuration where ``configurable-http-proxy``
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 use a different proxy or run ``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.
servers they had already launched, but will not be able to launch new servers or sign in.
Backup database & config
@@ -43,37 +42,37 @@ 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 use SQLite (the default), you should backup 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
have set up JupyterHub to run. Most likely, it is using a process
Shut down the JupyterHub process. This would vary depending on how you
have set up JupyterHub to run. It is most likely 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.
@@ -94,10 +93,8 @@ 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
@@ -111,7 +108,7 @@ 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
@@ -120,11 +117,11 @@ 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?
-------------------------------------
@@ -139,10 +136,10 @@ 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
================
@@ -150,7 +147,7 @@ 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

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.. _api-index:
##################
The JupyterHub API
##################
##############
JupyterHub API
##############
:Release: |release|
:Date: |today|
@@ -17,11 +17,6 @@ information on:
- making an API request programmatically using the requests library
- learning more about JupyterHub's API
The same JupyterHub API spec, as found here, is available in an interactive form
`here (on swagger's petstore) <http://petstore.swagger.io/?url=https://raw.githubusercontent.com/jupyterhub/jupyterhub/master/docs/rest-api.yml#!/default>`__.
The `OpenAPI Initiative`_ (fka Swagger™) is a project used to describe
and document RESTful APIs.
JupyterHub API Reference:
.. toctree::

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docs/source/conf.py
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# -*- coding: utf-8 -*-
# Configuration file for Sphinx to build our documentation to HTML.
#
# Configuration reference: https://www.sphinx-doc.org/en/master/usage/configuration.html
#
import contextlib
import datetime
import io
import os
import shlex
import sys
import subprocess
# Set paths
sys.path.insert(0, os.path.abspath('.'))
# -- General configuration ------------------------------------------------
# Minimal Sphinx version
needs_sphinx = '1.4'
# Sphinx extension modules
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.intersphinx',
'sphinx.ext.napoleon',
'autodoc_traits',
'sphinx_copybutton',
]
templates_path = ['_templates']
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'JupyterHub'
copyright = u'2016, Project Jupyter team'
author = u'Project Jupyter team'
# Autopopulate version
from os.path import dirname
docs = dirname(dirname(__file__))
root = dirname(docs)
sys.path.insert(0, root)
sys.path.insert(0, os.path.join(docs, 'sphinxext'))
from docutils import nodes
from sphinx.directives.other import SphinxDirective
import jupyterhub
from jupyterhub.app import JupyterHub
# The short X.Y version.
version = '%i.%i' % jupyterhub.version_info[:2]
# The full version, including alpha/beta/rc tags.
# -- Project information -----------------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
#
project = "JupyterHub"
author = "Project Jupyter Contributors"
copyright = f"{datetime.date.today().year}, {author}"
version = "%i.%i" % jupyterhub.version_info[:2]
release = jupyterhub.__version__
language = None
exclude_patterns = []
pygments_style = 'sphinx'
todo_include_todos = False
# Set the default role so we can use `foo` instead of ``foo``
default_role = 'literal'
# -- General Sphinx configuration --------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
#
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.intersphinx",
"sphinx.ext.napoleon",
"autodoc_traits",
"sphinx_copybutton",
"sphinx-jsonschema",
"sphinxext.opengraph",
"sphinxext.rediraffe",
"myst_parser",
]
root_doc = "index"
source_suffix = [".md", ".rst"]
# default_role let's use use `foo` instead of ``foo`` in rST
default_role = "literal"
# -- Source -------------------------------------------------------------
import recommonmark
from recommonmark.transform import AutoStructify
# -- MyST configuration ------------------------------------------------------
# ref: https://myst-parser.readthedocs.io/en/latest/configuration.html
#
myst_heading_anchors = 2
myst_enable_extensions = [
"colon_fence",
"deflist",
]
# -- Custom directives to generate documentation -----------------------------
# ref: https://myst-parser.readthedocs.io/en/latest/syntax/roles-and-directives.html
#
# We define custom directives to help us generate documentation using Python on
# demand when referenced from our documentation files.
#
# Create a temp instance of JupyterHub for use by two separate directive classes
# to get the output from using the "--generate-config" and "--help-all" CLI
# flags respectively.
#
jupyterhub_app = JupyterHub()
class ConfigDirective(SphinxDirective):
"""Generate the configuration file output for use in the documentation."""
has_content = False
required_arguments = 0
optional_arguments = 0
final_argument_whitespace = False
option_spec = {}
def run(self):
# The generated configuration file for this version
generated_config = jupyterhub_app.generate_config_file()
# post-process output
home_dir = os.environ["HOME"]
generated_config = generated_config.replace(home_dir, "$HOME", 1)
par = nodes.literal_block(text=generated_config)
return [par]
class HelpAllDirective(SphinxDirective):
"""Print the output of jupyterhub help --all for use in the documentation."""
has_content = False
required_arguments = 0
optional_arguments = 0
final_argument_whitespace = False
option_spec = {}
def run(self):
# The output of the help command for this version
buffer = io.StringIO()
with contextlib.redirect_stdout(buffer):
jupyterhub_app.print_help("--help-all")
all_help = buffer.getvalue()
# post-process output
home_dir = os.environ["HOME"]
all_help = all_help.replace(home_dir, "$HOME", 1)
par = nodes.literal_block(text=all_help)
return [par]
def setup(app):
app.add_config_value('recommonmark_config', {'enable_eval_rst': True}, True)
app.add_stylesheet('custom.css')
app.add_transform(AutoStructify)
app.add_css_file("custom.css")
app.add_directive("jupyterhub-generate-config", ConfigDirective)
app.add_directive("jupyterhub-help-all", HelpAllDirective)
source_parsers = {'.md': 'recommonmark.parser.CommonMarkParser'}
source_suffix = ['.rst', '.md']
# source_encoding = 'utf-8-sig'
# -- Options for HTML output ----------------------------------------------
# The theme to use for HTML and HTML Help pages.
import alabaster_jupyterhub
html_theme = 'alabaster_jupyterhub'
html_theme_path = [alabaster_jupyterhub.get_html_theme_path()]
html_logo = '_static/images/logo/logo.png'
html_favicon = '_static/images/logo/favicon.ico'
# Paths that contain custom static files (such as style sheets)
html_static_path = ['_static']
html_theme_options = {
'show_related': True,
'description': 'Documentation for JupyterHub',
'github_user': 'jupyterhub',
'github_repo': 'jupyterhub',
'github_banner': False,
'github_button': True,
'github_type': 'star',
'show_powered_by': False,
'extra_nav_links': {
'GitHub Repo': 'http://github.com/jupyterhub/jupyterhub',
'Issue Tracker': 'http://github.com/jupyterhub/jupyterhub/issues',
},
}
html_sidebars = {
'**': [
'about.html',
'searchbox.html',
'navigation.html',
'relations.html',
'sourcelink.html',
]
}
htmlhelp_basename = 'JupyterHubdoc'
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# 'papersize': 'letterpaper',
# 'pointsize': '10pt',
# 'preamble': '',
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(
master_doc,
'JupyterHub.tex',
u'JupyterHub Documentation',
u'Project Jupyter team',
'manual',
)
]
# latex_logo = None
# latex_use_parts = False
# latex_show_pagerefs = False
# latex_show_urls = False
# latex_appendices = []
# latex_domain_indices = True
# -- Read The Docs -----------------------------------------------------------
#
# Since RTD runs sphinx-build directly without running "make html", we run the
# pre-requisite steps for "make html" from here if needed.
#
if os.environ.get("READTHEDOCS"):
docs = os.path.dirname(os.path.dirname(__file__))
subprocess.check_call(["make", "metrics", "scopes"], cwd=docs)
# -- manual page output -------------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [(master_doc, 'jupyterhub', u'JupyterHub Documentation', [author], 1)]
# man_show_urls = False
# -- Texinfo output -----------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(
master_doc,
'JupyterHub',
u'JupyterHub Documentation',
author,
'JupyterHub',
'One line description of project.',
'Miscellaneous',
)
]
# texinfo_appendices = []
# texinfo_domain_indices = True
# texinfo_show_urls = 'footnote'
# texinfo_no_detailmenu = False
# -- Epub output --------------------------------------------------------
# Bibliographic Dublin Core info.
epub_title = project
epub_author = author
epub_publisher = author
epub_copyright = copyright
# A list of files that should not be packed into the epub file.
epub_exclude_files = ['search.html']
# -- Intersphinx ----------------------------------------------------------
intersphinx_mapping = {'https://docs.python.org/3/': None}
# -- Read The Docs --------------------------------------------------------
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
if on_rtd:
# readthedocs.org uses their theme by default, so no need to specify it
# build rest-api, since RTD doesn't run make
from subprocess import check_call as sh
sh(['make', 'rest-api'], cwd=docs)
# -- Spell checking -------------------------------------------------------
# -- Spell checking ----------------------------------------------------------
# ref: https://sphinxcontrib-spelling.readthedocs.io/en/latest/customize.html#configuration-options
#
# The "sphinxcontrib.spelling" extension is optionally enabled if its available.
#
try:
import sphinxcontrib.spelling
import sphinxcontrib.spelling # noqa
except ImportError:
pass
else:
extensions.append("sphinxcontrib.spelling")
spelling_word_list_filename = "spelling_wordlist.txt"
spelling_word_list_filename = 'spelling_wordlist.txt'
# -- Options for HTML output -------------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
#
html_logo = "_static/images/logo/logo.png"
html_favicon = "_static/images/logo/favicon.ico"
html_static_path = ["_static"]
html_theme = "pydata_sphinx_theme"
html_theme_options = {
"icon_links": [
{
"name": "GitHub",
"url": "https://github.com/jupyterhub/jupyterhub",
"icon": "fab fa-github-square",
},
{
"name": "Discourse",
"url": "https://discourse.jupyter.org/c/jupyterhub/10",
"icon": "fab fa-discourse",
},
],
"use_edit_page_button": True,
"navbar_align": "left",
}
html_context = {
"github_user": "jupyterhub",
"github_repo": "jupyterhub",
"github_version": "main",
"doc_path": "docs/source",
}
# -- Options for linkcheck builder -------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-the-linkcheck-builder
#
linkcheck_ignore = [
r"(.*)github\.com(.*)#", # javascript based anchors
r"(.*)/#%21(.*)/(.*)", # /#!forum/jupyter - encoded anchor edge case
r"https://github.com/[^/]*$", # too many github usernames / searches in changelog
"https://github.com/jupyterhub/jupyterhub/pull/", # too many PRs in changelog
"https://github.com/jupyterhub/jupyterhub/compare/", # too many comparisons in changelog
]
linkcheck_anchors_ignore = [
"/#!",
"/#%21",
]
# -- Intersphinx -------------------------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/extensions/intersphinx.html#configuration
#
intersphinx_mapping = {
"python": ("https://docs.python.org/3/", None),
"tornado": ("https://www.tornadoweb.org/en/stable/", None),
}
# -- Options for the opengraph extension -------------------------------------
# ref: https://github.com/wpilibsuite/sphinxext-opengraph#options
#
# ogp_site_url is set automatically by RTD
ogp_image = "_static/logo.png"
ogp_use_first_image = True
# -- Options for the rediraffe extension -------------------------------------
# ref: https://github.com/wpilibsuite/sphinxext-rediraffe#readme
#
# This extensions help us relocated content without breaking links. If a
# document is moved internally, a redirect like should be configured below to
# help us not break links.
#
rediraffe_branch = "main"
rediraffe_redirects = {
# "old-file": "new-folder/new-file-name",
}

27
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View File

@@ -0,0 +1,27 @@
# 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.
You can ask questions here if you are a first-time contributor to the JupyterHub project.
Everyone in the Jupyter community is welcome to bring ideas and questions there.
We recommend that you first use our Discourse as all past and current discussions on it are archived and searchable. Thus, all 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](https://docs.github.com/en/issues/tracking-your-work-with-issues/about-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!
```

30
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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>`_.

18
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View File

@@ -5,7 +5,7 @@ Contributing Documentation
==========================
Documentation is often more important than code. This page helps
you get set up on how to contribute documentation to JupyterHub.
you get set up on how to contribute to JupyterHub's documentation.
Building documentation locally
==============================
@@ -13,12 +13,12 @@ Building documentation locally
We use `sphinx <http://sphinx-doc.org>`_ to build our documentation. It takes
our documentation source files (written in `markdown
<https://daringfireball.net/projects/markdown/>`_ or `reStructuredText
<http://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html>`_ &
<https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html>`_ &
stored under the ``docs/source`` directory) and converts it into various
formats for people to read. To make sure the documentation you write or
change renders correctly, it is good practice to test it locally.
#. Make sure you have successfuly completed :ref:`contributing/setup`.
#. Make sure you have successfully completed :ref:`contributing/setup`.
#. Install the packages required to build the docs.
@@ -39,13 +39,17 @@ change renders correctly, it is good practice to test it locally.
along with the filename / line number in which they occurred. Fix them,
and re-run the ``make html`` command to re-render the documentation.
#. View the rendered documentation by opening ``build/html/index.html`` in
a web browser.
#. View the rendered documentation by opening ``build/html/index.html`` in
a web browser.
.. tip::
On macOS, you can open a file from the terminal with ``open <path-to-file>``.
On Linux, you can do the same with ``xdg-open <path-to-file>``.
**On Windows**, you can open a file from the terminal with ``start <path-to-file>``.
**On macOS**, you can do the same with ``open <path-to-file>``.
**On Linux**, you can do the same with ``xdg-open <path-to-file>``.
After opening index.html in your browser you can just refresh the page whenever
you rebuild the docs via ``make html``

21
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@@ -0,0 +1,21 @@
============
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,
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>`_
(`reporting guidelines <https://github.com/jupyter/governance/blob/HEAD/conduct/reporting_online.md>`_)
helps keep our community welcoming to as many people as possible.
.. toctree::
:maxdepth: 2
community
setup
docs
tests
roadmap
security

71
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@@ -4,10 +4,10 @@ This roadmap collects "next steps" for JupyterHub. It is about creating a
shared understanding of the project's vision and direction amongst
the community of users, contributors, and maintainers.
The goal is to communicate priorities and upcoming release plans.
It is not a aimed at limiting contributions to what is listed here.
It is not aimed at limiting contributions to what is listed here.
## Using the roadmap
### Sharing Feedback on the Roadmap
All of the community is encouraged to provide feedback as well as share new
@@ -22,17 +22,17 @@ maintainers will help identify what a good next step is for the issue.
When submitting an issue, think about what "next step" category best describes
your issue:
* **now**, concrete/actionable step that is ready for someone to start work on.
These might be items that have a link to an issue or more abstract like
"decrease typos and dead links in the documentation"
* **soon**, less concrete/actionable step that is going to happen soon,
discussions around the topic are coming close to an end at which point it can
move into the "now" category
* **later**, abstract ideas or tasks, need a lot of discussion or
experimentation to shape the idea so that it can be executed. Can also
contain concrete/actionable steps that have been postponed on purpose
(these are steps that could be in "now" but the decision was taken to work on
them later)
- **now**, concrete/actionable step that is ready for someone to start work on.
These might be items that have a link to an issue or more abstract like
"decrease typos and dead links in the documentation"
- **soon**, less concrete/actionable step that is going to happen soon,
discussions around the topic are coming close to an end at which point it can
move into the "now" category
- **later**, abstract ideas or tasks, need a lot of discussion or
experimentation to shape the idea so that it can be executed. Can also
contain concrete/actionable steps that have been postponed on purpose
(these are steps that could be in "now" but the decision was taken to work on
them later)
### Reviewing and Updating the Roadmap
@@ -47,8 +47,8 @@ For those please create a
The roadmap should give the reader an idea of what is happening next, what needs
input and discussion before it can happen and what has been postponed.
## The roadmap proper
### Project vision
JupyterHub is a dependable tool used by humans that reduces the complexity of
@@ -58,20 +58,19 @@ creating the environment in which a piece of software can be executed.
These "Now" items are considered active areas of focus for the project:
* HubShare - a sharing service for use with JupyterHub.
* Users should be able to:
- Push a project to other users.
- Get a checkout of a project from other users.
- Push updates to a published project.
- Pull updates from a published project.
- Manage conflicts/merges by simply picking a version (our/theirs)
- Get a checkout of a project from the internet. These steps are completely different from saving notebooks/files.
- Have directories that are managed by git completely separately from our stuff.
- Look at pushed content that they have access to without an explicit pull.
- Define and manage teams of users.
- Adding/removing a user to/from a team gives/removes them access to all projects that team has access to.
- Build other services, such as static HTML publishing and dashboarding on top of these things.
- HubShare - a sharing service for use with JupyterHub.
- Users should be able to:
- Push a project to other users.
- Get a checkout of a project from other users.
- Push updates to a published project.
- Pull updates from a published project.
- Manage conflicts/merges by simply picking a version (our/theirs)
- Get a checkout of a project from the internet. These steps are completely different from saving notebooks/files.
- Have directories that are managed by git completely separately from our stuff.
- Look at pushed content that they have access to without an explicit pull.
- Define and manage teams of users.
- Adding/removing a user to/from a team gives/removes them access to all projects that team has access to.
- Build other services, such as static HTML publishing and dashboarding on top of these things.
### Soon
@@ -79,12 +78,10 @@ These "Soon" items are under discussion. Once an item reaches the point of an
actionable plan, the item will be moved to the "Now" section. Typically,
these will be moved at a future review of the roadmap.
* resource monitoring and management:
- (prometheus?) API for resource monitoring
- tracking activity on single-user servers instead of the proxy
- notes and activity tracking per API token
- UI for managing named servers
- resource monitoring and management:
- (prometheus?) API for resource monitoring
- tracking activity on single-user servers instead of the proxy
- notes and activity tracking per API token
### Later
@@ -93,6 +90,6 @@ time there is no active plan for an item. The project would like to find the
resources and time to discuss these ideas.
- real-time collaboration
- Enter into real-time collaboration mode for a project that starts a shared execution context.
- Once the single-user notebook package supports realtime collaboration,
implement sharing mechanism integrated into the Hub.
- Enter into real-time collaboration mode for a project that starts a shared execution context.
- Once the single-user notebook package supports realtime collaboration,
implement sharing mechanism integrated into the Hub.

71
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@@ -7,28 +7,27 @@ Setting up a development install
System requirements
===================
JupyterHub can only run on MacOS or Linux operating systems. If you are
using Windows, we recommend using `VirtualBox <https://virtualbox.org>`_
JupyterHub can only run on macOS or Linux operating systems. If you are
using Windows, we recommend using `VirtualBox <https://virtualbox.org>`_
or a similar system to run `Ubuntu Linux <https://ubuntu.com>`_ for
development.
Install Python
--------------
JupyterHub is written in the `Python <https://python.org>`_ programming language, and
requires you have at least version 3.5 installed locally. If you haven’t
JupyterHub is written in the `Python <https://python.org>`_ programming language and
requires you have at least version 3.6 installed locally. If you haven’t
installed Python before, the recommended way to install it is to use
`miniconda <https://conda.io/miniconda.html>`_. Remember to get the ‘Python 3’ version,
`Miniconda <https://conda.io/miniconda.html>`_. Remember to get the ‘Python 3’ version,
and **not** the ‘Python 2’ version!
Install nodejs
--------------
``configurable-http-proxy``, the default proxy implementation for
JupyterHub, is written in Javascript to run on `NodeJS
<https://nodejs.org/en/>`_. If you have not installed nodejs before, we
recommend installing it in the ``miniconda`` environment you set up for
Python. You can do so with ``conda install nodejs``.
`NodeJS 12+ <https://nodejs.org/en/>`_ is required for building some JavaScript components.
``configurable-http-proxy``, the default proxy implementation for JupyterHub, is written in Javascript.
If you have not installed NodeJS before, we recommend installing it in the ``miniconda`` environment you set up for Python.
You can do so with ``conda install nodejs``.
Many in the Jupyter community use [``nvm``](https://github.com/nvm-sh/nvm) to
managing node dependencies.
@@ -36,7 +35,7 @@ managing node dependencies.
Install git
-----------
JupyterHub uses `git <https://git-scm.com>`_ & `GitHub <https://github.com>`_
JupyterHub uses `Git <https://git-scm.com>`_ & `GitHub <https://github.com>`_
for development & collaboration. You need to `install git
<https://git-scm.com/book/en/v2/Getting-Started-Installing-Git>`_ to work on
JupyterHub. We also recommend getting a free account on GitHub.com.
@@ -44,11 +43,15 @@ JupyterHub. We also recommend getting a free account on GitHub.com.
Setting up a development install
================================
When developing JupyterHub, you need to make changes to the code & see
their effects quickly. You need to do a developer install to make that
happen.
When developing JupyterHub, you would need to make changes and be able to instantly view the results of the changes. To achieve that, a developer install is required.
1. Clone the `JupyterHub git repository <https://github.com/jupyterhub/jupyterhub>`_
.. note:: This guide does not attempt to dictate *how* development
environments should be isolated since that is a personal preference and can
be achieved in many ways, for example, `tox`, `conda`, `docker`, etc. See this
`forum thread <https://discourse.jupyter.org/t/thoughts-on-using-tox/3497>`_ for
a more detailed discussion.
1. Clone the `JupyterHub git repository <https://github.com/jupyterhub/jupyterhub>`_
to your computer.
.. code:: bash
@@ -63,7 +66,7 @@ happen.
python -V
This should return a version number greater than or equal to 3.5.
This should return a version number greater than or equal to 3.6.
.. code:: bash
@@ -71,12 +74,11 @@ happen.
This should return a version number greater than or equal to 5.0.
3. Install ``configurable-http-proxy``. This is required to run
JupyterHub.
3. Install ``configurable-http-proxy`` (required to run and test the default JupyterHub configuration) and ``yarn`` (required to build some components):
.. code:: bash
npm install -g configurable-http-proxy
npm install -g configurable-http-proxy yarn
If you get an error that says ``Error: EACCES: permission denied``, you might need to prefix the command with ``sudo``.
``sudo`` may be required to perform a system-wide install.
@@ -84,25 +86,31 @@ happen.
.. code:: bash
npm install configurable-http-proxy
npm install configurable-http-proxy yarn
export PATH=$PATH:$(pwd)/node_modules/.bin
The second line needs to be run every time you open a new terminal.
4. Install the python packages required for JupyterHub development.
If you are using conda you can instead run:
.. code:: bash
python3 -m pip install -r dev-requirements.txt
python3 -m pip install -r requirements.txt
conda install configurable-http-proxy yarn
5. Install the development version of JupyterHub. This lets you edit
JupyterHub code in a text editor & restart the JupyterHub process to
see your code changes immediately.
4. Install an editable version of JupyterHub and its requirements for
development and testing. This lets you edit JupyterHub code in a text editor
& restart the JupyterHub process to see your code changes immediately.
.. code:: bash
python3 -m pip install --editable .
python3 -m pip install --editable ".[test]"
5. Set up a database.
The default database engine is ``sqlite`` so if you are just trying
to get up and running quickly for local development that should be
available via `Python <https://docs.python.org/3.5/library/sqlite3.html>`__.
See :doc:`/reference/database` for details on other supported databases.
6. You are now ready to start JupyterHub!
@@ -123,7 +131,7 @@ To simplify testing of JupyterHub, it’s helpful to use
authenticator and SimpleLocalProcessSpawner instead of the default spawner.
There is a sample configuration file that does this in
``testing/jupyterhub_config.py``. To launch jupyterhub with this
``testing/jupyterhub_config.py``. To launch JupyterHub with this
configuration:
.. code:: bash
@@ -139,14 +147,14 @@ JupyterHub as.
DummyAuthenticator allows you to log in with any username & password,
while SimpleLocalProcessSpawner allows you to start servers without having to
create a unix user for each JupyterHub user. Together, these make it
create a Unix user for each JupyterHub user. Together, these make it
much easier to test JupyterHub.
Tip: If you are working on parts of JupyterHub that are common to all
authenticators & spawners, we recommend using both DummyAuthenticator &
SimpleLocalProcessSpawner. If you are working on just authenticator related
SimpleLocalProcessSpawner. If you are working on just authenticator-related
parts, use only SimpleLocalProcessSpawner. Similarly, if you are working on
just spawner related parts, use only DummyAuthenticator.
just spawner-related parts, use only DummyAuthenticator.
Troubleshooting
===============
@@ -176,3 +184,4 @@ development updates, with:
python3 setup.py js # fetch updated client-side js
python3 setup.py css # recompile CSS from LESS sources
python3 setup.py jsx # build React admin app

113
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@@ -1,49 +1,49 @@
.. _contributing/tests:
==================
Testing JupyterHub
==================
===================================
Testing JupyterHub and linting code
===================================
Unit testing helps to 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.
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
.. code-block:: bash
pytest --async-test-timeout 15 -v jupyterhub/tests
pytest -v jupyterhub/tests
This should display progress as it runs all the tests, printing
information about any test failures as they occur.
If you wish to confirm test coverage the run tests with the `--cov` flag:
The ``--async-test-timeout`` parameter is used by `pytest-tornado
<https://github.com/eugeniy/pytest-tornado#markers>`_ to set the
asynchronous test timeout to 15 seconds rather than the default 5,
since some of our tests take longer than 5s to execute.
.. code-block:: bash
pytest -v --cov=jupyterhub jupyterhub/tests
#. You can also run tests in just a specific file:
.. code-block:: bash
pytest --async-test-timeout 15 -v jupyterhub/tests/<test-file-name>
pytest -v jupyterhub/tests/<test-file-name>
#. To run a specific test only, you can do:
.. code-block:: bash
pytest --async-test-timeout 15 -v jupyterhub/tests/<test-file-name>::<test-name>
pytest -v jupyterhub/tests/<test-file-name>::<test-name>
This runs the test with function name ``<test-name>`` defined in
``<test-file-name>``. This is very useful when you are iteratively
@@ -56,6 +56,49 @@ Running the tests
pytest -v jupyterhub/tests/test_api.py::test_shutdown
For more information, refer to the `pytest usage documentation <https://pytest.readthedocs.io/en/latest/usage.html>`_.
Test organisation
=================
The tests live in ``jupyterhub/tests`` and are organized roughly into:
#. ``test_api.py`` tests the REST API
#. ``test_pages.py`` tests loading the HTML pages
and other collections of tests for different components.
When writing a new test, there should usually be a test of
similar functionality already written and related tests should
be added nearby.
The fixtures live in ``jupyterhub/tests/conftest.py``. There are
fixtures that can be used for JupyterHub components, such as:
- ``app``: an instance of JupyterHub with mocked parts
- ``auth_state_enabled``: enables persisting auth_state (like authentication tokens)
- ``db``: a sqlite in-memory DB session
- ``io_loop```: a Tornado event loop
- ``event_loop``: a new asyncio event loop
- ``user``: creates a new temporary user
- ``admin_user``: creates a new temporary admin user
- single user servers
- ``cleanup_after``: allows cleanup of single user servers between tests
- mocked service
- ``MockServiceSpawner``: a spawner that mocks services for testing with a short poll interval
- ``mockservice```: mocked service with no external service url
- ``mockservice_url``: mocked service with a url to test external services
And fixtures to add functionality or spawning behavior:
- ``admin_access``: grants admin access
- ``no_patience```: sets slow-spawning timeouts to zero
- ``slow_spawn``: enables the SlowSpawner (a spawner that takes a few seconds to start)
- ``never_spawn``: enables the NeverSpawner (a spawner that will never start)
- ``bad_spawn``: enables the BadSpawner (a spawner that fails immediately)
- ``slow_bad_spawn``: enables the SlowBadSpawner (a spawner that fails after a short delay)
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
=============================
@@ -63,16 +106,34 @@ Troubleshooting Test Failures
All the tests are failing
-------------------------
Make sure you have completed all the steps in :ref:`contributing/setup` sucessfully, 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.
Tests are timing out
--------------------
The ``--async-test-timeout`` parameter to ``pytest`` is used by
`pytest-tornado <https://github.com/eugeniy/pytest-tornado#markers>`_ to set
the asynchronous test timeout to a higher value than the default of 5s,
since some of our tests take longer than 5s to execute. If the tests
are still timing out, try increasing that value even more. You can
also set an environment variable ``ASYNC_TEST_TIMEOUT`` instead of
passing ``--async-test-timeout`` to each invocation of pytest.
Code formatting and linting
===========================
JupyterHub automatically enforces code formatting. This means that pull requests
with changes breaking this formatting will receive a commit from pre-commit.ci
automatically.
To automatically format code locally, you can install pre-commit and register a
*git hook* to automatically check with pre-commit before you make a commit if
the formatting is okay.
.. code:: bash
pip install pre-commit
pre-commit install --install-hooks
To run pre-commit manually you would do:
.. code:: bash
# check for changes to code not yet committed
pre-commit run
# check for changes also in already committed code
pre-commit run --all-files
You may also install `black integration <https://github.com/psf/black#editor-integration>`_
into your text editor to format code automatically.

1
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@@ -120,3 +120,4 @@ contribution on JupyterHub:
- yuvipanda
- zoltan-fedor
- zonca
- Neeraj Natu

46
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@@ -0,0 +1,46 @@
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_.
.. _logging: https://docs.python.org/3/library/logging.html
.. _`Telemetry System`: https://github.com/jupyter/telemetry
.. _`JSON schemas`: https://json-schema.org/
How to emit events
------------------
Event logging is handled by its ``Eventlog`` object. This leverages Python's standing logging_ library to emit, filter, and collect event data.
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 the event log file.
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:
.. code-block::
import logging
c.EventLog.handlers = [
logging.FileHandler('event.log'),
]
c.EventLog.allowed_schemas = [
'hub.jupyter.org/server-action'
]
The output is a file, ``"event.log"``, with events recorded as JSON data.
.. _page:
Event schemas
-------------
.. toctree::
:maxdepth: 2
server-actions.rst

1
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View File

@@ -0,0 +1 @@
.. jsonschema:: ../../../jupyterhub/event-schemas/server-actions/v1.yaml

85
docs/source/gallery-jhub-deployments.md
View File

@@ -8,27 +8,29 @@ high performance computing.
Please submit pull requests to update information or to add new institutions or uses.
## Academic Institutions, Research Labs, and Supercomputer Centers
### University of California Berkeley
- [BIDS - Berkeley Institute for Data Science](https://bids.berkeley.edu/)
- [Teaching with Jupyter notebooks and JupyterHub](https://bids.berkeley.edu/resources/videos/teaching-ipythonjupyter-notebooks-and-jupyterhub)
- [Teaching with Jupyter notebooks and JupyterHub](https://bids.berkeley.edu/resources/videos/teaching-ipythonjupyter-notebooks-and-jupyterhub)
- [Data 8](http://data8.org/)
- [GitHub organization](https://github.com/data-8)
- [GitHub organization](https://github.com/data-8)
- [NERSC](http://www.nersc.gov/)
- [Press release on Jupyter and Cori](http://www.nersc.gov/news-publications/nersc-news/nersc-center-news/2016/jupyter-notebooks-will-open-up-new-possibilities-on-nerscs-cori-supercomputer/)
- [Moving and sharing data](https://www.nersc.gov/assets/Uploads/03-MovingAndSharingData-Cholia.pdf)
- [Press release on Jupyter and Cori](http://www.nersc.gov/news-publications/nersc-news/nersc-center-news/2016/jupyter-notebooks-will-open-up-new-possibilities-on-nerscs-cori-supercomputer/)
- [Moving and sharing data](https://www.nersc.gov/assets/Uploads/03-MovingAndSharingData-Cholia.pdf)
- [Research IT](http://research-it.berkeley.edu)
- [JupyterHub server supports campus research computation](http://research-it.berkeley.edu/blog/17/01/24/free-fully-loaded-jupyterhub-server-supports-campus-research-computation)
- [JupyterHub server supports campus research computation](http://research-it.berkeley.edu/blog/17/01/24/free-fully-loaded-jupyterhub-server-supports-campus-research-computation)
### University of California Davis
- [Spinning up multiple Jupyter Notebooks on AWS for a tutorial](https://github.com/mblmicdiv/course2017/blob/master/exercises/sourmash-setup.md)
- [Spinning up multiple Jupyter Notebooks on AWS for a tutorial](https://github.com/mblmicdiv/course2017/blob/HEAD/exercises/sourmash-setup.md)
Although not technically a JupyterHub deployment, this tutorial setup
may be helpful to others in the Jupyter community.
@@ -59,23 +61,31 @@ easy to do with RStudio too.
- [jupyterhub-deploy-teaching](https://github.com/jupyterhub/jupyterhub-deploy-teaching) based on work by Brian Granger for Cal Poly's Data Science 301 Course
### Chameleon
[Chameleon](https://www.chameleoncloud.org) is a NSF-funded configurable experimental environment for large-scale computer science systems research with [bare metal reconfigurability](https://chameleoncloud.readthedocs.io/en/latest/technical/baremetal.html). Chameleon users utilize JupyterHub to document and reproduce their complex CISE and networking experiments.
- [Shared JupyterHub](https://jupyter.chameleoncloud.org): provides a common "workbench" environment for any Chameleon user.
- [Trovi](https://www.chameleoncloud.org/experiment/share): a sharing portal of experiments, tutorials, and examples, which users can launch as a dedicated isolated environments on Chameleon's JupyterHub.
### Clemson University
- Advanced Computing
- [Palmetto cluster and JupyterHub](http://citi.sites.clemson.edu/2016/08/18/JupyterHub-for-Palmetto-Cluster.html)
- [Palmetto cluster and JupyterHub](http://citi.sites.clemson.edu/2016/08/18/JupyterHub-for-Palmetto-Cluster.html)
### University of Colorado Boulder
- (CU Research Computing) CURC
- [JupyterHub User Guide](https://www.rc.colorado.edu/support/user-guide/jupyterhub.html)
- Slurm job dispatched on Crestone compute cluster
- log troubleshooting
- Profiles in IPython Clusters tab
- [Parallel Processing with JupyterHub tutorial](https://www.rc.colorado.edu/support/examples-and-tutorials/parallel-processing-with-jupyterhub.html)
- [Parallel Programming with JupyterHub document](https://www.rc.colorado.edu/book/export/html/833)
- [JupyterHub User Guide](https://www.rc.colorado.edu/support/user-guide/jupyterhub.html)
- Slurm job dispatched on Crestone compute cluster
- log troubleshooting
- Profiles in IPython Clusters tab
- [Parallel Processing with JupyterHub tutorial](https://www.rc.colorado.edu/support/examples-and-tutorials/parallel-processing-with-jupyterhub.html)
- [Parallel Programming with JupyterHub document](https://www.rc.colorado.edu/book/export/html/833)
- Earth Lab at CU
- [Tutorial on Parallel R on JupyterHub](https://earthdatascience.org/tutorials/parallel-r-on-jupyterhub/)
- [Tutorial on Parallel R on JupyterHub](https://earthdatascience.org/tutorials/parallel-r-on-jupyterhub/)
### George Washington University
@@ -87,7 +97,7 @@ easy to do with RStudio too.
### University of Illinois
- https://datascience.business.illinois.edu (currently down; checked 04/26/19)
- https://datascience.business.illinois.edu (currently down; checked 10/26/22)
### IllustrisTNG Simulation Project
@@ -112,11 +122,11 @@ easy to do with RStudio too.
### Paderborn University
- [Data Science (DICE) group](https://dice.cs.uni-paderborn.de/)
- [nbgraderutils](https://github.com/dice-group/nbgraderutils): Use JupyterHub + nbgrader + iJava kernel for online Java exercises. Used in lecture Statistical Natural Language Processing.
- [nbgraderutils](https://github.com/dice-group/nbgraderutils): Use JupyterHub + nbgrader + iJava kernel for online Java exercises. Used in lecture Statistical Natural Language Processing.
### Penn State University
- [Press release](https://news.psu.edu/story/523093/2018/05/24/new-open-source-web-apps-available-students-and-faculty): "New open-source web apps available for students and faculty" (but Hub is currently down; checked 04/26/19)
- [Press release](https://news.psu.edu/story/523093/2018/05/24/new-open-source-web-apps-available-students-and-faculty): "New open-source web apps available for students and faculty"
### University of Rochester CIRC
@@ -125,33 +135,34 @@ easy to do with RStudio too.
### University of California San Diego
- San Diego Supercomputer Center - Andrea Zonca
- [Deploy JupyterHub on a Supercomputer with SSH](https://zonca.github.io/2017/05/jupyterhub-hpc-batchspawner-ssh.html)
- [Run Jupyterhub on a Supercomputer](https://zonca.github.io/2015/04/jupyterhub-hpc.html)
- [Deploy JupyterHub on a VM for a Workshop](https://zonca.github.io/2016/04/jupyterhub-sdsc-cloud.html)
- [Customize your Python environment in Jupyterhub](https://zonca.github.io/2017/02/customize-python-environment-jupyterhub.html)
- [Jupyterhub deployment on multiple nodes with Docker Swarm](https://zonca.github.io/2016/05/jupyterhub-docker-swarm.html)
- [Sample deployment of Jupyterhub in HPC on SDSC Comet](https://zonca.github.io/2017/02/sample-deployment-jupyterhub-hpc.html)
- [Deploy JupyterHub on a Supercomputer with SSH](https://zonca.github.io/2017/05/jupyterhub-hpc-batchspawner-ssh.html)
- [Run Jupyterhub on a Supercomputer](https://zonca.github.io/2015/04/jupyterhub-hpc.html)
- [Deploy JupyterHub on a VM for a Workshop](https://zonca.github.io/2016/04/jupyterhub-sdsc-cloud.html)
- [Customize your Python environment in Jupyterhub](https://zonca.github.io/2017/02/customize-python-environment-jupyterhub.html)
- [Jupyterhub deployment on multiple nodes with Docker Swarm](https://zonca.github.io/2016/05/jupyterhub-docker-swarm.html)
- [Sample deployment of Jupyterhub in HPC on SDSC Comet](https://zonca.github.io/2017/02/sample-deployment-jupyterhub-hpc.html)
- Educational Technology Services - Paul Jamason
- [jupyterhub.ucsd.edu](https://jupyterhub.ucsd.edu)
- [jupyterhub.ucsd.edu](https://jupyterhub.ucsd.edu)
### TACC University of Texas
### Texas A&M
- Kristen Thyng - Oceanography
- [Teaching with JupyterHub and nbgrader](http://kristenthyng.com/blog/2016/09/07/jupyterhub+nbgrader/)
- [Teaching with JupyterHub and nbgrader](http://kristenthyng.com/blog/2016/09/07/jupyterhub+nbgrader/)
### Elucidata
- What's new in Jupyter Notebooks @[Elucidata](https://elucidata.io/):
- Using Jupyter Notebooks with Jupyterhub on GCP, managed by GKE
- https://medium.com/elucidata/why-you-should-be-using-a-jupyter-notebook-8385a4ccd93d
- What's new in Jupyter Notebooks @[Elucidata](https://elucidata.io/):
- [Using Jupyter Notebooks with Jupyterhub on GCP, managed by GKE](https://medium.com/elucidata/why-you-should-be-using-a-jupyter-notebook-8385a4ccd93d)
## Service Providers
### AWS
- [running-jupyter-notebook-and-jupyterhub-on-amazon-emr](https://aws.amazon.com/blogs/big-data/running-jupyter-notebook-and-jupyterhub-on-amazon-emr/)
- [Run Jupyter Notebook and JupyterHub on Amazon EMR](https://aws.amazon.com/blogs/big-data/running-jupyter-notebook-and-jupyterhub-on-amazon-emr/)
### Google Cloud Platform
@@ -164,28 +175,28 @@ easy to do with RStudio too.
### Microsoft Azure
- https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-linux-dsvm-intro
- [Azure Data Science Virtual Machine release notes](https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-linux-dsvm-intro)
### Rackspace Carina
- https://getcarina.com/blog/learning-how-to-whale/
- http://carolynvanslyck.com/talk/carina/jupyterhub/#/
- http://carolynvanslyck.com/talk/carina/jupyterhub/#/ (but carolynvanslyck is currently down; checked 10/26/22)
### Hadoop
- [Deploying JupyterHub on Hadoop](https://jupyterhub-on-hadoop.readthedocs.io)
## Miscellaneous
- https://medium.com/@ybarraud/setting-up-jupyterhub-with-sudospawner-and-anaconda-844628c0dbee#.rm3yt87e1
- https://groups.google.com/forum/#!topic/jupyter/nkPSEeMr8c0 Mailing list UT deployment
- JupyterHub setup on Centos https://gist.github.com/johnrc/604971f7d41ebf12370bf5729bf3e0a4
- Deploy JupyterHub to Docker Swarm https://jupyterhub.surge.sh/#/welcome
- [Mailing list UT deployment](https://groups.google.com/forum/#!topic/jupyter/nkPSEeMr8c0)
- [JupyterHub setup on Centos](https://gist.github.com/johnrc/604971f7d41ebf12370bf5729bf3e0a4)
- [Deploy JupyterHub to Docker Swarm](https://jupyterhub.surge.sh/#/welcome)
- http://www.laketide.com/building-your-lab-part-3/
- http://estrellita.hatenablog.com/entry/2015/07/31/083202
- http://www.walkingrandomly.com/?p=5734
- https://wrdrd.com/docs/consulting/education-technology
- https://bitbucket.org/jackhale/fenics-jupyter
- [LinuxCluster blog](https://linuxcluster.wordpress.com/category/application/jupyterhub/)
- [Network Technology](https://arnesund.com/tag/jupyterhub/) [Spark Cluster on OpenStack with Multi-User Jupyter Notebook](https://arnesund.com/2015/09/21/spark-cluster-on-openstack-with-multi-user-jupyter-notebook/)
- [Network Technology](https://arnesund.com/tag/jupyterhub/)
- [Spark Cluster on OpenStack with Multi-User Jupyter Notebook](https://arnesund.com/2015/09/21/spark-cluster-on-openstack-with-multi-user-jupyter-notebook/)

95
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@@ -1,40 +1,51 @@
# Authentication and User Basics
The default Authenticator uses [PAM][] to authenticate system users with
The default Authenticator uses [PAM][] (Pluggable Authentication Module) to authenticate system users with
their username and password. With the default Authenticator, any user
with an account and password on the system will be allowed to login.
## Create a whitelist of users
You can restrict which users are allowed to login with a whitelist,
`Authenticator.whitelist`:
## Create a set of allowed users (`allowed_users`)
You can restrict which users are allowed to login with a set,
`Authenticator.allowed_users`:
```python
c.Authenticator.whitelist = {'mal', 'zoe', 'inara', 'kaylee'}
c.Authenticator.allowed_users = {'mal', 'zoe', 'inara', 'kaylee'}
```
Users in the whitelist are added to the Hub database when the Hub is
Users in the `allowed_users` set are added to the Hub database when the Hub is
started.
```{warning}
If this configuration value is not set, then **all authenticated users will be allowed into your hub**.
```
## Configure admins (`admin_users`)
```{note}
As of JupyterHub 2.0, the full permissions of `admin_users`
should not be required.
Instead, you can assign [roles](define-role-target) to users or groups
with only the scopes they require.
```
Admin users of JupyterHub, `admin_users`, can add and remove users from
the user `whitelist`. `admin_users` can take actions on other users'
the user `allowed_users` set. `admin_users` can take actions on other users'
behalf, such as stopping and restarting their servers.
A set of initial admin users, `admin_users` can configured be as follows:
A set of initial admin users, `admin_users` can be configured as follows:
```python
c.Authenticator.admin_users = {'mal', 'zoe'}
```
Users in the admin list are automatically added to the user `whitelist`,
Users in the admin set are automatically added to the user `allowed_users` set,
if they are not already present.
Each authenticator may have different ways of determining whether a user is an
administrator. By default JupyterHub use the PAMAuthenticator which provide the
`admin_groups` option and can determine administrator status base on a user
groups. For example we can let any users in the `wheel` group be admin:
Each Authenticator may have different ways of determining whether a user is an
administrator. By default, JupyterHub uses the PAMAuthenticator which provides the
`admin_groups` option and can set administrator status based on a user
group. For example, we can let any user in the `wheel` group be an admin:
```python
c.PAMAuthenticator.admin_groups = {'wheel'}
@@ -42,35 +53,35 @@ c.PAMAuthenticator.admin_groups = {'wheel'}
## Give admin access to other users' notebook servers (`admin_access`)
Since the default `JupyterHub.admin_access` setting is False, the admins
Since the default `JupyterHub.admin_access` setting is `False`, the admins
do not have permission to log in to the single user notebook servers
owned by *other users*. If `JupyterHub.admin_access` is set to True,
then admins have permission to log in *as other users* on their
respective machines, for debugging. **As a courtesy, you should make
owned by _other users_. If `JupyterHub.admin_access` is set to `True`,
then admins have permission to log in _as other users_ on their
respective machines for debugging. **As a courtesy, you should make
sure your users know if admin_access is enabled.**
## Add or remove users from the Hub
Users can be added to and removed from the Hub via either the admin
Users can be added to and removed from the Hub via the admin
panel or the REST API. When a user is **added**, the user will be
automatically added to the whitelist and database. Restarting the Hub
will not require manually updating the whitelist in your config file,
automatically added to the `allowed_users` set and database. Restarting the Hub
will not require manually updating the `allowed_users` set in your config file,
as the users will be loaded from the database.
After starting the Hub once, it is not sufficient to **remove** a user
from the whitelist in your config file. You must also remove the user
from the allowed users set in your config file. You must also remove the user
from the Hub's database, either by deleting the user from JupyterHub's
admin page, or you can clear the `jupyterhub.sqlite` database and start
fresh.
## Use LocalAuthenticator to create system users
The `LocalAuthenticator` is a special kind of authenticator that has
The `LocalAuthenticator` is a special kind of Authenticator that has
the ability to manage users on the local system. When you try to add a
new user to the Hub, a `LocalAuthenticator` will check if the user
already exists. If you set the configuration value, `create_system_users`,
to `True` in the configuration file, the `LocalAuthenticator` has
the privileges to add users to the system. The setting in the config
the ability to add users to the system. The setting in the config
file is:
```python
@@ -80,7 +91,7 @@ c.LocalAuthenticator.create_system_users = True
Adding a user to the Hub that doesn't already exist on the system will
result in the Hub creating that user via the system `adduser` command
line tool. This option is typically used on hosted deployments of
JupyterHub, to avoid the need to manually create all your users before
JupyterHub to avoid the need to manually create all your users before
launching the service. This approach is not recommended when running
JupyterHub in situations where JupyterHub users map directly onto the
system's UNIX users.
@@ -90,27 +101,25 @@ system's UNIX users.
JupyterHub's [OAuthenticator][] currently supports the following
popular services:
- Auth0
- Bitbucket
- CILogon
- GitHub
- GitLab
- Globus
- Google
- MediaWiki
- Okpy
- OpenShift
- [Auth0](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.auth0.html#module-oauthenticator.auth0)
- [Azure AD](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.azuread.html#module-oauthenticator.azuread)
- [Bitbucket](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.bitbucket.html#module-oauthenticator.bitbucket)
- [CILogon](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.cilogon.html#module-oauthenticator.cilogon)
- [GitHub](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.github.html#module-oauthenticator.github)
- [GitLab](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.gitlab.html#module-oauthenticator.gitlab)
- [Globus](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.globus.html#module-oauthenticator.globus)
- [Google](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.google.html#module-oauthenticator.google)
- [MediaWiki](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.mediawiki.html#module-oauthenticator.mediawiki)
- [Okpy](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.okpy.html#module-oauthenticator.okpy)
- [OpenShift](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.openshift.html#module-oauthenticator.openshift)
NOTE: Open issue asking for more details on this generic implementation.
It's not clear if this is a different implementation or if the JupyterHub OAuth
_is_ the generic implementation.
A generic implementation, which you can use for OAuth authentication
A [generic implementation](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.generic.html#module-oauthenticator.generic), which you can use for OAuth authentication
with any provider, is also available.
## Use DummyAuthenticator for testing
The :class:`~jupyterhub.auth.DummyAuthenticator` is a simple authenticator that
allows for any username/password unless if a global password has been set. If
The `DummyAuthenticator` is a simple Authenticator that
allows for any username or password unless a global password has been set. If
set, it will allow for any username as long as the correct password is provided.
To set a global password, add this to the config file:
@@ -118,5 +127,5 @@ To set a global password, add this to the config file:
c.DummyAuthenticator.password = "some_password"
```
[PAM]: https://en.wikipedia.org/wiki/Pluggable_authentication_module
[OAuthenticator]: https://github.com/jupyterhub/oauthenticator
[pam]: https://en.wikipedia.org/wiki/Pluggable_authentication_module
[oauthenticator]: https://github.com/jupyterhub/oauthenticator

24
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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.
@@ -44,30 +44,30 @@ jupyterhub -f /etc/jupyterhub/jupyterhub_config.py
```
The IPython documentation provides additional information on the
[config system](http://ipython.readthedocs.io/en/stable/development/config)
[config system](http://ipython.readthedocs.io/en/stable/development/config.html)
that Jupyter uses.
## Configure using command line options
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
```
Configuration using the command line options is done when launching JupyterHub.
For example, to start JupyterHub on ``10.0.1.2:443`` with https, you
For example, to start JupyterHub on `10.0.1.2:443` with https, you
would enter:
```bash
jupyterhub --ip 10.0.1.2 --port 443 --ssl-key my_ssl.key --ssl-cert my_ssl.cert
```
All configurable options may technically be set on the command-line,
All configurable options may technically be set on the command line,
though some are inconvenient to type. To set a particular configuration
parameter, `c.Class.trait`, you would use the command line option,
`--Class.trait`, when starting JupyterHub. For example, to configure the
`c.Spawner.notebook_dir` trait from the command-line, use the
`c.Spawner.notebook_dir` trait from the command line, use the
`--Spawner.notebook_dir` option:
```bash
@@ -77,24 +77,24 @@ jupyterhub --Spawner.notebook_dir='~/assignments'
## Configure for various deployment environments
The default authentication and process spawning mechanisms can be replaced, and
specific [authenticators](./authenticators-users-basics) and
[spawners](./spawners-basics) can be set in the configuration file.
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)
## Run the proxy separately
This is *not* strictly necessary, but useful in many cases. If you
use a custom proxy (e.g. Traefik), this also not needed.
This is _not_ strictly necessary, but useful in many cases. If you
use a custom proxy (e.g. Traefik), this is also not needed.
Connections to user servers go through the proxy, and *not* the hub
itself. If the proxy stays running when the hub restarts (for
maintenance, re-configuration, etc.), then user connections are not
interrupted. For simplicity, by default the hub starts the proxy
automatically, so if the hub restarts, the proxy restarts, and user
connections are interrupted. It is easy to run the proxy separately,
connections are interrupted. It is easy to run the proxy separately,
for information see [the separate proxy page](../reference/separate-proxy).

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@@ -0,0 +1,35 @@
# Frequently asked questions
## How do I share links to notebooks?
In short, where you see `/user/name/notebooks/foo.ipynb` use `/hub/user-redirect/notebooks/foo.ipynb` (replace `/user/name` with `/hub/user-redirect`).
Sharing links to notebooks is a common activity,
and can look different based on what you mean.
Your first instinct might be to copy the URL you see in the browser,
e.g. `hub.jupyter.org/user/yourname/notebooks/coolthing.ipynb`.
However, let's break down what this URL means:
`hub.jupyter.org/user/yourname/` is the URL prefix handled by _your server_,
which means that sharing this URL is asking the person you share the link with
to come to _your server_ and look at the exact same file.
In most circumstances, this is forbidden by permissions because the person you share with does not have access to your server.
What actually happens when someone visits this URL will depend on whether your server is running and other factors.
But what is our actual goal?
A typical situation is that you have some shared or common filesystem,
such that the same path corresponds to the same document
(either the exact same document or another copy of it).
Typically, what folks want when they do sharing like this
is for each visitor to open the same file _on their own server_,
so Breq would open `/user/breq/notebooks/foo.ipynb` and
Seivarden would open `/user/seivarden/notebooks/foo.ipynb`, etc.
JupyterHub has a special URL that does exactly this!
It's called `/hub/user-redirect/...`.
So if you replace `/user/yourname` in your URL bar
with `/hub/user-redirect` any visitor should get the same
URL on their own server, rather than visiting yours.
In JupyterLab 2.0, this should also be the result of the "Copy Shareable Link"
action in the file browser.

11
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View File

@@ -1,5 +1,10 @@
Getting Started
===============
Get Started
===========
This section covers how to configure and customize JupyterHub for your
needs. It contains information about authentication, networking, security, and
other topics that are relevant to individuals or organizations deploying their
own JupyterHub.
.. toctree::
:maxdepth: 2
@@ -10,3 +15,5 @@ Getting Started
authenticators-users-basics
spawners-basics
services-basics
faq
institutional-faq

260
docs/source/getting-started/institutional-faq.md Normal file
View File

@@ -0,0 +1,260 @@
# Institutional FAQ
This page contains common questions from users of JupyterHub,
broken down by their roles within organizations.
## For all
### Is it appropriate for adoption within a larger institutional context?
Yes! JupyterHub has been used at-scale for large pools of users, as well
as complex and high-performance computing. For example, UC Berkeley uses
JupyterHub for its Data Science Education Program courses (serving over
3,000 students). The Pangeo project uses JupyterHub to provide access
to scalable cloud computing with Dask. JupyterHub is stable and customizable
to the use-cases of large organizations.
### I keep hearing about Jupyter Notebook, JupyterLab, and now JupyterHub. What’s the difference?
Here is a quick breakdown of these three tools:
- **The Jupyter Notebook** is a document specification (the `.ipynb`) file that interweaves
narrative text with code cells and their outputs. It is also a graphical interface
that allows users to edit these documents. There are also several other graphical interfaces
that allow users to edit the `.ipynb` format (nteract, Jupyter Lab, Google Colab, Kaggle, etc).
- **JupyterLab** is a flexible and extendible user interface for interactive computing. It
has several extensions that are tailored for using Jupyter Notebooks, as well as extensions
for other parts of the data science stack.
- **JupyterHub** is an application that manages interactive computing sessions for **multiple users**.
It also connects them with infrastructure those users wish to access. It can provide
remote access to Jupyter Notebooks and JupyterLab for many people.
## For management
### Briefly, what problem does JupyterHub solve for us?
JupyterHub provides a shared platform for data science and collaboration.
It allows users to utilize familiar data science workflows (such as the scientific Python stack,
the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also allows administrators
some control over access to resources, security, environments, and authentication.
### Is JupyterHub mature? Why should we trust it?
Yes - the core JupyterHub application recently
reached 1.0 status, and is considered stable and performant for most institutions.
JupyterHub has also been deployed (along with other tools) to work on
scalable infrastructure, large datasets, and high-performance computing.
### Who else uses JupyterHub?
JupyterHub is used at a variety of institutions in academia,
industry, and government research labs. It is most-commonly used by two kinds of groups:
- Small teams (e.g., data science teams, research labs, or collaborative projects) to provide a
shared resource for interactive computing, collaboration, and analytics.
- Large teams (e.g., a department, a large class, or a large group of remote users) to provide
access to organizational hardware, data, and analytics environments at scale.
Here is a sample of organizations that use JupyterHub:
- **Universities and colleges**: UC Berkeley, UC San Diego, Cal Poly SLO, Harvard University, University of Chicago,
University of Oslo, University of Sheffield, Université Paris Sud, University of Versailles
- **Research laboratories**: NASA, NCAR, NOAA, the Large Synoptic Survey Telescope, Brookhaven National Lab,
Minnesota Supercomputing Institute, ALCF, CERN, Lawrence Livermore National Laboratory
- **Online communities**: Pangeo, Quantopian, mybinder.org, MathHub, Open Humans
- **Computing infrastructure providers**: NERSC, San Diego Supercomputing Center, Compute Canada
- **Companies**: Capital One, SANDVIK code, Globus
See the [Gallery of JupyterHub deployments](../gallery-jhub-deployments.md) for
a more complete list of JupyterHub deployments at institutions.
### How does JupyterHub compare with hosted products, like Google Colaboratory, RStudio.cloud, or Anaconda Enterprise?
JupyterHub puts you in control of your data, infrastructure, and coding environment.
In addition, it is vendor neutral, which reduces lock-in to a particular vendor or service.
JupyterHub provides access to interactive computing environments in the cloud (similar to each of these services).
Compared with the tools above, it is more flexible, more customizable, free, and
gives administrators more control over their setup and hardware.
Because JupyterHub is an open-source, community-driven tool, it can be extended and
modified to fit an institution's needs. It plays nicely with the open source data science
stack, and can serve a variety of computing environments, user interfaces, and
computational hardware. It can also be deployed anywhere - on enterprise cloud infrastructure, on
High-Performance-Computing machines, on local hardware, or even on a single laptop, which
is not possible with most other tools for shared interactive computing.
## For IT
### How would I set up JupyterHub on institutional hardware?
That depends on what kind of hardware you've got. JupyterHub is flexible enough to be deployed
on a variety of hardware, including in-room hardware, on-prem clusters, cloud infrastructure,
etc.
The most common way to set up a JupyterHub is to use a JupyterHub distribution, these are pre-configured
and opinionated ways to set up a JupyterHub on particular kinds of infrastructure. The two distributions
that we currently suggest are:
- [Zero to JupyterHub for Kubernetes](https://z2jh.jupyter.org) is a scalable JupyterHub deployment and
guide that runs on Kubernetes. Better for larger or dynamic user groups (50-10,000) or more complex
compute/data needs.
- [The Littlest JupyterHub](https://tljh.jupyter.org) is a lightweight JupyterHub that runs on a single
single machine (in the cloud or under your desk). Better for smaller user groups (4-80) or more
lightweight computational resources.
### Does JupyterHub run well in the cloud?
Yes - most deployments of JupyterHub are run via cloud infrastructure and on a variety of cloud providers.
Depending on the distribution of JupyterHub that you'd like to use, you can also connect your JupyterHub
deployment with a number of other cloud-native services so that users have access to other resources from
their interactive computing sessions.
For example, if you use the [Zero to JupyterHub for Kubernetes](https://z2jh.jupyter.org) distribution,
you'll be able to utilize container-based workflows of other technologies such as the [dask-kubernetes](https://kubernetes.dask.org/en/latest/)
project for distributed computing.
The Z2JH Helm Chart also has some functionality built in for auto-scaling your cluster up and down
as more resources are needed - allowing you to utilize the benefits of a flexible cloud-based deployment.
### Is JupyterHub secure?
The short answer: yes. JupyterHub as a standalone application has been battle-tested at an institutional
level for several years, and makes a number of "default" security decisions that are reasonable for most
users.
- For security considerations in the base JupyterHub application,
[see the JupyterHub security page](https://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html).
- For security considerations when deploying JupyterHub on Kubernetes, see the
[JupyterHub on Kubernetes security page](https://zero-to-jupyterhub.readthedocs.io/en/latest/security.html).
The longer answer: it depends on your deployment. Because JupyterHub is very flexible, it can be used
in a variety of deployment setups. This often entails connecting your JupyterHub to **other** infrastructure
(such as a [Dask Gateway service](https://gateway.dask.org/)). There are many security decisions to be made
in these cases, and the security of your JupyterHub deployment will often depend on these decisions.
If you are worried about security, don't hesitate to reach out to the JupyterHub community in the
[Jupyter Community Forum](https://discourse.jupyter.org/c/jupyterhub). This community of practice has many
individuals with experience running secure JupyterHub deployments.
### Does JupyterHub provide computing or data infrastructure?
No - JupyterHub manages user sessions and can _control_ computing infrastructure, but it does not provide these
things itself. You are expected to run JupyterHub on your own infrastructure (local or in the cloud). Moreover,
JupyterHub has no internal concept of "data", but is designed to be able to communicate with data repositories
(again, either locally or remotely) for use within interactive computing sessions.
### How do I manage users?
JupyterHub offers a few options for managing your users. Upon setting up a JupyterHub, you can choose what
kind of **authentication** you'd like to use. For example, you can have users sign up with an institutional
email address, or choose a username / password when they first log-in, or offload authentication onto
another service such as an organization's OAuth.
The users of a JupyterHub are stored locally, and can be modified manually by an administrator of the JupyterHub.
Moreover, the _active_ users on a JupyterHub can be found on the administrator's page. This page
gives you the abiltiy to stop or restart kernels, inspect user filesystems, and even take over user
sessions to assist them with debugging.
### How do I manage software environments?
A key benefit of JupyterHub is the ability for an administrator to define the environment(s) that users
have access to. There are many ways to do this, depending on what kind of infrastructure you're using for
your JupyterHub.
For example, **The Littlest JupyterHub** runs on a single VM. In this case, the administrator defines
an environment by installing packages to a shared folder that exists on the path of all users. The
**JupyterHub for Kubernetes** deployment uses Docker images to define environments. You can create your
own list of Docker images that users can select from, and can also control things like the amount of
RAM available to users, or the types of machines that their sessions will use in the cloud.
### How does JupyterHub manage computational resources?
For interactive computing sessions, JupyterHub controls computational resources via a **spawner**.
Spawners define how a new user session is created, and are customized for particular kinds of
infrastructure. For example, the KubeSpawner knows how to control a Kubernetes deployment
to create new pods when users log in.
For more sophisticated computational resources (like distributed computing), JupyterHub can
connect with other infrastructure tools (like Dask or Spark). This allows users to control
scalable or high-performance resources from within their JupyterHub sessions. The logic of
how those resources are controlled is taken care of by the non-JupyterHub application.
### Can JupyterHub be used with my high-performance computing resources?
Yes - JupyterHub can provide access to many kinds of computing infrastructure.
Especially when combined with other open-source schedulers such as Dask, you can manage fairly
complex computing infrastructures from the interactive sessions of a JupyterHub. For example
[see the Dask HPC page](https://docs.dask.org/en/latest/setup/hpc.html).
### How much resources do user sessions take?
This is highly configurable by the administrator. If you wish for your users to have simple
data analytics environments for prototyping and light data exploring, you can restrict their
memory and CPU based on the resources that you have available. If you'd like your JupyterHub
to serve as a gateway to high-performance compute or data resources, you may increase the
resources available on user machines, or connect them with computing infrastructures elsewhere.
### Can I customize the look and feel of a JupyterHub?
JupyterHub provides some customization of the graphics displayed to users. The most common
modification is to add custom branding to the JupyterHub login page, loading pages, and
various elements that persist across all pages (such as headers).
## For Technical Leads
### Will JupyterHub “just work” with our team's interactive computing setup?
Depending on the complexity of your setup, you'll have different experiences with "out of the box"
distributions of JupyterHub. If all of the resources you need will fit on a single VM, then
[The Littlest JupyterHub](https://tljh.jupyter.org) should get you up-and-running within
a half day or so. For more complex setups, such as scalable Kubernetes clusters or access
to high-performance computing and data, it will require more time and expertise with
the technologies your JupyterHub will use (e.g., dev-ops knowledge with cloud computing).
In general, the base JupyterHub deployment is not the bottleneck for setup, it is connecting
your JupyterHub with the various services and tools that you wish to provide to your users.
### How well does JupyterHub scale? What are JupyterHub's limitations?
JupyterHub works well at both a small scale (e.g., a single VM or machine) as well as a
high scale (e.g., a scalable Kubernetes cluster). It can be used for teams as small as 2, and
for user bases as large as 10,000. The scalability of JupyterHub largely depends on the
infrastructure on which it is deployed. JupyterHub has been designed to be lightweight and
flexible, so you can tailor your JupyterHub deployment to your needs.
### Is JupyterHub resilient? What happens when a machine goes down?
For JupyterHubs that are deployed in a containerized environment (e.g., Kubernetes), it is
possible to configure the JupyterHub to be fairly resistant to failures in the system.
For example, if JupyterHub fails, then user sessions will not be affected (though new
users will not be able to log in). When a JupyterHub process is restarted, it should
seamlessly connect with the user database and the system will return to normal.
Again, the details of your JupyterHub deployment (e.g., whether it's deployed on a scalable cluster)
will affect the resiliency of the deployment.
### What interfaces does JupyterHub support?
Out of the box, JupyterHub supports a variety of popular data science interfaces for user sessions,
such as JupyterLab, Jupyter Notebooks, and RStudio. Any interface that can be served
via a web address can be served with a JupyterHub (with the right setup).
### Does JupyterHub make it easier for our team to collaborate?
JupyterHub provides a standardized environment and access to shared resources for your teams.
This greatly reduces the cost associated with sharing analyses and content with other team
members, and makes it easier to collaborate and build off of one another's ideas. Combined with
access to high-performance computing and data, JupyterHub provides a common resource to
amplify your team's ability to prototype their analyses, scale them to larger data, and then
share their results with one another.
JupyterHub also provides a computational framework to share computational narratives between
different levels of an organization. For example, data scientists can share Jupyter Notebooks
rendered as [Voilà dashboards](https://voila.readthedocs.io/en/stable/) with those who are not
familiar with programming, or create publicly-available interactive analyses to allow others to
interact with your work.
### Can I use JupyterHub with R/RStudio or other languages and environments?
Yes, Jupyter is a polyglot project, and there are over 40 community-provided kernels for a variety
of languages (the most common being Python, Julia, and R). You can also use a JupyterHub to provide
access to other interfaces, such as RStudio, that provide their own access to a language kernel.

14
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View File

@@ -11,8 +11,8 @@ This section will help you with basic proxy and network configuration to:
The Proxy's main IP address setting determines where JupyterHub is available to users.
By default, JupyterHub is configured to be available on all network interfaces
(`''`) on port 8000. *Note*: Use of `'*'` is discouraged for IP configuration;
instead, use of `'0.0.0.0'` is preferred.
(`''`) on port 8000. _Note_: Use of `'*'` is discouraged for IP configuration;
instead, use of `'0.0.0.0'` is preferred.
Changing the Proxy's main IP address and port can be done with the following
JupyterHub **command line options**:
@@ -74,7 +74,7 @@ The Hub service listens only on `localhost` (port 8081) by default.
The Hub needs to be accessible from both the proxy and all Spawners.
When spawning local servers, an IP address setting of `localhost` is fine.
If *either* the Proxy *or* (more likely) the Spawners will be remote or
If _either_ the Proxy _or_ (more likely) the Spawners will be remote or
isolated in containers, the Hub must listen on an IP that is accessible.
```python
@@ -82,20 +82,20 @@ c.JupyterHub.hub_ip = '10.0.1.4'
c.JupyterHub.hub_port = 54321
```
**Added in 0.8:** The `c.JupyterHub.hub_connect_ip` setting is the ip address or
**Added in 0.8:** The `c.JupyterHub.hub_connect_ip` setting is the IP address or
hostname that other services should use to connect to the Hub. A common
configuration for, e.g. docker, is:
```python
c.JupyterHub.hub_ip = '0.0.0.0' # listen on all interfaces
c.JupyterHub.hub_connect_ip = '10.0.1.4' # ip as seen on the docker network. Can also be a hostname.
c.JupyterHub.hub_connect_ip = '10.0.1.4' # IP as seen on the docker network. Can also be a hostname.
```
## Adjusting the hub's URL
The hub will most commonly be running on a hostname of its own. If it
The hub will most commonly be running on a hostname of its own. If it
is not – for example, if the hub is being reverse-proxied and being
exposed at a URL such as `https://proxy.example.org/jupyter/` – then
you will need to tell JupyterHub the base URL of the service. In such
you will need to tell JupyterHub the base URL of the service. In such
a case, it is both necessary and sufficient to set
`c.JupyterHub.base_url = '/jupyter/'` in the configuration.

146
docs/source/getting-started/security-basics.rst
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@@ -5,17 +5,17 @@ Security settings
You should not run JupyterHub without SSL encryption on a public network.
Security is the most important aspect of configuring Jupyter. Three
configuration settings are the main aspects of security configuration:
Security is the most important aspect of configuring Jupyter.
Three (3) configuration settings are the main aspects of security configuration:
1. :ref:`SSL encryption <ssl-encryption>` (to enable HTTPS)
2. :ref:`Cookie secret <cookie-secret>` (a key for encrypting browser cookies)
3. Proxy :ref:`authentication token <authentication-token>` (used for the Hub and
other services to authenticate to the Proxy)
The Hub hashes all secrets (e.g., auth tokens) before storing them in its
The Hub hashes all secrets (e.g. auth tokens) before storing them in its
database. A loss of control over read-access to the database should have
minimal impact on your deployment; if your database has been compromised, it
minimal impact on your deployment. If your database has been compromised, it
is still a good idea to revoke existing tokens.
.. _ssl-encryption:
@@ -31,7 +31,7 @@ Using an SSL certificate
This will require you to obtain an official, trusted SSL certificate or create a
self-signed certificate. Once you have obtained and installed a key and
certificate you need to specify their locations in the ``jupyterhub_config.py``
certificate, you need to specify their locations in the ``jupyterhub_config.py``
configuration file as follows:
.. code-block:: python
@@ -72,7 +72,7 @@ would be the needed configuration:
If SSL termination happens outside of the Hub
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In certain cases, for example if the hub is running behind a reverse proxy, and
In certain cases, for example, if the hub is running behind a reverse proxy, and
`SSL termination is being provided by NGINX <https://www.nginx.com/resources/admin-guide/nginx-ssl-termination/>`_,
it is reasonable to run the hub without SSL.
@@ -80,12 +80,55 @@ To achieve this, remove ``c.JupyterHub.ssl_key`` and ``c.JupyterHub.ssl_cert``
from your configuration (setting them to ``None`` or an empty string does not
have the same effect, and will result in an error).
.. _authentication-token:
Proxy authentication token
--------------------------
The Hub authenticates its requests to the Proxy using a secret token that
the Hub and Proxy agree upon. Note that this applies to the default
``ConfigurableHTTPProxy`` implementation. Not all proxy implementations
use an auth token.
The value of this token should be a random string (for example, generated by
``openssl rand -hex 32``). You can store it in the configuration file or an
environment variable.
Generating and storing token in the configuration file
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You can set the value in the configuration file, ``jupyterhub_config.py``:
.. code-block:: python
c.ConfigurableHTTPProxy.api_token = 'abc123...' # any random string
Generating and storing as an environment variable
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You can pass this value of the proxy authentication token to the Hub and Proxy
using the ``CONFIGPROXY_AUTH_TOKEN`` environment variable:
.. code-block:: bash
export CONFIGPROXY_AUTH_TOKEN=$(openssl rand -hex 32)
This environment variable needs to be visible to the Hub and Proxy.
Default if token is not set
~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you do not set the Proxy authentication token, the Hub will generate a random
key itself. This means that any time you restart the Hub, you **must also
restart the Proxy**. If the proxy is a subprocess of the Hub, this should happen
automatically (this is the default configuration).
.. _cookie-secret:
Cookie secret
-------------
The cookie secret is an encryption key, used to encrypt the browser cookies
The cookie secret is an encryption key, used to encrypt the browser cookies,
which are used for authentication. Three common methods are described for
generating and configuring the cookie secret.
@@ -93,8 +136,8 @@ Generating and storing as a cookie secret file
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The cookie secret should be 32 random bytes, encoded as hex, and is typically
stored in a ``jupyterhub_cookie_secret`` file. An example command to generate the
``jupyterhub_cookie_secret`` file is:
stored in a ``jupyterhub_cookie_secret`` file. Below, is an example command to generate the
``jupyterhub_cookie_secret`` file:
.. code-block:: bash
@@ -112,7 +155,7 @@ The location of the ``jupyterhub_cookie_secret`` file can be specified in the
If the cookie secret file doesn't exist when the Hub starts, a new cookie
secret is generated and stored in the file. The file must not be readable by
``group`` or ``other`` or the server won't start. The recommended permissions
``group`` or ``other``, otherwise the server won't start. The recommended permissions
for the cookie secret file are ``600`` (owner-only rw).
Generating and storing as an environment variable
@@ -133,54 +176,79 @@ the Hub starts.
Generating and storing as a binary string
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You can also set the cookie secret in the configuration file
itself, ``jupyterhub_config.py``, as a binary string:
You can also set the cookie secret, as a binary string,
in the configuration file (``jupyterhub_config.py``) itself:
.. code-block:: python
c.JupyterHub.cookie_secret = bytes.fromhex('64 CHAR HEX STRING')
.. _cookies:
.. important::
Cookies used by JupyterHub authentication
-----------------------------------------
If the cookie secret value changes for the Hub, all single-user notebook
servers must also be restarted.
The following cookies are used by the Hub for handling user authentication.
This section was created based on this post_ from Discourse.
.. _authentication-token:
.. _post: https://discourse.jupyter.org/t/how-to-force-re-login-for-users/1998/6
Proxy authentication token
--------------------------
jupyterhub-hub-login
~~~~~~~~~~~~~~~~~~~~
The Hub authenticates its requests to the Proxy using a secret token that
the Hub and Proxy agree upon. The value of this string should be a random
string (for example, generated by ``openssl rand -hex 32``).
This is the login token used when visiting Hub-served pages that are
protected by authentication, such as the main home, the spawn form, etc.
If this cookie is set, then the user is logged in.
Generating and storing token in the configuration file
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Resetting the Hub cookie secret effectively revokes this cookie.
Or you can set the value in the configuration file, ``jupyterhub_config.py``:
This cookie is restricted to the path ``/hub/``.
.. code-block:: python
jupyterhub-user-<username>
~~~~~~~~~~~~~~~~~~~~~~~~~~
c.JupyterHub.proxy_auth_token = '0bc02bede919e99a26de1e2a7a5aadfaf6228de836ec39a05a6c6942831d8fe5'
This is the cookie used for authenticating with a single-user server.
It is set by the single-user server, after OAuth with the Hub.
Generating and storing as an environment variable
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Effectively the same as ``jupyterhub-hub-login``, but for the
single-user server instead of the Hub. It contains an OAuth access token,
which is checked with the Hub to authenticate the browser.
You can pass this value of the proxy authentication token to the Hub and Proxy
using the ``CONFIGPROXY_AUTH_TOKEN`` environment variable:
Each OAuth access token is associated with a session id (see ``jupyterhub-session-id`` section
below).
.. code-block:: bash
To avoid hitting the Hub on every request, the authentication response is cached.
The cache key is comprised of both the token and session id, to avoid a stale cache.
export CONFIGPROXY_AUTH_TOKEN=$(openssl rand -hex 32)
Resetting the Hub cookie secret effectively revokes this cookie.
This environment variable needs to be visible to the Hub and Proxy.
This cookie is restricted to the path ``/user/<username>``,
to ensure that only the user’s server receives it.
Default if token is not set
~~~~~~~~~~~~~~~~~~~~~~~~~~~
jupyterhub-session-id
~~~~~~~~~~~~~~~~~~~~~
If you don't set the Proxy authentication token, the Hub will generate a random
key itself, which means that any time you restart the Hub you **must also
restart the Proxy**. If the proxy is a subprocess of the Hub, this should happen
automatically (this is the default configuration).
This is a random string, meaningless in itself, and the only cookie
shared by the Hub and single-user servers.
Its sole purpose is to coordinate logout of the multiple OAuth cookies.
This cookie is set to ``/`` so all endpoints can receive it, clear it, etc.
jupyterhub-user-<username>-oauth-state
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A short-lived cookie, used solely to store and validate OAuth state.
It is only set while OAuth between the single-user server and the Hub
is processing.
If you use your browser development tools, you should see this cookie
for a very brief moment before you are logged in,
with an expiration date shorter than ``jupyterhub-hub-login`` or
``jupyterhub-user-<username>``.
This cookie should not exist after you have successfully logged in.
This cookie is restricted to the path ``/user/<username>``, so that only
the user’s server receives it.

78
docs/source/getting-started/services-basics.md
View File

@@ -2,10 +2,10 @@
When working with JupyterHub, a **Service** is defined as a process
that interacts with the Hub's REST API. A Service may perform a specific
or action or task. For example, shutting down individuals' single user
action or task. For example, shutting down individuals' single user
notebook servers that have been idle for some time is a good example of
a task that could be automated by a Service. Let's look at how the
[cull_idle_servers][] script can be used as a Service.
[jupyterhub_idle_culler][] script can be used as a Service.
## Real-world example to cull idle servers
@@ -15,16 +15,16 @@ document will:
- explain some basic information about API tokens
- clarify that API tokens can be used to authenticate to
single-user servers as of [version 0.8.0](../changelog)
- show how the [cull_idle_servers][] script can be:
- used in a Hub-managed service
- run as a standalone script
- show how the [jupyterhub_idle_culler][] script can be:
- used in a Hub-managed service
- run as a standalone script
Both examples for `cull_idle_servers` will communicate tasks to the
Both examples for `jupyterhub_idle_culler` will communicate tasks to the
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:
@@ -78,44 +78,72 @@ 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 `cull-idle` to run as a Hub-Managed Service
## How to configure the idle culler to run as a Hub-Managed Service
In `jupyterhub_config.py`, add the following dictionary for the
`cull-idle` Service to the `c.JupyterHub.services` list:
### Step 1: Install the idle culler:
```
pip install jupyterhub-idle-culler
```
### 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 = [
{
'name': 'cull-idle',
'admin': True,
'command': [sys.executable, 'cull_idle_servers.py', '--timeout=3600'],
'name': 'idle-culler',
'command': [sys.executable, '-m', 'jupyterhub_idle_culler', '--timeout=3600'],
}
]
c.JupyterHub.load_roles = [
{
"name": "list-and-cull", # name the role
"services": [
"idle-culler", # assign the service to this role
],
"scopes": [
# declare what permissions the service should have
"list:users", # list users
"read:users:activity", # read user last-activity
"admin:servers", # start/stop servers
],
}
]
```
where:
- `'admin': True` indicates that the Service has 'admin' permissions, and
- `'command'` indicates that the Service will be launched as a
- `command` indicates that the Service will be launched as a
subprocess, managed by the Hub.
## Run `cull-idle` manually as a standalone script
```{versionchanged} 2.0
Prior to 2.0, the idle-culler required 'admin' permissions.
It now needs the scopes:
Now you can run your script, i.e. `cull_idle_servers`, by providing it
- `list:users` to access the user list endpoint
- `read:users:activity` to read activity info
- `admin:servers` to start/stop servers
```
## 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
interact with it.
This will run `cull-idle` manually. `cull-idle` can be run as a standalone
This will run the idle culler service manually. It can be run as a standalone
script anywhere with access to the Hub, and will periodically check for idle
servers and shut them down via the Hub's REST API. In order to shutdown the
servers, the token given to cull-idle must have admin privileges.
servers, the token given to `cull-idle` must have permission to list users
and admin their servers.
Generate an API token and store it in the `JUPYTERHUB_API_TOKEN` environment
variable. Run `cull_idle_servers.py` manually.
variable. Run `jupyterhub_idle_culler` manually.
```bash
export JUPYTERHUB_API_TOKEN='token'
python3 cull_idle_servers.py [--timeout=900] [--url=http://127.0.0.1:8081/hub/api]
python -m jupyterhub_idle_culler [--timeout=900] [--url=http://127.0.0.1:8081/hub/api]
```
[cull_idle_servers]: https://github.com/jupyterhub/jupyterhub/blob/master/examples/cull-idle/cull_idle_servers.py
[jupyterhub_idle_culler]: https://github.com/jupyterhub/jupyterhub-idle-culler

12
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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 aspect of that server can configure, and a lot of ways
to express that configuration.
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.
@@ -14,13 +14,13 @@ expanded to the user's home directory.
c.Spawner.notebook_dir = '~/notebooks'
```
You can also specify extra command-line arguments to the notebook server with:
You can also specify extra command line arguments to the notebook server with:
```python
c.Spawner.args = ['--debug', '--profile=PHYS131']
```
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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@@ -0,0 +1,15 @@
=====
About
=====
JupyterHub is an open source project and community. It is a part of the
`Jupyter Project <https://jupyter.org>`_. JupyterHub is an open and inclusive
community, and invites contributions from anyone. This section covers information
about our community, as well as ways that you can connect and get involved.
.. toctree::
:maxdepth: 1
contributor-list
changelog
gallery-jhub-deployments

14
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@@ -0,0 +1,14 @@
=====================
Administrator's Guide
=====================
This guide covers best-practices, tips, common questions and operations, as
well as other information relevant to running your own JupyterHub over time.
.. toctree::
:maxdepth: 2
troubleshooting
admin/upgrading
admin/log-messages
changelog

142
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View File

@@ -3,30 +3,28 @@ JupyterHub
==========
`JupyterHub`_ is the best way to serve `Jupyter notebook`_ for multiple users.
It can be used in a classes of students, a corporate data science group or scientific
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 have distributions. 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:
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`_. 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
@@ -43,7 +41,7 @@ JupyterHub performs the following functions:
notebook servers
For convenient administration of the Hub, its users, and services,
JupyterHub also provides a `REST API`_.
JupyterHub also provides a :doc:`REST API <reference/rest-api>`.
The JupyterHub team and Project Jupyter value our community, and JupyterHub
follows the Jupyter `Community Guides <https://jupyter.readthedocs.io/en/latest/community/content-community.html>`_.
@@ -56,120 +54,89 @@ 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
------------------
.. toctree::
:maxdepth: 1
:maxdepth: 2
installation-guide
quickstart
quickstart-docker
installation-basics
Getting Started
---------------
.. toctree::
:maxdepth: 1
:maxdepth: 2
getting-started/index
getting-started/config-basics
getting-started/networking-basics
getting-started/security-basics
getting-started/authenticators-users-basics
getting-started/spawners-basics
getting-started/services-basics
Technical Reference
-------------------
.. toctree::
:maxdepth: 1
:maxdepth: 2
reference/index
reference/technical-overview
reference/websecurity
reference/authenticators
reference/spawners
reference/services
reference/rest
reference/templates
reference/config-user-env
reference/config-examples
reference/config-ghoauth
reference/config-proxy
reference/config-sudo
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,
and are glad to have your contributions in whatever form you wish :)
Our `Code of Conduct <https://github.com/jupyter/governance/blob/master/conduct/code_of_conduct.md>`_
(`reporting guidelines <https://github.com/jupyter/governance/blob/master/conduct/reporting_online.md>`_)
helps keep our community welcoming to as many people as possible.
.. toctree::
:maxdepth: 1
contributing/community
contributing/setup
contributing/docs
contributing/tests
contributing/roadmap
contributing/security
Upgrading JupyterHub
Administrators guide
--------------------
We try to make upgrades between minor versions as painless as possible.
.. toctree::
:maxdepth: 1
:maxdepth: 2
admin/upgrading
changelog
index-admin
API Reference
-------------
.. toctree::
:maxdepth: 1
:maxdepth: 2
api/index
Troubleshooting
---------------
RBAC Reference
--------------
.. toctree::
:maxdepth: 1
:maxdepth: 2
troubleshooting
rbac/index
Contributing
------------
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>`_
(`reporting guidelines <https://github.com/jupyter/governance/blob/HEAD/conduct/reporting_online.md>`_)
helps keep our community welcoming to as many people as possible.
.. toctree::
:maxdepth: 2
contributing/index
About JupyterHub
----------------
.. toctree::
:maxdepth: 1
:maxdepth: 2
contributor-list
changelog
gallery-jhub-deployments
index-about
Indices and tables
==================
@@ -184,24 +151,5 @@ Questions? Suggestions?
- `Jupyter mailing list <https://groups.google.com/forum/#!forum/jupyter>`_
- `Jupyter website <https://jupyter.org>`_
.. _contents:
Full Table of Contents
======================
.. toctree::
:maxdepth: 2
installation-guide
getting-started/index
reference/index
api/index
troubleshooting
contributor-list
gallery-jhub-deployments
changelog
.. _JupyterHub: https://github.com/jupyterhub/jupyterhub
.. _Jupyter notebook: https://jupyter-notebook.readthedocs.io/en/latest/
.. _REST API: http://petstore.swagger.io/?url=https://raw.githubusercontent.com/jupyterhub/jupyterhub/master/docs/rest-api.yml#!/default

6
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@@ -0,0 +1,6 @@
:orphan:
JupyterHub the hard way
=======================
This guide has moved to https://github.com/jupyterhub/jupyterhub-the-hard-way/blob/HEAD/docs/installation-guide-hard.md

8
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@@ -1,5 +1,9 @@
Installation Guide
==================
Installation
============
These sections cover how to get up-and-running with JupyterHub. They cover
some basics of the tools needed to deploy JupyterHub as well as how to get it
running on your own infrastructure.
.. toctree::
:maxdepth: 3

72
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@@ -1,49 +1,69 @@
Using Docker
============
Install JupyterHub with Docker
==============================
.. important::
We highly recommend following the `Zero to JupyterHub`_ tutorial for
installing JupyterHub.
Alternate installation using Docker
-----------------------------------
A ready to go `docker image <https://hub.docker.com/r/jupyterhub/jupyterhub/>`_
gives a straightforward deployment of JupyterHub.
The JupyterHub `docker image <https://hub.docker.com/r/jupyterhub/jupyterhub/>`_ is the fastest way to set up Jupyterhub in your local development environment.
.. note::
This ``jupyterhub/jupyterhub`` docker image is only an image for running
the Hub service itself. It does not provide the other Jupyter components,
such as Notebook installation, which are needed by the single-user servers.
To run the single-user servers, which may be on the same system as the Hub or
not, Jupyter Notebook version 4 or greater must be installed.
not, `JupyterLab <https://jupyterlab.readthedocs.io/>`_ or Jupyter Notebook must be installed.
Starting JupyterHub with docker
-------------------------------
The JupyterHub docker image can be started with the following command::
.. important::
We strongly recommend that you follow the `Zero to JupyterHub`_ tutorial to
install JupyterHub.
Prerequisites
-------------
You should have `Docker`_ installed on a Linux/Unix based system.
Run the Docker Image
--------------------
To pull the latest JupyterHub image and start the `jupyterhub` container, run this command in your terminal.
::
docker run -d -p 8000:8000 --name jupyterhub jupyterhub/jupyterhub jupyterhub
This command will create a container named ``jupyterhub`` that you can
**stop and resume** with ``docker stop/start``.
The Hub service will be listening on all interfaces at port 8000, which makes
this a good choice for **testing JupyterHub on your desktop or laptop**.
This command exposes the Jupyter container on port:8000. Navigate to `http://localhost:8000` in a web browser to access the JupyterHub console.
You can stop and resume the container by running `docker stop` and `docker start` respectively.
::
# find the container id
docker ps
# stop the running container
docker stop <container-id>
# resume the paused container
docker start <container-id>
If you want to run docker on a computer that has a public IP then you should
(as in MUST) **secure it with ssl** by adding ssl options to your docker
configuration or using an ssl enabled proxy.
`Mounting volumes <https://docs.docker.com/engine/admin/volumes/volumes/>`_
will allow you to store data outside the docker image (host system) so it will
be persistent, even when you start a new image.
`Mounting volumes <https://docs.docker.com/engine/admin/volumes/volumes/>`_
enables you to persist and store the data generated by the docker container, even when you stop the container.
The persistent data can be stored on the host system, outside the container.
The command ``docker exec -it jupyterhub bash`` will spawn a root shell in your
docker container. You can use the root shell to **create system users in the container**.
These accounts will be used for authentication in JupyterHub's default
Create System Users
-------------------
Spawn a root shell in your docker container by running this command in the terminal.::
docker exec -it jupyterhub bash
The created accounts will be used for authentication in JupyterHub's default
configuration.
.. _Zero to JupyterHub: https://zero-to-jupyterhub.readthedocs.io/en/latest/
.. _Docker: https://www.docker.com/

40
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@@ -5,35 +5,45 @@
Before installing JupyterHub, you will need:
- a Linux/Unix based system
- [Python](https://www.python.org/downloads/) 3.5 or greater. An understanding
of using [`pip`](https://pip.pypa.io/en/stable/) or
- [Python](https://www.python.org/downloads/) 3.6 or greater. An understanding
of using [`pip`](https://pip.pypa.io) or
[`conda`](https://conda.io/docs/get-started.html) for
installing Python packages is helpful.
- [nodejs/npm](https://www.npmjs.com/). [Install nodejs/npm](https://docs.npmjs.com/getting-started/installing-node),
using your operating system's package manager.
* If you are using **`conda`**, the nodejs and npm dependencies will be installed for
- If you are using **`conda`**, the nodejs and npm dependencies will be installed for
you by conda.
* If you are using **`pip`**, install a recent version of
- If you are using **`pip`**, install a recent version of
[nodejs/npm](https://docs.npmjs.com/getting-started/installing-node).
For example, install it on Linux (Debian/Ubuntu) using:
```
sudo apt-get install npm nodejs-legacy
sudo apt-get install nodejs npm
```
The `nodejs-legacy` package installs the `node` executable and is currently
required for npm to work on Debian/Ubuntu.
[nodesource][] is a great resource to get more recent versions of the nodejs runtime,
if your system package manager only has an old version of Node.js (e.g. 10 or older).
- A [pluggable authentication module (PAM)](https://en.wikipedia.org/wiki/Pluggable_authentication_module)
to use the [default Authenticator](./getting-started/authenticators-users-basics.md).
PAM is often available by default on most distributions, if this is not the case it can be installed by
using the operating system's package manager.
- TLS certificate and key for HTTPS communication
- Domain name
[nodesource]: https://github.com/nodesource/distributions#table-of-contents
Before running the single-user notebook servers (which may be on the same
system as the Hub or not), you will need:
- [Jupyter Notebook](https://jupyter.readthedocs.io/en/latest/install.html)
version 4 or greater
- [JupyterLab][] version 3 or greater,
or [Jupyter Notebook][]
4 or greater.
[jupyterlab]: https://jupyterlab.readthedocs.io
[jupyter notebook]: https://jupyter.readthedocs.io/en/latest/install.html
## Installation
@@ -44,14 +54,14 @@ JupyterHub can be installed with `pip` (and the proxy with `npm`) or `conda`:
```bash
python3 -m pip install jupyterhub
npm install -g configurable-http-proxy
python3 -m pip install notebook # needed if running the notebook servers locally
python3 -m pip install jupyterlab notebook # needed if running the notebook servers in the same environment
```
**conda** (one command installs jupyterhub and proxy):
```bash
conda install -c conda-forge jupyterhub # installs jupyterhub and proxy
conda install notebook # needed if running the notebook servers locally
conda install jupyterlab notebook # needed if running the notebook servers in the same environment
```
Test your installation. If installed, these commands should return the packages'
@@ -70,16 +80,16 @@ To start the Hub server, run the command:
jupyterhub
```
Visit `https://localhost:8000` in your browser, and sign in with your unix
Visit `http://localhost:8000` in your browser, and sign in with your unix
credentials.
To **allow multiple users to sign in** to the Hub server, you must start
`jupyterhub` as a *privileged user*, such as root:
`jupyterhub` as a _privileged user_, such as root:
```bash
sudo jupyterhub
```
The [wiki](https://github.com/jupyterhub/jupyterhub/wiki/Using-sudo-to-run-JupyterHub-without-root-privileges)
describes how to run the server as a *less privileged user*. This requires
describes how to run the server as a _less privileged user_. This requires
additional configuration of the system.

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@@ -0,0 +1,161 @@
"""
This script updates two files with the RBAC scope descriptions found in
`scopes.py`.
The files are:
1. scope-table.md
This file is git ignored and referenced by the documentation.
2. rest-api.yml
This file is JupyterHub's REST API schema. Both a version and the RBAC
scopes descriptions are updated in it.
"""
import os
from collections import defaultdict
from pathlib import Path
from subprocess import run
from pytablewriter import MarkdownTableWriter
from ruamel.yaml import YAML
from jupyterhub import __version__
from jupyterhub.scopes import scope_definitions
HERE = os.path.abspath(os.path.dirname(__file__))
DOCS = Path(HERE).parent.parent.absolute()
REST_API_YAML = DOCS.joinpath("source", "_static", "rest-api.yml")
SCOPE_TABLE_MD = Path(HERE).joinpath("scope-table.md")
class ScopeTableGenerator:
def __init__(self):
self.scopes = scope_definitions
@classmethod
def create_writer(cls, table_name, headers, values):
writer = MarkdownTableWriter()
writer.table_name = table_name
writer.headers = headers
writer.value_matrix = values
writer.margin = 1
return writer
def _get_scope_relationships(self):
"""Returns a tuple of dictionary of all scope-subscope pairs and a list of just subscopes:
({scope: subscope}, [subscopes])
used for creating hierarchical scope table in _parse_scopes()
"""
pairs = []
for scope, data in self.scopes.items():
subscopes = data.get('subscopes')
if subscopes is not None:
for subscope in subscopes:
pairs.append((scope, subscope))
else:
pairs.append((scope, None))
subscopes = [pair[1] for pair in pairs]
pairs_dict = defaultdict(list)
for scope, subscope in pairs:
pairs_dict[scope].append(subscope)
return pairs_dict, subscopes
def _get_top_scopes(self, subscopes):
"""Returns a list of highest level scopes
(not a subscope of any other scopes)"""
top_scopes = []
for scope in self.scopes.keys():
if scope not in subscopes:
top_scopes.append(scope)
return top_scopes
def _parse_scopes(self):
"""Returns a list of table rows where row:
[indented scopename string, scope description string]"""
scope_pairs, subscopes = self._get_scope_relationships()
top_scopes = self._get_top_scopes(subscopes)
table_rows = []
md_indent = "&nbsp;&nbsp;&nbsp;"
def _add_subscopes(table_rows, scopename, depth=0):
description = self.scopes[scopename]['description']
doc_description = self.scopes[scopename].get('doc_description', '')
if doc_description:
description = doc_description
table_row = [f"{md_indent * depth}`{scopename}`", description]
table_rows.append(table_row)
for subscope in scope_pairs[scopename]:
if subscope:
_add_subscopes(table_rows, subscope, depth + 1)
for scope in top_scopes:
_add_subscopes(table_rows, scope)
return table_rows
def write_table(self):
"""Generates the RBAC scopes reference documentation as a markdown table
and writes it to the .gitignored `scope-table.md`."""
filename = SCOPE_TABLE_MD
table_name = ""
headers = ["Scope", "Grants permission to:"]
values = self._parse_scopes()
writer = self.create_writer(table_name, headers, values)
title = "Table 1. Available scopes and their hierarchy"
content = f"{title}\n{writer.dumps()}"
with open(filename, 'w') as f:
f.write(content)
print(f"Generated {filename}.")
print(
"Run 'make clean' before 'make html' to ensure the built scopes.html contains latest scope table changes."
)
def write_api(self):
"""Loads `rest-api.yml` and writes it back with a dynamically set
JupyterHub version field and list of RBAC scopes descriptions from
`scopes.py`."""
filename = REST_API_YAML
yaml = YAML(typ="rt")
yaml.preserve_quotes = True
yaml.indent(mapping=2, offset=2, sequence=4)
scope_dict = {}
with open(filename) as f:
content = yaml.load(f.read())
content["info"]["version"] = __version__
for scope in self.scopes:
description = self.scopes[scope]['description']
doc_description = self.scopes[scope].get('doc_description', '')
if doc_description:
description = doc_description
scope_dict[scope] = description
content['components']['securitySchemes']['oauth2']['flows'][
'authorizationCode'
]['scopes'] = scope_dict
with open(filename, 'w') as f:
yaml.dump(content, f)
run(
['pre-commit', 'run', 'prettier', '--files', filename],
cwd=HERE,
check=False,
)
def main():
table_generator = ScopeTableGenerator()
table_generator.write_table()
table_generator.write_api()
if __name__ == "__main__":
main()

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(RBAC)=
# JupyterHub RBAC
Role Based Access Control (RBAC) in JupyterHub serves to provide fine grained control of access to Jupyterhub's API resources.
RBAC is new in JupyterHub 2.0.
## Motivation
The JupyterHub API requires authorization to access its APIs.
This ensures that an arbitrary user, or even an unauthenticated third party, are not allowed to perform such actions.
For instance, the behaviour prior to adoption of RBAC is that creating or deleting users requires _admin rights_.
The prior system is functional, but lacks flexibility. If your Hub serves a number of users in different groups, you might want to delegate permissions to other users or automate certain processes.
Prior to RBAC, appointing a 'group-only admin' or a bot that culls idle servers, requires granting full admin rights to all actions. This poses a risk of the user or service intentionally or unintentionally accessing and modifying any data within the Hub and violates the [principle of least privilege](https://en.wikipedia.org/wiki/Principle_of_least_privilege).
To remedy situations like this, JupyterHub is transitioning to an RBAC system. By equipping users, groups and services with _roles_ that supply them with a collection of permissions (_scopes_), administrators are able to fine-tune which parties are granted access to which resources.
## Definitions
**Scopes** are specific permissions used to evaluate API requests. For example: the API endpoint `users/servers`, which enables starting or stopping user servers, is guarded by the scope `servers`.
Scopes are not directly assigned to requesters. Rather, when a client performs an API call, their access will be evaluated based on their assigned roles.
**Roles** are collections of scopes that specify the level of what a client is allowed to do. For example, a group administrator may be granted permission to control the servers of group members, but not to create, modify or delete group members themselves.
Within the RBAC framework, this is achieved by assigning a role to the administrator that covers exactly those privileges.
## Technical Overview
```{toctree}
:maxdepth: 2
roles
scopes
use-cases
tech-implementation
upgrade
```

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# Roles
JupyterHub provides four (4) roles that are available by default:
```{admonition} **Default roles**
- `user` role provides a {ref}`default user scope <default-user-scope-target>` `self` that grants access to the user's own resources.
- `admin` role contains all available scopes and grants full rights to all actions. This role **cannot be edited**.
- `token` role provides a {ref}`default token scope <default-token-scope-target>` `inherit` that resolves to the same permissions as the owner of the token has.
- `server` role allows for posting activity of "itself" only.
**These roles cannot be deleted.**
```
We call these 'default' roles because they are available by default and have a default collection of scopes.
However, you can define the scopes associated with each role (excluding the admin role) to suit your needs,
as seen [below](overriding-default-roles).
The `user`, `admin`, and `token` roles, by default, all preserve the permissions prior to Role-based Access Control (RBAC).
Only the `server` role is changed from pre-2.0, to reduce its permissions to activity-only
instead of the default of a full access token.
Additional custom roles can also be defined (see {ref}`define-role-target`).
Roles can be assigned to the following entities:
- Users
- Services
- Groups
An entity can have zero, one, or multiple roles, and there are no restrictions on which roles can be assigned to which entity. Roles can be added to or removed from entities at any time.
**Users** \
When a new user gets created, they are assigned their default role, `user`. Additionally, if the user is created with admin privileges (via `c.Authenticator.admin_users` in `jupyterhub_config.py` or `admin: true` via API), they will be also granted `admin` role. If existing user's admin status changes via API or `jupyterhub_config.py`, their default role will be updated accordingly (after next startup for the latter).
**Services** \
Services do not have a default role. Services without roles have no access to the guarded API end-points. So, most services will require assignment of a role in order to function.
**Groups** \
A group does not require any role, and has no roles by default. If a user is a member of a group, they automatically inherit any of the group's permissions (see {ref}`resolving-roles-scopes-target` for more details). This is useful for assigning a set of common permissions to several users.
**Tokens** \
A token’s permissions are evaluated based on their owning entity. Since a token is always issued for a user or service, it can never have more permissions than its owner. If no specific scopes are requested for a new token, the token is assigned the scopes of the `token` role.
(define-role-target)=
## Defining Roles
Roles can be defined or modified in the configuration file as a list of dictionaries. An example:
% TODO: think about loading users into roles if membership has been changed via API.
% What should be the result?
```python
# in jupyterhub_config.py
c.JupyterHub.load_roles = [
 {
   'name': 'server-rights',
   'description': 'Allows parties to start and stop user servers',
   'scopes': ['servers'],
   'users': ['alice', 'bob'],
   'services': ['idle-culler'],
   'groups': ['admin-group'],
 }
]
```
The role `server-rights` now allows the starting and stopping of servers by any of the following:
- users `alice` and `bob`
- the service `idle-culler`
- any member of the `admin-group`.
```{attention}
Tokens cannot be assigned roles through role definition but may be assigned specific roles when requested via API (see {ref}`requesting-api-token-target`).
```
Another example:
```python
# in jupyterhub_config.py
c.JupyterHub.load_roles = [
{
'description': 'Read-only user models',
'name': 'reader',
'scopes': ['read:users'],
'services': ['external'],
'users': ['maria', 'joe']
}
]
```
The role `reader` allows users `maria` and `joe` and service `external` to read (but not modify) any user’s model.
```{admonition} Requirements
:class: warning
In a role definition, the `name` field is required, while all other fields are optional.\
**Role names must:**
- be 3 - 255 characters
- use ascii lowercase, numbers, 'unreserved' URL punctuation `-_.~`
- start with a letter
- end with letter or number.
`users`, `services`, and `groups` only accept objects that already exist in the database or are defined previously in the file.
It is not possible to implicitly add a new user to the database by defining a new role.
```
If no scopes are defined for _new role_, JupyterHub will raise a warning. Providing non-existing scopes will result in an error.
In case the role with a certain name already exists in the database, its definition and scopes will be overwritten. This holds true for all roles except the `admin` role, which cannot be overwritten; an error will be raised if trying to do so. All the role bearers permissions present in the definition will change accordingly.
(overriding-default-roles)=
### Overriding Default Roles
Role definitions can include those of the "default" roles listed above (admin excluded),
if the default scopes associated with those roles do not suit your deployment.
For example, to specify what permissions the $JUPYTERHUB_API_TOKEN issued to all single-user servers
has,
define the `server` role.
To restore the JupyterHub 1.x behavior of servers being able to do anything their owners can do,
use the scope `inherit` (for 'inheriting' the owner's permissions):
```python
c.JupyterHub.load_roles = [
{
'name': 'server',
'scopes': ['inherit'],
}
]
```
or, better yet, identify the specific [scopes][] you want server environments to have access to.
[scopes]: available-scopes-target
If you don't want to get too detailed,
one option is the `self` scope,
which will have no effect on non-admin users,
but will restrict the token issued to admin user servers to only have access to their own resources,
instead of being able to take actions on behalf of all other users.
```python
c.JupyterHub.load_roles = [
{
'name': 'server',
'scopes': ['self'],
}
]
```
(removing-roles-target)=
## Removing Roles
Only the entities present in the role definition in the `jupyterhub_config.py` remain the role bearers. If a user, service or group is removed from the role definition, they will lose the role on the next startup.
Once a role is loaded, it remains in the database until removing it from the `jupyterhub_config.py` and restarting the Hub. All previously defined role bearers will lose the role and associated permissions. Default roles, even if previously redefined through the config file and removed, will not be deleted from the database.

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# Scopes in JupyterHub
A scope has a syntax-based design that reveals which resources it provides access to. Resources are objects with a type, associated data, relationships to other resources, and a set of methods that operate on them (see [RESTful API](https://restful-api-design.readthedocs.io/en/latest/resources.html) documentation for more information).
`<resource>` in the RBAC scope design refers to the resource name in the [JupyterHub's API](../reference/rest-api.rst) endpoints in most cases. For instance, `<resource>` equal to `users` corresponds to JupyterHub's API endpoints beginning with _/users_.
(scope-conventions-target)=
## Scope conventions
- `<resource>` \
The top-level `<resource>` scopes, such as `users` or `groups`, grant read, write, and list permissions to the resource itself as well as its sub-resources. For example, the scope `users:activity` is included in the scope `users`.
- `read:<resource>` \
Limits permissions to read-only operations on single resources.
- `list:<resource>` \
Read-only access to listing endpoints.
Use `read:<resource>:<subresource>` to control what fields are returned.
- `admin:<resource>` \
Grants additional permissions such as create/delete on the corresponding resource in addition to read and write permissions.
- `access:<resource>` \
Grants access permissions to the `<resource>` via API or browser.
- `<resource>:<subresource>` \
The {ref}`vertically filtered <vertical-filtering-target>` scopes provide access to a subset of the information granted by the `<resource>` scope. E.g., the scope `users:activity` only provides permission to post user activity.
- `<resource>!<object>=<objectname>` \
{ref}`horizontal-filtering-target` is implemented by the `!<object>=<objectname>`scope structure. A resource (or sub-resource) can be filtered based on `user`, `server`, `group` or `service` name. For instance, `<resource>!user=charlie` limits access to only return resources of user `charlie`. \
Only one filter per scope is allowed, but filters for the same scope have an additive effect; a larger filter can be used by supplying the scope multiple times with different filters.
By adding a scope to an existing role, all role bearers will gain the associated permissions.
## Metascopes
Metascopes do not follow the general scope syntax. Instead, a metascope resolves to a set of scopes, which can refer to different resources, based on their owning entity. In JupyterHub, there are currently two metascopes:
1. default user scope `self`, and
2. default token scope `inherit`.
(default-user-scope-target)=
### Default user scope
Access to the user's own resources and subresources is covered by metascope `self`. This metascope includes the user's model, activity, servers and tokens. For example, `self` for a user named "gerard" includes:
- `users!user=gerard` where the `users` scope provides access to the full user model and activity. The filter restricts this access to the user's own resources.
- `servers!user=gerard` which grants the user access to their own servers without being able to create/delete any.
- `tokens!user=gerard` which allows the user to access, request and delete their own tokens.
- `access:servers!user=gerard` which allows the user to access their own servers via API or browser.
The `self` scope is only valid for user entities. In other cases (e.g., for services) it resolves to an empty set of scopes.
(default-token-scope-target)=
### Default token scope
The token metascope `inherit` causes the token to have the same permissions as the token's owner. For example, if a token owner has roles containing the scopes `read:groups` and `read:users`, the `inherit` scope resolves to the set of scopes `{read:groups, read:users}`.
If the token owner has default `user` role, the `inherit` scope resolves to `self`, which will subsequently be expanded to include all the user-specific scopes (or empty set in the case of services).
If the token owner is a member of any group with roles, the group scopes will also be included in resolving the `inherit` scope.
(horizontal-filtering-target)=
## Horizontal filtering
Horizontal filtering, also called _resource filtering_, is the concept of reducing the payload of an API call to cover only the subset of the _resources_ that the scopes of the client provides them access to.
Requested resources are filtered based on the filter of the corresponding scope. For instance, if a service requests a user list (guarded with scope `read:users`) with a role that only contains scopes `read:users!user=hannah` and `read:users!user=ivan`, the returned list of user models will be an intersection of all users and the collection `{hannah, ivan}`. In case this intersection is empty, the API call returns an HTTP 404 error, regardless if any users exist outside of the clients scope filter collection.
In case a user resource is being accessed, any scopes with _group_ filters will be expanded to filters for each _user_ in those groups.
(self-referencing-filters)=
### Self-referencing filters
There are some 'shortcut' filters,
which can be applied to all scopes,
that filter based on the entities associated with the request.
The `!user` filter is a special horizontal filter that strictly refers to the **"owner only"** scopes, where _owner_ is a user entity. The filter resolves internally into `!user=<ownerusername>` ensuring that only the owner's resources may be accessed through the associated scopes.
For example, the `server` role assigned by default to server tokens contains `access:servers!user` and `users:activity!user` scopes. This allows the token to access and post activity of only the servers owned by the token owner.
:::{versionadded} 3.0
`!service` and `!server` filters.
:::
In addition to `!user`, _tokens_ may have filters `!service`
or `!server`, which expand similarly to `!service=servicename`
and `!server=servername`.
This only applies to tokens issued via the OAuth flow.
In these cases, the name is the _issuing_ entity (a service or single-user server),
so that access can be restricted to the issuing service,
e.g. `access:servers!server` would grant access only to the server that requested the token.
These filters can be applied to any scope.
(vertical-filtering-target)=
## Vertical filtering
Vertical filtering, also called _attribute filtering_, is the concept of reducing the payload of an API call to cover only the _attributes_ of the resources that the scopes of the client provides them access to. This occurs when the client scopes are subscopes of the API endpoint that is called.
For instance, if a client requests a user list with the only scope being `read:users:groups`, the returned list of user models will contain only a list of groups per user.
In case the client has multiple subscopes, the call returns the union of the data the client has access to.
The payload of an API call can be filtered both horizontally and vertically simultaneously. For instance, performing an API call to the endpoint `/users/` with the scope `users:name!user=juliette` returns a payload of `[{name: 'juliette'}]` (provided that this name is present in the database).
(available-scopes-target)=
## Available scopes
Table below lists all available scopes and illustrates their hierarchy. Indented scopes indicate subscopes of the scope(s) above them.
There are four exceptions to the general {ref}`scope conventions <scope-conventions-target>`:
- `read:users:name` is a subscope of both `read:users` and `read:servers`. \
The `read:servers` scope requires access to the user name (server owner) due to named servers distinguished internally in the form `!server=username/servername`.
- `read:users:activity` is a subscope of both `read:users` and `users:activity`. \
Posting activity via the `users:activity`, which is not included in `users` scope, needs to check the last valid activity of the user.
- `read:roles:users` is a subscope of both `read:roles` and `admin:users`. \
Admin privileges to the _users_ resource include the information about user roles.
- `read:roles:groups` is a subscope of both `read:roles` and `admin:groups`. \
Similar to the `read:roles:users` above.
```{include} scope-table.md
```
:::{versionadded} 3.0
The `admin-ui` scope is added to explicitly grant access to the admin page,
rather than combining `admin:users` and `admin:servers` permissions.
This means a deployment can enable the admin page with only a subset of functionality enabled.
Note that this means actions to take _via_ the admin UI
and access _to_ the admin UI are separated.
For example, it generally doesn't make sense to grant
`admin-ui` without at least `list:users` for at least some subset of users.
For example:
```python
c.JupyterHub.load_roles = [
{
"name": "instructor-data8",
"scopes": [
# access to the admin page
"admin-ui",
# list users in the class group
"list:users!group=students-data8",
# start/stop servers for users in the class
"admin:servers!group=students-data8",
# access servers for users in the class
"access:servers!group=students-data8",
],
"group": ["instructors-data8"],
}
]
```
will grant instructors in the data8 course permission to:
1. view the admin UI
2. see students in the class (but not all users)
3. start/stop/access servers for users in the class
4. but _not_ permission to administer the users themselves (e.g. change their permissions, etc.)
:::
```{Caution}
Note that only the {ref}`horizontal filtering <horizontal-filtering-target>` can be added to scopes to customize them. \
Metascopes `self` and `all`, `<resource>`, `<resource>:<subresource>`, `read:<resource>`, `admin:<resource>`, and `access:<resource>` scopes are predefined and cannot be changed otherwise.
```
(custom-scopes)=
### Custom scopes
:::{versionadded} 3.0
:::
JupyterHub 3.0 introduces support for custom scopes.
Services that use JupyterHub for authentication and want to implement their own granular access may define additional _custom_ scopes and assign them to users with JupyterHub roles.
% Note: keep in sync with pattern/description in jupyterhub/scopes.py
Custom scope names must start with `custom:`
and contain only lowercase ascii letters, numbers, hyphen, underscore, colon, and asterisk (`-_:*`).
The part after `custom:` must start with a letter or number.
Scopes may not end with a hyphen or colon.
The only strict requirement is that a custom scope definition must have a `description`.
It _may_ also have `subscopes` if you are defining multiple scopes that have a natural hierarchy,
For example:
```python
c.JupyterHub.custom_scopes = {
"custom:myservice:read": {
"description": "read-only access to myservice",
},
"custom:myservice:write": {
"description": "write access to myservice",
# write permission implies read permission
"subscopes": [
"custom:myservice:read",
],
},
}
c.JupyterHub.load_roles = [
# graders have read-only access to the service
{
"name": "service-user",
"groups": ["graders"],
"scopes": [
"custom:myservice:read",
"access:service!service=myservice",
],
},
# instructors have read and write access to the service
{
"name": "service-admin",
"groups": ["instructors"],
"scopes": [
"custom:myservice:write",
"access:service!service=myservice",
],
},
]
```
In the above configuration, two scopes are defined:
- `custom:myservice:read` grants read-only access to the service, and
- `custom:myservice:write` grants write access to the service
- write access _implies_ read access via the `subscope`
These custom scopes are assigned to two groups via `roles`:
- users in the group `graders` are granted read access to the service
- users in the group `instructors` are
- both are granted _access_ to the service via `access:service!service=myservice`
When the service completes OAuth, it will retrieve the user model from `/hub/api/user`.
This model includes a `scopes` field which is a list of authorized scope for the request,
which can be used.
```python
def require_scope(scope):
"""decorator to require a scope to perform an action"""
def wrapper(func):
@functools.wraps(func)
def wrapped_func(request):
user = fetch_hub_api_user(request.token)
if scope not in user["scopes"]:
raise HTTP403(f"Requires scope {scope}")
else:
return func()
return wrapper
@require_scope("custom:myservice:read")
async def read_something(request):
...
@require_scope("custom:myservice:write")
async def write_something(request):
...
```
If you use {class}`~.HubOAuthenticated`, this check is performed automatically
against the `.hub_scopes` attribute of each Handler
(the default is populated from `$JUPYTERHUB_OAUTH_ACCESS_SCOPES` and usually `access:services!service=myservice`).
:::{versionchanged} 3.0
The JUPYTERHUB_OAUTH_SCOPES environment variable is deprecated and renamed to JUPYTERHUB_OAUTH_ACCESS_SCOPES,
to avoid ambiguity with JUPYTERHUB_OAUTH_CLIENT_ALLOWED_SCOPES
:::
```python
from tornado import web
from jupyterhub.services.auth import HubOAuthenticated
class MyHandler(HubOAuthenticated, BaseHandler):
hub_scopes = ["custom:myservice:read"]
@web.authenticated
def get(self):
...
```
Existing scope filters (`!user=`, etc.) may be applied to custom scopes.
Custom scope _filters_ are NOT supported.
### Scopes and APIs
The scopes are also listed in the [](../reference/rest-api.rst) documentation. Each API endpoint has a list of scopes which can be used to access the API; if no scopes are listed, the API is not authenticated and can be accessed without any permissions (i.e., no scopes).
Listed scopes by each API endpoint reflect the "lowest" permissions required to gain any access to the corresponding API. For example, posting user's activity (_POST /users/:name/activity_) needs `users:activity` scope. If scope `users` is passed during the request, the access will be granted as the required scope is a subscope of the `users` scope. If, on the other hand, `read:users:activity` scope is passed, the access will be denied.

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# Technical Implementation
[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 that are reflected throughout the utilities via specific nomenclature:
```{admonition} **Scope variable nomenclature**
:class: tip
- _scopes_ \
List of scopes that may contain abbreviations (used in role definitions). E.g., `["users:activity!user", "self"]`.
- _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 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 identity (whoami) endpoints.
```
(resolving-roles-scopes-target)=
## Resolving roles and 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 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 when making an API request. The following sections provide more details.
(requesting-api-token-target)=
### Requesting API token with specific scopes
:::{versionchanged} 3.0
API tokens have _scopes_ instead of roles,
so that their permissions cannot be updated.
You may still request roles for a token,
but those roles will be evaluated to the corresponding _scopes_ immediately.
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](./index.md) allows for requesting 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
(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 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 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.
```{figure} ../images/rbac-token-request-chart.png
:align: center
:name: token-request-chart
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 in order to gain the access to the API.
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:
- if the API scopes are present within the set of passed scopes, the access is granted and the API returns its "full" response
- 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 not found, the access to API is denied
{ref}`Figure 2 <api-request-chart>` illustrates this process highlighting the steps where the role and scope resolutions as well as filtering occur in orange.
```{figure} ../images/rbac-api-request-chart.png
:align: center
:name: api-request-chart
Figure 2. Resolving roles and scopes when an API request is made
```

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@@ -0,0 +1,54 @@
# Upgrading JupyterHub with RBAC framework
RBAC framework requires different database setup than any previous JupyterHub versions due to eliminating the distinction between OAuth and API tokens (see {ref}`oauth-vs-api-tokens-target` for more details). This requires merging the previously two different database tables into one. By doing so, all existing tokens created before the upgrade no longer comply with the new database version and must be replaced.
This is achieved by the Hub deleting all existing tokens during the database upgrade and recreating the tokens loaded via the `jupyterhub_config.py` file with updated structure. However, any manually issued or stored tokens are not recreated automatically and must be manually re-issued after the upgrade.
No other database records are affected.
(rbac-upgrade-steps-target)=
## Upgrade steps
1. All running **servers must be stopped** before proceeding with the upgrade.
2. To upgrade the Hub, follow the [Upgrading JupyterHub](../admin/upgrading.rst) instructions.
```{attention}
We advise against defining any new roles in the `jupyterhub.config.py` file right after the upgrade is completed and JupyterHub restarted for the first time. This preserves the 'current' state of the Hub. You can define and assign new roles on any other following startup.
```
3. After restarting the Hub **re-issue all tokens that were previously issued manually** (i.e., not through the `jupyterhub_config.py` file).
When the JupyterHub is restarted for the first time after the upgrade, all users, services and tokens stored in the database or re-loaded through the configuration file will be assigned their default role. Any newly added entities after that will be assigned their default role only if no other specific role is requested for them.
## Changing the permissions after the upgrade
Once all the {ref}`upgrade steps <rbac-upgrade-steps-target>` above are completed, the RBAC framework will be available for utilization. You can define new roles, modify default roles (apart from `admin`) and assign them to entities as described in the {ref}`define-role-target` section.
We recommended the following procedure to start with RBAC:
1. Identify which admin users and services you would like to grant only the permissions they need through the new roles.
2. Strip these users and services of their admin status via API or UI. This will change their roles from `admin` to `user`.
```{note}
Stripping entities of their roles is currently available only via `jupyterhub_config.py` (see {ref}`removing-roles-target`).
```
3. Define new roles that you would like to start using with appropriate scopes and assign them to these entities in `jupyterhub_config.py`.
4. Restart the JupyterHub for the new roles to take effect.
(oauth-vs-api-tokens-target)=
## OAuth vs API tokens
### Before RBAC
Previous JupyterHub versions utilize two types of tokens, OAuth token and API token.
OAuth token is issued by the Hub to a single-user server when the user logs in. The token is stored in the browser cookie and is used to identify the user who owns the server during the OAuth flow. This token by default expires when the cookie reaches its expiry time of 2 weeks (or after 1 hour in JupyterHub versions < 1.3.0).
API token is issued by the Hub to a single-user server when launched and is used to communicate with the Hub's APIs such as posting activity or completing the OAuth flow. This token has no expiry by default.
API tokens can also be issued to users via API ([_/hub/token_](../reference/urls.md) or [_POST /users/:username/tokens_](../reference/rest-api.rst)) and services via `jupyterhub_config.py` to perform API requests.
### With RBAC
The RBAC framework allows for granting tokens different levels of permissions via scopes attached to roles. The 'only identify' purpose of the separate OAuth tokens is no longer required. API tokens can be used for every action, including the login and authentication, for which an API token with no role (i.e., no scope in {ref}`available-scopes-target`) is used.
OAuth tokens are therefore dropped from the Hub upgraded with the RBAC framework.

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# Use Cases
To determine which scopes a role should have, one can follow these steps:
1. Determine what actions the role holder should have/have not access to
2. Match the actions against the [JupyterHub's APIs](../reference/rest-api.rst)
3. Check which scopes are required to access the APIs
4. Combine scopes and subscopes if applicable
5. Customize the scopes with filters if needed
6. Define the role with required scopes and assign to users/services/groups/tokens
Below, different use cases are presented on how to use the [RBAC framework](./index.md)
## Service to cull idle servers
Finding and shutting down idle servers can save a lot of computational resources.
**We can make use of [jupyterhub-idle-culler](https://github.com/jupyterhub/jupyterhub-idle-culler) to manage this for us.**
Below follows a short tutorial on how to add a cull-idle service in the RBAC system.
1. Install the cull-idle server script with `pip install jupyterhub-idle-culler`.
2. Define a new service `idle-culler` and a new role for this service:
```python
# in jupyterhub_config.py
c.JupyterHub.services = [
{
"name": "idle-culler",
"command": [
sys.executable, "-m",
"jupyterhub_idle_culler",
"--timeout=3600"
],
}
]
c.JupyterHub.load_roles = [
{
"name": "idle-culler",
"description": "Culls idle servers",
"scopes": ["read:users:name", "read:users:activity", "servers"],
"services": ["idle-culler"],
}
]
```
```{important}
Note that in the RBAC system the `admin` field in the `idle-culler` service definition is omitted. Instead, the `idle-culler` role provides the service with only the permissions it needs.
If the optional actions of deleting the idle servers and/or removing inactive users are desired, **change the following scopes** in the `idle-culler` role definition:
- `servers` to `admin:servers` for deleting servers
- `read:users:name`, `read:users:activity` to `admin:users` for deleting users.
```
3. Restart JupyterHub to complete the process.
## API launcher
A service capable of creating/removing users and launching multiple servers should have access to:
1. _POST_ and _DELETE /users_
2. _POST_ and _DELETE /users/:name/server_ or _/users/:name/servers/:server_name_
3. Creating/deleting servers
The scopes required to access the API enpoints:
1. `admin:users`
2. `servers`
3. `admin:servers`
From the above, the role definition is:
```python
# in jupyterhub_config.py
c.JupyterHub.load_roles = [
{
"name": "api-launcher",
"description": "Manages servers",
"scopes": ["admin:users", "admin:servers"],
"services": [<service_name>]
}
]
```
If needed, the scopes can be modified to limit the permissions to e.g. a particular group with `!group=groupname` filter.
## Group admin roles
Roles can be used to specify different group member privileges.
For example, a group of students `class-A` may have a role allowing all group members to access information about their group. Teacher `johan`, who is a student of `class-A` but a teacher of another group of students `class-B`, can have additional role permitting him to access information about `class-B` students as well as start/stop their servers.
The roles can then be defined as follows:
```python
# in jupyterhub_config.py
c.JupyterHub.load_groups = {
'class-A': ['johan', 'student1', 'student2'],
'class-B': ['student3', 'student4']
}
c.JupyterHub.load_roles = [
{
   'name': 'class-A-student',
   'description': 'Grants access to information about the group',
   'scopes': ['read:groups!group=class-A'],
   'groups': ['class-A']
},
{
   'name': 'class-B-student',
   'description': 'Grants access to information about the group',
   'scopes': ['read:groups!group=class-B'],
   'groups': ['class-B']
},
{
   'name': 'teacher',
   'description': 'Allows for accessing information about teacher group members and starting/stopping their servers',
   'scopes': [ 'read:users!group=class-B', 'servers!group=class-B'],
   'users': ['johan']
}
]
```
In the above example, `johan` has privileges inherited from `class-A-student` role and the `teacher` role on top of those.
```{note}
The scope filters (`!group=`) limit the privileges only to the particular groups. `johan` can access the servers and information of `class-B` group members only.
```

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@@ -0,0 +1,128 @@
(api-only)=
# Deploying JupyterHub in "API only mode"
As a service for deploying and managing Jupyter servers for users, JupyterHub
exposes this functionality _primarily_ via a [REST API](rest).
For convenience, JupyterHub also ships with a _basic_ web UI built using that REST API.
The basic web UI enables users to click a button to quickly start and stop their servers,
and it lets admins perform some basic user and server management tasks.
The REST API has always provided additional functionality beyond what is available in the basic web UI.
Similarly, we avoid implementing UI functionality that is also not available via the API.
With JupyterHub 2.0, the basic web UI will **always** be composed using the REST API.
In other words, no UI pages should rely on information not available via the REST API.
Previously, some admin UI functionality could only be achieved via admin pages,
such as paginated requests.
## Limited UI customization via templates
The JupyterHub UI is customizable via extensible HTML [templates](templates),
but this has some limited scope to what can be customized.
Adding some content and messages to existing pages is well supported,
but changing the page flow and what pages are available are beyond the scope of what is customizable.
## Rich UI customization with REST API based apps
Increasingly, JupyterHub is used purely as an API for managing Jupyter servers
for other Jupyter-based applications that might want to present a different user experience.
If you want a fully customized user experience,
you can now disable the Hub UI and use your own pages together with the JupyterHub REST API
to build your own web application to serve your users,
relying on the Hub only as an API for managing users and servers.
One example of such an application is [BinderHub][], which powers https://mybinder.org,
and motivates many of these changes.
BinderHub is distinct from a traditional JupyterHub deployment
because it uses temporary users created for each launch.
Instead of presenting a login page,
users are presented with a form to specify what environment they would like to launch:
![Binder launch form](../images/binderhub-form.png)
When a launch is requested:
1. an image is built, if necessary
2. a temporary user is created,
3. a server is launched for that user, and
4. when running, users are redirected to an already running server with an auth token in the URL
5. after the session is over, the user is deleted
This means that a lot of JupyterHub's UI flow doesn't make sense:
- there is no way for users to login
- the human user doesn't map onto a JupyterHub `User` in a meaningful way
- when a server isn't running, there isn't a 'restart your server' action available because the user has been deleted
- users do not have any access to any Hub functionality, so presenting pages for those features would be confusing
BinderHub is one of the motivating use cases for JupyterHub supporting being used _only_ via its API.
We'll use BinderHub here as an example of various configuration options.
[binderhub]: https://binderhub.readthedocs.io
## Disabling Hub UI
`c.JupyterHub.hub_routespec` is a configuration option to specify which URL prefix should be routed to the Hub.
The default is `/` which means that the Hub will receive all requests not already specified to be routed somewhere else.
There are three values that are most logical for `hub_routespec`:
- `/` - this is the default, and used in most deployments.
It is also the only option prior to JupyterHub 1.4.
- `/hub/` - this serves only Hub pages, both UI and API
- `/hub/api` - this serves _only the Hub API_, so all Hub UI is disabled,
aside from the OAuth confirmation page, if used.
If you choose a hub routespec other than `/`,
the main JupyterHub feature you will lose is the automatic handling of requests for `/user/:username`
when the requested server is not running.
JupyterHub's handling of this request shows this page,
telling you that the server is not running,
with a button to launch it again:
![screenshot of hub page for server not running](../images/server-not-running.png)
If you set `hub_routespec` to something other than `/`,
it is likely that you also want to register another destination for `/` to handle requests to not-running servers.
If you don't, you will see a default 404 page from the proxy:
![screenshot of CHP default 404](../images/chp-404.png)
For mybinder.org, the default "start my server" page doesn't make sense,
because when a server is gone, there is no restart action.
Instead, we provide hints about how to get back to a link to start a _new_ server:
![screenshot of mybinder.org 404](../images/binder-404.png)
To achieve this, mybinder.org registers a route for `/` that goes to a custom endpoint
that runs nginx and only serves this static HTML error page.
This is set with
```python
c.Proxy.extra_routes = {
"/": "http://custom-404-entpoint/",
}
```
You may want to use an alternate behavior, such as redirecting to a landing page,
or taking some other action based on the requested page.
If you use `c.JupyterHub.hub_routespec = "/hub/"`,
then all the Hub pages will be available,
and only this default-page-404 issue will come up.
If you use `c.JupyterHub.hub_routespec = "/hub/api/"`,
then only the Hub _API_ will be available,
and all UI will be up to you.
mybinder.org takes this last option,
because none of the Hub UI pages really make sense.
Binder users don't have any reason to know or care that JupyterHub happens
to be an implementation detail of how their environment is managed.
Seeing Hub error pages and messages in that situation is more likely to be confusing than helpful.
:::{versionadded} 1.4
`c.JupyterHub.hub_routespec` and `c.Proxy.extra_routes` are new in JupyterHub 1.4.
:::

100
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@@ -1,6 +1,6 @@
# Authenticators
The [Authenticator][] is the mechanism for authorizing users to use the
The {class}`.Authenticator` is the mechanism for authorizing users to use the
Hub and single user notebook servers.
## The default PAM Authenticator
@@ -36,7 +36,7 @@ A [generic implementation](https://github.com/jupyterhub/oauthenticator/blob/mas
## The Dummy Authenticator
When testing, it may be helpful to use the
:class:`~jupyterhub.auth.DummyAuthenticator`. This allows for any username and
{class}`jupyterhub.auth.DummyAuthenticator`. This allows for any username and
password unless if a global password has been set. Once set, any username will
still be accepted but the correct password will need to be provided.
@@ -88,7 +88,6 @@ class DictionaryAuthenticator(Authenticator):
return data['username']
```
#### Normalize usernames
Since the Authenticator and Spawner both use the same username,
@@ -111,11 +110,8 @@ normalize usernames using PAM (basically round-tripping them: username
to uid to username), which is useful in case you use some external
service that allows multiple usernames mapping to the same user (such
as ActiveDirectory, yes, this really happens). When
`pam_normalize_username` is on, usernames are *not* normalized to
`pam_normalize_username` is on, usernames are _not_ normalized to
lowercase.
NOTE: Earlier it says that usernames are normalized using PAM.
I guess that doesn't normalize them?
#### Validate usernames
@@ -133,7 +129,6 @@ To only allow usernames that start with 'w':
c.Authenticator.username_pattern = r'w.*'
```
### How to write a custom authenticator
You can use custom Authenticator subclasses to enable authentication
@@ -141,12 +136,11 @@ via other mechanisms. One such example is using [GitHub OAuth][].
Because the username is passed from the Authenticator to the Spawner,
a custom Authenticator and Spawner are often used together.
For example, the Authenticator methods, [pre_spawn_start(user, spawner)][]
and [post_spawn_stop(user, spawner)][], are hooks that can be used to do
For example, the Authenticator methods, {meth}`.Authenticator.pre_spawn_start`
and {meth}`.Authenticator.post_spawn_stop`, are hooks that can be used to do
auth-related startup (e.g. opening PAM sessions) and cleanup
(e.g. closing PAM sessions).
See a list of custom Authenticators [on the wiki](https://github.com/jupyterhub/jupyterhub/wiki/Authenticators).
If you are interested in writing a custom authenticator, you can read
@@ -187,7 +181,6 @@ Additionally, configurable attributes for your authenticator will
appear in jupyterhub help output and auto-generated configuration files
via `jupyterhub --generate-config`.
### Authentication state
JupyterHub 0.8 adds the ability to persist state related to authentication,
@@ -221,25 +214,22 @@ To store auth_state, two conditions must be met:
export JUPYTERHUB_CRYPT_KEY=$(openssl rand -hex 32)
```
JupyterHub uses [Fernet](https://cryptography.io/en/latest/fernet/) to encrypt auth_state.
To facilitate key-rotation, `JUPYTERHUB_CRYPT_KEY` may be a semicolon-separated list of encryption keys.
If there are multiple keys present, the **first** key is always used to persist any new auth_state.
#### Using auth_state
Typically, if `auth_state` is persisted it is desirable to affect the Spawner environment in some way.
This may mean defining environment variables, placing certificate in the user's home directory, etc.
The `Authenticator.pre_spawn_start` method can be used to pass information from authenticator state
The {meth}`Authenticator.pre_spawn_start` method can be used to pass information from authenticator state
to Spawner environment:
```python
class MyAuthenticator(Authenticator):
@gen.coroutine
def authenticate(self, handler, data=None):
username = yield identify_user(handler, data)
upstream_token = yield token_for_user(username)
async def authenticate(self, handler, data=None):
username = await identify_user(handler, data)
upstream_token = await token_for_user(username)
return {
'name': username,
'auth_state': {
@@ -247,21 +237,69 @@ class MyAuthenticator(Authenticator):
},
}
@gen.coroutine
def pre_spawn_start(self, user, spawner):
async def pre_spawn_start(self, user, spawner):
"""Pass upstream_token to spawner via environment variable"""
auth_state = yield user.get_auth_state()
auth_state = await user.get_auth_state()
if not auth_state:
# auth_state not enabled
return
spawner.environment['UPSTREAM_TOKEN'] = auth_state['upstream_token']
```
Note that environment variable names and values are always strings, so passing multiple values means setting multiple environment variables or serializing more complex data into a single variable, e.g. as a JSON string.
auth state can also be used to configure the spawner via _config_ without subclassing
by setting `c.Spawner.auth_state_hook`. This function will be called with `(spawner, auth_state)`,
only when auth_state is defined.
For example:
(for KubeSpawner)
```python
def auth_state_hook(spawner, auth_state):
spawner.volumes = auth_state['user_volumes']
spawner.mounts = auth_state['user_mounts']
c.Spawner.auth_state_hook = auth_state_hook
```
(authenticator-groups)=
## Authenticator-managed group membership
:::{versionadded} 2.2
:::
Some identity providers may have their own concept of group membership that you would like to preserve in JupyterHub.
This is now possible with `Authenticator.managed_groups`.
You can set the config:
```python
c.Authenticator.manage_groups = True
```
to enable this behavior.
The default is False for Authenticators that ship with JupyterHub,
but may be True for custom Authenticators.
Check your Authenticator's documentation for manage_groups support.
If True, {meth}`.Authenticator.authenticate` and {meth}`.Authenticator.refresh_user` may include a field `groups`
which is a list of group names the user should be a member of:
- Membership will be added for any group in the list
- Membership in any groups not in the list will be revoked
- Any groups not already present in the database will be created
- If `None` is returned, no changes are made to the user's group membership
If authenticator-managed groups are enabled,
all group-management via the API is disabled.
## pre_spawn_start and post_spawn_stop hooks
Authenticators uses two hooks, [pre_spawn_start(user, spawner)][] and
[post_spawn_stop(user, spawner)][] to pass additional state information
between the authenticator and a spawner. These hooks are typically used for auth-related
Authenticators use two hooks, {meth}`.Authenticator.pre_spawn_start` and
{meth}`.Authenticator.post_spawn_stop(user, spawner)` to add pass additional state information
between the authenticator and a spawner. These hooks are typically used auth-related
startup, i.e. opening a PAM session, and auth-related cleanup, i.e. closing a
PAM session.
@@ -269,11 +307,7 @@ PAM session.
Beginning with version 0.8, JupyterHub is an OAuth provider.
[Authenticator]: https://github.com/jupyterhub/jupyterhub/blob/master/jupyterhub/auth.py
[PAM]: https://en.wikipedia.org/wiki/Pluggable_authentication_module
[OAuth]: https://en.wikipedia.org/wiki/OAuth
[GitHub OAuth]: https://developer.github.com/v3/oauth/
[OAuthenticator]: https://github.com/jupyterhub/oauthenticator
[pre_spawn_start(user, spawner)]: https://jupyterhub.readthedocs.io/en/latest/api/auth.html#jupyterhub.auth.Authenticator.pre_spawn_start
[post_spawn_stop(user, spawner)]: https://jupyterhub.readthedocs.io/en/latest/api/auth.html#jupyterhub.auth.Authenticator.post_spawn_stop
[pam]: https://en.wikipedia.org/wiki/Pluggable_authentication_module
[oauth]: https://en.wikipedia.org/wiki/OAuth
[github oauth]: https://developer.github.com/v3/oauth/
[oauthenticator]: https://github.com/jupyterhub/oauthenticator

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@@ -3,18 +3,17 @@
In this example, we show a configuration file for a fairly standard JupyterHub
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 the default spawner (to configure other spawners, uncomment and edit
- Running JupyterHub on a single cloud server
- Using SSL on the standard HTTPS port 443
- 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
- 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`.
- 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`
The `jupyterhub_config.py` file would have these settings:
@@ -52,7 +51,7 @@ c.GitHubOAuthenticator.oauth_callback_url = os.environ['OAUTH_CALLBACK_URL']
c.LocalAuthenticator.create_system_users = True
# specify users and admin
c.Authenticator.whitelist = {'rgbkrk', 'minrk', 'jhamrick'}
c.Authenticator.allowed_users = {'rgbkrk', 'minrk', 'jhamrick'}
c.Authenticator.admin_users = {'jhamrick', 'rgbkrk'}
# uses the default spawner
@@ -70,7 +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
@@ -80,3 +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.

93
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View File

@@ -6,15 +6,15 @@ SSL port `443`. This could be useful if the JupyterHub server machine is also
hosting other domains or content on `443`. The goal in this example is to
satisfy the following:
* JupyterHub is running on a server, accessed *only* via `HUB.DOMAIN.TLD:443`
* On the same machine, `NO_HUB.DOMAIN.TLD` strictly serves different content,
- JupyterHub is running on a server, accessed _only_ via `HUB.DOMAIN.TLD:443`
- On the same machine, `NO_HUB.DOMAIN.TLD` strictly serves different content,
also on port `443`
* `nginx` or `apache` is used as the public access point (which means that
only nginx/apache will bind to `443`)
* After testing, the server in question should be able to score at least an A on the
- `nginx` or `apache` is used as the public access point (which means that
only nginx/apache will bind to `443`)
- 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;
@@ -83,8 +83,12 @@ server {
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
# websocket headers
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection $connection_upgrade;
proxy_set_header X-Scheme $scheme;
proxy_buffering off;
}
# Managing requests to verify letsencrypt host
@@ -97,10 +101,10 @@ server {
If `nginx` is not running on port 443, substitute `$http_host` for `$host` on
the lines setting the `Host` header.
`nginx` will now be the front facing element of JupyterHub on `443` which means
`nginx` will now be the front-facing element of JupyterHub on `443` which means
it is also free to bind other servers, like `NO_HUB.DOMAIN.TLD` to the same port
on the same machine and network interface. In fact, one can simply use the same
server blocks as above for `NO_HUB` and simply add line for the root directory
server blocks as above for `NO_HUB` and simply add a line for the root directory
of the site as well as the applicable location call:
```bash
@@ -108,7 +112,7 @@ server {
listen 80;
server_name NO_HUB.DOMAIN.TLD;
# Tell all requests to port 80 to be 302 redirected to HTTPS
# Redirect the request to HTTPS
return 302 https://$host$request_uri;
}
@@ -139,25 +143,40 @@ 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
On distributions with SELinux enabled (e.g. Fedora), one may encounter permission errors
when the Nginx service is started.
We need to allow Nginx to perform network relay and connect to the JupyterHub port. The
following commands do that:
```bash
semanage port -a -t http_port_t -p tcp 8000
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.
## 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 proxy_http proxy_wstunnel
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
@@ -169,15 +188,26 @@ Listen 443
ServerName HUB.DOMAIN.TLD
# configure SSL
# 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
SSLEngine on
SSLCertificateFile /etc/letsencrypt/live/HUB.DOMAIN.TLD/fullchain.pem
SSLCertificateKeyFile /etc/letsencrypt/live/HUB.DOMAIN.TLD/privkey.pem
SSLProtocol All -SSLv2 -SSLv3
SSLOpenSSLConfCmd DHParameters /etc/ssl/certs/dhparam.pem
SSLCipherSuite EECDH+AESGCM:EDH+AESGCM:AES256+EECDH:AES256+EDH
# Use RewriteEngine to handle websocket connection upgrades
# 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
RewriteEngine On
RewriteCond %{HTTP:Connection} Upgrade [NC]
RewriteCond %{HTTP:Upgrade} websocket [NC]
@@ -189,26 +219,29 @@ Listen 443
# proxy to JupyterHub
ProxyPass http://127.0.0.1:8000/
ProxyPassReverse http://127.0.0.1:8000/
RequestHeader set "X-Forwarded-Proto" expr=%{REQUEST_SCHEME}
</Location>
</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
httpd.conf amendments:
```bash
RewriteRule /jhub/(.*) ws://127.0.0.1:8000/jhub/$1 [P,L]
RewriteRule /jhub/(.*) http://127.0.0.1:8000/jhub/$1 [P,L]
ProxyPass /jhub/ http://127.0.0.1:8000/jhub/
ProxyPassReverse /jhub/ http://127.0.0.1:8000/jhub/
```
```
jupyterhub_config.py amendments:
```python
# The public facing URL of the whole JupyterHub application.
# This is the address on which the proxy will bind. Sets protocol, ip, base_url
# 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/'
```

30
docs/source/reference/config-reference.rst Normal file
View File

@@ -0,0 +1,30 @@
==============================
Configuration Reference
==============================
.. important::
Make sure the version of JupyterHub for this documentation matches your
installation version, as the output of this command may change between versions.
JupyterHub configuration
------------------------
As explained in the `Configuration Basics <../getting-started/config-basics.html#generate-a-default-config-file>`_
section, the ``jupyterhub_config.py`` can be automatically generated via
.. code-block:: bash
jupyterhub --generate-config
The following contains the output of that command for reference.
.. jupyterhub-generate-config::
JupyterHub help command output
------------------------------
This section contains the output of the command ``jupyterhub --help-all``.
.. jupyterhub-help-all::

34
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View File

@@ -6,10 +6,10 @@ Only do this if you are very sure you must.
## Overview
There are many Authenticators and Spawners available for JupyterHub. Some, such
as DockerSpawner or OAuthenticator, do not need any elevated permissions. This
There are many [Authenticators](../getting-started/authenticators-users-basics) and [Spawners](../getting-started/spawners-basics) available for JupyterHub. Some, such
as [DockerSpawner](https://github.com/jupyterhub/dockerspawner) or [OAuthenticator](https://github.com/jupyterhub/oauthenticator), do not need any elevated permissions. This
document describes how to get the full default behavior of JupyterHub while
running notebook servers as real system users on a shared system without
running notebook servers as real system users on a shared system, without
running the Hub itself as root.
Since JupyterHub needs to spawn processes as other users, the simplest way
@@ -50,14 +50,13 @@ To do this we add to `/etc/sudoers` (use `visudo` for safe editing of sudoers):
- specify the list of users `JUPYTER_USERS` for whom `rhea` can spawn servers
- set the command `JUPYTER_CMD` that `rhea` can execute on behalf of users
- give `rhea` permission to run `JUPYTER_CMD` on behalf of `JUPYTER_USERS`
- give `rhea` permission to run `JUPYTER_CMD` on behalf of `JUPYTER_USERS`
without entering a password
For example:
```bash
# comma-separated whitelist of users that can spawn single-user servers
# comma-separated list of users that can spawn single-user servers
# this should include all of your Hub users
Runas_Alias JUPYTER_USERS = rhea, zoe, wash
@@ -92,16 +91,16 @@ $ adduser -G jupyterhub newuser
Test that the new user doesn't need to enter a password to run the sudospawner
command.
This should prompt for your password to switch to rhea, but *not* prompt for
This should prompt for your password to switch to `rhea`, but _not_ prompt for
any password for the second switch. It should show some help output about
logging options:
```bash
$ sudo -u rhea sudo -n -u $USER /usr/local/bin/sudospawner --help
Usage: /usr/local/bin/sudospawner [OPTIONS]
Options:
--help show this help information
...
```
@@ -121,6 +120,11 @@ the shadow password database.
### Shadow group (Linux)
**Note:** On [Fedora based distributions](https://fedoraproject.org/wiki/List_of_Fedora_remixes) there is no clear way to configure
the PAM database to allow sufficient access for authenticating with the target user's password
from JupyterHub. As a workaround we recommend use an
[alternative authentication method](https://github.com/jupyterhub/jupyterhub/wiki/Authenticators).
```bash
$ ls -l /etc/shadow
-rw-r----- 1 root shadow 2197 Jul 21 13:41 shadow
@@ -147,12 +151,13 @@ We want our new user to be able to read the shadow passwords, so add it to the s
$ sudo usermod -a -G shadow rhea
```
If you want jupyterhub to serve pages on a restricted port (such as port 80 for http),
If you want jupyterhub to serve pages on a restricted port (such as port 80 for HTTP),
then you will need to give `node` permission to do so:
```bash
sudo setcap 'cap_net_bind_service=+ep' /usr/bin/node
```
However, you may want to further understand the consequences of this.
([Further reading](http://man7.org/linux/man-pages/man7/capabilities.7.html))
@@ -162,7 +167,6 @@ distributions' packaging system. This can be used to keep any user's process
from using too much CPU cycles. You can configure it accoring to [these
instructions](http://ubuntuforums.org/showthread.php?t=992706).
### Shadow group (FreeBSD)
**NOTE:** This has not been tested on FreeBSD and may not work as expected on
@@ -184,7 +188,7 @@ $ sudo chgrp shadow /etc/master.passwd
$ sudo chmod g+r /etc/master.passwd
```
We want our new user to be able to read the shadow passwords, so add it to the
We want our new user to be able to read the shadow passwords, so add it to the
shadow group:
```bash
@@ -218,15 +222,15 @@ Finally, start the server as our newly configured user, `rhea`:
```bash
$ cd /etc/jupyterhub
$ sudo -u rhea jupyterhub --JupyterHub.spawner_class=sudospawner.SudoSpawner
```
```
And try logging in.
## Troubleshooting: SELinux
If you still get a generic `Permission denied` `PermissionError`, it's possible SELinux is blocking you.
Here's how you can make a module to allow this.
First, put this in a file named `sudo_exec_selinux.te`:
Here's how you can make a module to resolve this.
First, put this in a file named `sudo_exec_selinux.te`:
```bash
module sudo_exec_selinux 1.1;

170
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View File

@@ -1,8 +1,8 @@
# 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
@@ -10,71 +10,56 @@ 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`.
**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:
- Installing packages
- Configuring Jupyter and IPython
- Installing kernelspecs
- Using containers vs. multi-user hosts
This section will focus on user environments, which includes the following:
- [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
`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.
If you are using a standard system Python install, you would use:
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 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 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`).
TODO: Get a link from the conda team for a description of what "appropriate permissions for users" is
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. The env must be *readable and executable* by all
users. Additionally it must be *writeable* if you want users to install
additional packages.
## Configuring Jupyter and IPython
[Jupyter](https://jupyter-notebook.readthedocs.io/en/stable/config_overview.html)
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}`
- **env-wide** (environment wide) in `{sys.prefix}/etc/{jupyter|ipython}`.
### 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")
@@ -82,32 +67,39 @@ c.InteractiveShellApp.extensions.append("cython")
### Example: Enable a Jupyter notebook configuration setting for all users
To enable Jupyter notebook's internal idle-shutdown behavior (requires
notebook ≥ 5.4), set the following in the `/etc/jupyter/jupyter_notebook_config.py`
file:
:::{note}
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:
- 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:
```python
# shutdown the server after no activity for an hour
c.NotebookApp.shutdown_no_activity_timeout = 60 * 60
c.ServerApp.shutdown_no_activity_timeout = 60 * 60
# shutdown kernels after no activity for 20 minutes
c.MappingKernelManager.cull_idle_timeout = 20 * 60
# check for idle kernels every two minutes
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
@@ -115,15 +107,13 @@ 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 IPython kernel install --prefix=/usr/local
/path/to/python2 -m IPython kernel install --prefix=/usr/local
/path/to/python3 -m ipykernel install --prefix=/usr/local
/path/to/python2 -m ipykernel install --prefix=/usr/local
```
## Multi-user hosts vs. Containers
There are two broad categories of user environments that depend on what
@@ -136,31 +126,25 @@ 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.
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, 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
@@ -176,10 +160,66 @@ 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
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
```
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):
user = handler.current_user
if user and user.admin:
return 0
return 5
c.JupyterHub.named_server_limit_per_user = named_server_limit_per_user_fn
```
This can be useful for quota service implementations. The example above limits the number of named servers for non-admin users only.
If `named_server_limit_per_user` is set to `0`, no limit is enforced.
(classic-notebook-ui)=
## Switching back to the classic notebook
By default, the single-user server launches JupyterLab,
which is based on [Jupyter Server][].
This is the default server when running JupyterHub ≥ 2.0.
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
export JUPYTERHUB_SINGLEUSER_APP='notebook.notebookapp.NotebookApp'
```
[jupyter server]: https://jupyter-server.readthedocs.io
[jupyter notebook]: https://jupyter-notebook.readthedocs.io
:::{versionchanged} 2.0
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 the Jupyter server could be selected by specifying the following:
```python
# jupyterhub_config.py
c.Spawner.cmd = ["jupyter-labhub"]
```
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'
```
:::

4
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View File

@@ -46,8 +46,8 @@ additional configuration required for MySQL that is not needed for PostgreSQL.
- You should use the `pymysql` sqlalchemy provider (the other one, MySQLdb,
isn't available for py3).
- You also need to set `pool_recycle` to some value (typically 60 - 300)
which depends on your MySQL setup. This is necessary since MySQL kills
- You also need to set `pool_recycle` to some value (typically 60 - 300)
which depends on your MySQL setup. This is necessary since MySQL kills
connections serverside if they've been idle for a while, and the connection
from the hub will be idle for longer than most connections. This behavior
will lead to frustrating 'the connection has gone away' errors from

10
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@@ -1,6 +1,9 @@
Technical Reference
===================
This section covers more of the details of the JupyterHub architecture, as well as
what happens under-the-hood when you deploy and configure your JupyterHub.
.. toctree::
:maxdepth: 2
@@ -13,10 +16,17 @@ Technical Reference
proxy
separate-proxy
rest
rest-api
server-api
monitoring
database
templates
api-only
../events/index
config-user-env
config-examples
config-ghoauth
config-proxy
config-sudo
config-reference
oauth

20
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@@ -0,0 +1,20 @@
Monitoring
==========
This section covers details on monitoring the state of your JupyterHub installation.
JupyterHub expose the ``/metrics`` endpoint that returns text describing its current
operational state formatted in a way `Prometheus <https://prometheus.io/docs/introduction/overview/>`_ understands.
Prometheus is a separate open source tool that can be configured to repeatedly poll
JupyterHub's ``/metrics`` endpoint to parse and save its current state.
By doing so, Prometheus can describe JupyterHub's evolving state over time.
This evolving state can then be accessed through Prometheus that expose its underlying
storage to those allowed to access it, and be presented with dashboards by a
tool like `Grafana <https://grafana.com/docs/grafana/latest/getting-started/what-is-grafana/>`_.
.. toctree::
:maxdepth: 2
metrics

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@@ -0,0 +1,373 @@
# JupyterHub and OAuth
JupyterHub uses [OAuth 2](https://oauth.net/2/) as an internal mechanism for authenticating users.
As such, JupyterHub itself always functions as an OAuth **provider**.
You can find out more about what that means [below](oauth-terms).
Additionally, JupyterHub is _often_ deployed with [OAuthenticator](https://oauthenticator.readthedocs.io),
where an external identity provider, such as GitHub or KeyCloak, is used to authenticate users.
When this is the case, there are _two_ nested OAuth flows:
an _internal_ OAuth flow where JupyterHub is the **provider**,
and an _external_ OAuth flow, where JupyterHub is the **client**.
This means that when you are using JupyterHub, there is always _at least one_ and often two layers of OAuth involved in a user logging in and accessing their server.
The following points are noteworthy:
- Single-user servers _never_ need to communicate with or be aware of the upstream provider configured in your Authenticator.
As far as the servers are concerned, only JupyterHub is an OAuth provider,
and how users authenticate with the Hub itself is irrelevant.
- When interacting with a single-user server,
there are ~always two tokens:
first, a token issued to the server itself to communicate with the Hub API,
and second, a per-user token in the browser to represent the completed login process and authorized permissions.
More on this [later](two-tokens).
(oauth-terms)=
## Key OAuth terms
Here are some key definitions to keep in mind when we are talking about OAuth.
You can also read more in detail [here](https://www.oauth.com/oauth2-servers/definitions/).
- **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;
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
for a **token** at the conclusion of OAuth,
in what's generally called the "OAuth callback handler."
## One oauth flow
OAuth **flow** is what we call the sequence of HTTP requests involved in authenticating a user and issuing a token, ultimately used for authorizing access to a service or single-user server.
A single OAuth flow typically goes like this:
### OAuth request and redirect
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 **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,
the browser must be _authenticated_ with the provider.
This is often already stored in a cookie,
but if not the provider webapp must begin its _own_ authentication process before serving the authorization page.
This _may_ even begin another OAuth flow!
4. After the user tells the provider that they want to proceed with the authorization,
the provider records this authorization in a short-lived record called an **OAuth code**.
5. Finally, the oauth provider redirects the browser _back_ to the oauth client's "redirect URI"
(or "OAuth callback URI"),
with the OAuth code in a URL parameter.
That marks the end of the requests made between the **browser** and the **provider**.
### State after redirect
At this point:
- The browser is authenticated with the _provider_.
- The user's authorized permissions are recorded in an _OAuth code_.
- The _provider_ knows that the permissions requested by the OAuth client have been granted, but the client doesn't know this yet.
- All the requests so far have been made directly by the browser.
No requests have originated from the client or provider.
### OAuth Client Handles Callback Request
At this stage, we get to finish the OAuth process.
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_.
- Once the token is retrieved, the client _usually_
makes a second API request to the _provider_
to retrieve information about the owner of the token (the user).
This is the step where behavior diverges for different OAuth providers.
Up to this point, all OAuth providers are the same, following the OAuth specification.
However, OAuth does not define a standard for issuing tokens in exchange for information about their owner or permissions ([OpenID Connect](https://openid.net/connect/) does that),
so this step may be different for each OAuth provider.
- Finally, the OAuth client stores its own record that the user is authorized in a cookie.
This could be the token itself, or any other appropriate representation of successful authentication.
- Now that credentials have been established,
the browser can be redirected to the _original_ URL where it started,
to try the request again.
If the client wasn't able to keep track of the original URL all this time
(not always easy!),
you might end up back at a default landing page instead of where you started the login process. This is frustrating!
😮‍💨 _phew_.
So that's _one_ OAuth process.
## Full sequence of OAuth in JupyterHub
Let's go through the above OAuth process in JupyterHub,
with specific examples of each HTTP request and what information it contains.
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."
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.
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**)
- response: 302 redirect -> `/hub/api/oauth2/authorize`
with:
- client-id=`jupyterhub-user-danez`
- redirect-uri=`/user/danez/oauth_callback` (we'll come back later!)
Second request, following redirect:
- browser->JupyterHub
- `GET /hub/api/oauth2/authorize`
- 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`
- redirect-uri=`/hub/oauth_callback` (we'll come back later!)
_pause_ This is where JupyterHub configuration comes into play.
Recall, in this case JupyterHub is using:
```python
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.
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.
Third request, following redirect:
- browser->GitHub
- `GET https://github.com/login/oauth/authorize`
Here, GitHub prompts for login and asks for confirmation of authorization
(more redirects if you aren't logged in to GitHub yet, but ultimately back to this `/authorize` URL).
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`
Next request:
- browser->JupyterHub
- `GET /hub/oauth_callback?code=github-code`
Inside the callback handler, JupyterHub makes two API requests:
The first:
- JupyterHub->GitHub
- `POST https://github.com/login/oauth/access_token`
- request made with OAuth **code** from URL parameter
- response includes an access **token**
The second:
- JupyterHub->GitHub
- `GET https://api.github.com/user`
- 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:
- 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! 🎉
Now, we get our first repeated request:
- browser->jupyterhub
- `GET /hub/api/oauth2/authorize`
- this time with credentials,
so jupyterhub either
1. serves the internal authorization confirmation page, or
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.
- browser->single-user server
- `GET /user/danez/oauth_callback`
(in handler)
Inside the internal OAuth callback handler,
Danez's server makes two API requests to JupyterHub:
The first:
- single-user server->JupyterHub
- `POST /hub/api/oauth2/token`
- request made with oauth code from url parameter
- response includes an API token
The second:
- single-user server->JupyterHub
- `GET /hub/api/user`
- request made with token in the `Authorization` header
- response is the user model, including username, groups, etc.
Finally completing `GET /user/danez/oauth_callback`:
- response sets cookie, storing encrypted access token
- _finally_ redirects back to the original `/user/danez/notebooks/mynotebook.ipynb`
Final request:
- browser -> single-user server
- `GET /user/danez/notebooks/mynotebook.ipynb`
- encrypted jupyterhub token in cookie
To authenticate this request, the single token stored in the encrypted cookie is passed to the Hub for verification:
- single-user server -> Hub
- `GET /hub/api/user`
- browser's token in Authorization header
- response: user model with name, groups, etc.
If the user model matches who should be allowed (e.g. Danez),
then the request is allowed.
See {doc}`../rbac/scopes` for how JupyterHub uses scopes to determine authorized access to servers and services.
_the end_
## Token caches and expiry
Because tokens represent information from an external source,
they can become 'stale,'
or the information they represent may no longer be accurate.
For example: a user's GitHub account may no longer be authorized to use JupyterHub,
that should ultimately propagate to revoking access and force logging in again.
To handle this, OAuth tokens and the various places they are stored can _expire_,
which should have the same effect as no credentials,
and trigger the authorization process again.
In JupyterHub's internal OAuth, we have these layers of information that can go stale:
- The OAuth client has a **cache** of Hub responses for tokens,
so it doesn't need to make API requests to the Hub for every request it receives.
This cache has an expiry of five minutes by default,
and is governed by the configuration `HubAuth.cache_max_age` in the single-user server.
- The internal OAuth token is stored in a cookie, which has its own expiry (default: 14 days),
governed by `JupyterHub.cookie_max_age_days`.
- The internal OAuth token itself can also expire,
which is by default the same as the cookie expiry,
since it makes sense for the token itself and the place it is stored to expire at the same time.
This is governed by `JupyterHub.cookie_max_age_days` first,
or can overridden by `JupyterHub.oauth_token_expires_in`.
That's all for _internal_ auth storage,
but the information from the _external_ authentication provider
(could be PAM or GitHub OAuth, etc.) can also expire.
Authenticator configuration governs when JupyterHub needs to ask again,
triggering the external login process anew before letting a user proceed.
- `jupyterhub-hub-login` cookie stores that a browser is authenticated with the Hub.
This expires according to `JupyterHub.cookie_max_age_days` configuration,
with a default of 14 days.
The `jupyterhub-hub-login` cookie is encrypted with `JupyterHub.cookie_secret`
configuration.
- {meth}`.Authenticator.refresh_user` is a method to refresh a user's auth info.
By default, it does nothing, but it can return an updated user model if a user's information has changed,
or force a full login process again if needed.
- {attr}`.Authenticator.auth_refresh_age` configuration governs how often
`refresh_user()` will be called to check if a user must login again (default: 300 seconds).
- {attr}`.Authenticator.refresh_pre_spawn` configuration governs whether
`refresh_user()` should be called prior to spawning a server,
to force fresh auth info when a server is launched (default: False).
This can be useful when Authenticators pass access tokens to spawner environments, to ensure they aren't getting a stale token that's about to expire.
**So what happens when these things expire or get stale?**
- If the HubAuth **token response cache** expires,
when a request is made with a token,
the Hub is asked for the latest information about the token.
This usually has no visible effect, since it is just refreshing a cache.
If it turns out that the token itself has expired or been revoked,
the request will be denied.
- If the token has expired, but is still in the cookie:
when the token response cache expires,
the next time the server asks the hub about the token,
no user will be identified and the internal OAuth process begins again.
- If the token _cookie_ expires, the next browser request will be made with no credentials,
and the internal OAuth process will begin again.
This will usually have the form of a transparent redirect browsers won't notice.
However, if this occurs on an API request in a long-lived page visit
such as a JupyterLab session, the API request may fail and require
a page refresh to get renewed credentials.
- If the _JupyterHub_ cookie expires, the next time the browser makes a request to the Hub,
the Hub's authorization process must begin again (e.g. login with GitHub).
Hub cookie expiry on its own **does not** mean that a user can no longer access their single-user server!
- If credentials from the upstream provider (e.g. GitHub) become stale or outdated,
these will not be refreshed until/unless `refresh_user` is called
_and_ `refresh_user()` on the given Authenticator is implemented to perform such a check.
At this point, few Authenticators implement `refresh_user` to support this feature.
If your Authenticator does not or cannot implement `refresh_user`,
the only way to force a check is to reset the `JupyterHub.cookie_secret` encryption key,
which invalidates the `jupyterhub-hub-login` cookie for all users.
### Logging out
Logging out of JupyterHub means clearing and revoking many of these credentials:
- The `jupyterhub-hub-login` cookie is revoked, meaning the next request to the Hub itself will require a new login.
- The token stored in the `jupyterhub-user-username` cookie for the single-user server
will be revoked, based on its associaton with `jupyterhub-session-id`, but the _cookie itself cannot be cleared at this point_
- The shared `jupyterhub-session-id` is cleared, which ensures that the HubAuth **token response cache** will not be used,
and the next request with the expired token will ask the Hub, which will inform the single-user server that the token has expired
## Extra bits
(two-tokens)=
### A tale of two tokens
**TODO**: discuss API token issued to server at startup ($JUPYTERHUB_API_TOKEN)
and OAuth-issued token in the cookie,
and some details of how JupyterLab currently deals with that.
They are different, and JupyterLab should be making requests using the token from the cookie,
not the token from the server,
but that is not currently the case.
### Redirect loops
In general, an authenticated web endpoint has this behavior,
based on the authentication/authorization state of the browser:
- If authorized, allow the request to happen
- If authenticated (I know who you are) but not authorized (you are not allowed), fail with a 403 permission denied error
- If not authenticated, start a redirect process to establish authorization,
which should end in a redirect back to the original URL to try again.
**This is why problems in authentication result in redirect loops!**
If the second request fails to detect the authentication that should have been established during the redirect,
it will start the authentication redirect process over again,
and keep redirecting in a loop until the browser balks.

23
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@@ -7,9 +7,12 @@ Hub manages by default as a subprocess (it can be run externally, as well, and
typically is in production deployments).
The upside to CHP, and why we use it by default, is that it's easy to install
and run (if you have nodejs, you are set!). The downsides are that it's a
single process and does not support any persistence of the routing table. So
if the proxy process dies, your whole JupyterHub instance is inaccessible
and run (if you have nodejs, you are set!). The downsides are that
- it's a single process and
- does not support any persistence of the routing table.
So if the proxy process dies, your whole JupyterHub instance is inaccessible
until the Hub notices, restarts the proxy, and restores the routing table. For
deployments that want to avoid such a single point of failure, or leverage
existing proxy infrastructure in their chosen deployment (such as Kubernetes
@@ -54,7 +57,7 @@ class MyProxy(Proxy):
"""Stop the proxy"""
```
These methods **may** be coroutines.
These methods **may** be coroutines.
`c.Proxy.should_start` is a configurable flag that determines whether the
Hub should call these methods when the Hub itself starts and stops.
@@ -103,7 +106,7 @@ route to be proxied, such as `/user/name/`. A routespec will:
When adding a route, JupyterHub may pass a JSON-serializable dict as a `data`
argument that should be attached to the proxy route. When that route is
retrieved, the `data` argument should be returned as well. If your proxy
retrieved, the `data` argument should be returned as well. If your proxy
implementation doesn't support storing data attached to routes, then your
Python wrapper may have to handle storing the `data` piece itself, e.g in a
simple file or database.
@@ -136,7 +139,7 @@ async def delete_route(self, routespec):
### Retrieving routes
For retrieval, you only *need* to implement a single method that retrieves all
For retrieval, you only _need_ to implement a single method that retrieves all
routes. The return value for this function should be a dictionary, keyed by
`routespec`, of dicts whose keys are the same three arguments passed to
`add_route` (`routespec`, `target`, `data`)
@@ -220,3 +223,11 @@ as previously required.
Additionally, configurable attributes for your proxy will
appear in jupyterhub help output and auto-generated configuration files
via `jupyterhub --generate-config`.
### Index of proxies
A list of the proxies that are currently available for JupyterHub (that we know about).
1. [`jupyterhub/configurable-http-proxy`](https://github.com/jupyterhub/configurable-http-proxy) The default proxy which uses node-http-proxy
2. [`jupyterhub/traefik-proxy`](https://github.com/jupyterhub/traefik-proxy) The proxy which configures traefik proxy server for jupyterhub
3. [`AbdealiJK/configurable-http-proxy`](https://github.com/AbdealiJK/configurable-http-proxy) A pure python implementation of the configurable-http-proxy

27
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@@ -0,0 +1,27 @@
# JupyterHub REST API
Below is an interactive view of JupyterHub's OpenAPI specification.
<!-- client-rendered openapi UI copied from FastAPI -->
<link type="text/css" rel="stylesheet" href="https://cdn.jsdelivr.net/npm/swagger-ui-dist@3/swagger-ui.css">
<script src="https://cdn.jsdelivr.net/npm/swagger-ui-dist@4.1/swagger-ui-bundle.js"></script>
<!-- `SwaggerUIBundle` is now available on the page -->
<!-- render the ui here -->
<div id="openapi-ui"></div>
<script>
const ui = SwaggerUIBundle({
url: '../_static/rest-api.yml',
dom_id: '#openapi-ui',
presets: [
SwaggerUIBundle.presets.apis,
SwaggerUIBundle.SwaggerUIStandalonePreset
],
layout: "BaseLayout",
deepLinking: true,
showExtensions: true,
showCommonExtensions: true,
});
</script>

14
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@@ -1,14 +0,0 @@
:orphan:
===================
JupyterHub REST API
===================
.. this doc exists as a resolvable link target
.. which _static files are not
.. meta::
:http-equiv=refresh: 0;url=../_static/rest-api/index.html
The rest API docs are `here <../_static/rest-api/index.html>`_
if you are not redirected automatically.

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@@ -1,34 +1,39 @@
(rest-api)=
# Using JupyterHub's REST API
This section will give you information on:
- what you can do with the API
- create an API token
- add API tokens to the config files
- make an API request programmatically using the requests library
- learn more about JupyterHub's API
- What you can do with the API
- How to create an API token
- Assigning permissions to a token
- Updating to admin services
- Making an API request programmatically using the requests library
- Paginating API requests
- Enabling users to spawn multiple named-servers via the API
- Learn more about JupyterHub's API
Before we discuss about JupyterHub's REST API, you can learn about [REST APIs here](https://en.wikipedia.org/wiki/Representational_state_transfer). A REST
API provides a standard way for users to get and send information to the
Hub.
## What you can do with the API
Using the [JupyterHub REST API][], you can perform actions on the Hub,
such as:
- checking which users are active
- adding or removing users
- stopping or starting single user notebook servers
- authenticating services
A [REST](https://en.wikipedia.org/wiki/Representational_state_transfer)
API provides a standard way for users to get and send information to the
Hub.
- Checking which users are active
- Adding or removing users
- Stopping or starting single user notebook servers
- Authenticating services
- Communicating with an individual Jupyter server's REST API
## Create an API token
To send requests using JupyterHub API, you must pass an API token with
To send requests using the JupyterHub API, you must pass an API token with
the request.
As of [version 0.6.0](../changelog.md), the preferred way of
generating an API token is:
The preferred way of generating an API token is by running:
```bash
openssl rand -hex 32
@@ -38,8 +43,12 @@ This `openssl` command generates a potential token that can then be
added to JupyterHub using `.api_tokens` configuration setting in
`jupyterhub_config.py`.
Alternatively, use the `jupyterhub token` command to generate a token
for a specific hub user by passing the 'username':
```{note}
The api_tokens configuration has been softly deprecated since the introduction of services.
```
Alternatively, you can use the `jupyterhub token` command to generate a token
for a specific hub user by passing the **username**:
```bash
jupyterhub token <username>
@@ -48,25 +57,94 @@ jupyterhub token <username>
This command generates a random string to use as a token and registers
it for the given user with the Hub's database.
In [version 0.8.0](../changelog.md), a TOKEN request page for
In [version 0.8.0](../changelog.md), a token request page for
generating an API token is available from the JupyterHub user interface:
![Request API TOKEN page](../images/token-request.png)
:::{figure-md}
![API TOKEN success page](../images/token-request-success.png)
![token request page](../images/token-request.png)
## Add API tokens to the config file
JupyterHub's API token page
:::
You may also add a dictionary of API tokens and usernames to the hub's
configuration file, `jupyterhub_config.py` (note that
the **key** is the 'secret-token' while the **value** is the 'username'):
:::{figure-md}
![token-request-success](../images/token-request-success.png)
JupyterHub's token page after successfully requesting a token.
:::
## Assigning permissions to a token
Prior to JupyterHub 2.0, there were two levels of permissions:
1. user, and
2. admin
where a token would always have full permissions to do whatever its owner could do.
In JupyterHub 2.0,
specific permissions are now defined as '**scopes**',
and can be assigned both at the user/service level,
and at the individual token level.
This allows e.g. a user with full admin permissions to request a token with limited permissions.
## Updating to admin services
```{note}
The `api_tokens` configuration has been softly deprecated since the introduction of services.
We have no plans to remove it,
but deployments are encouraged to use service configuration instead.
```
If you have been using `api_tokens` to create an admin user
and the token for that user to perform some automations, then
the services' mechanism may be a better fit if you have the following configuration:
```python
c.JupyterHub.admin_users = {"service-admin"}
c.JupyterHub.api_tokens = {
'secret-token': 'username',
"secret-token": "service-admin",
}
```
This can be updated to create a service, with the following configuration:
```python
c.JupyterHub.services = [
{
# give the token a name
"name": "service-admin",
"api_token": "secret-token",
# "admin": True, # if using JupyterHub 1.x
},
]
# roles were introduced in JupyterHub 2.0
# prior to 2.0, only "admin": True or False was available
c.JupyterHub.load_roles = [
{
"name": "service-role",
"scopes": [
# specify the permissions the token should have
"admin:users",
],
"services": [
# assign the service the above permissions
"service-admin",
],
}
]
```
The token will have the permissions listed in the role
(see [scopes][] for a list of available permissions),
but there will no longer be a user account created to house it.
The main noticeable difference between a user and a service is that there will be no notebook server associated with the account
and the service will not show up in the various user list pages and APIs.
## Make an API request
To authenticate your requests, pass the API token in the request's
@@ -74,10 +152,9 @@ Authorization header.
### Use requests
Using the popular Python [requests](http://docs.python-requests.org/en/master/)
library, here's example code to make an API request for the users of a JupyterHub
deployment. An API GET request is made, and the request sends an API token for
authorization. The response contains information about the users:
Using the popular Python [requests](https://docs.python-requests.org)
library, an API GET request is made, and the request sends an API token for
authorization. The response contains information about the users, here's example code to make an API request for the users of a JupyterHub deployment
```python
import requests
@@ -86,9 +163,9 @@ api_url = 'http://127.0.0.1:8081/hub/api'
r = requests.get(api_url + '/users',
headers={
'Authorization': 'token %s' % token,
}
)
'Authorization': f'token {token}',
}
)
r.raise_for_status()
users = r.json()
@@ -106,23 +183,100 @@ data = {'name': 'mygroup', 'users': ['user1', 'user2']}
r = requests.post(api_url + '/groups/formgrade-data301/users',
headers={
'Authorization': 'token %s' % token,
},
json=data
'Authorization': f'token {token}',
},
json=data,
)
r.raise_for_status()
r.json()
```
The same API token can also authorize access to the [Jupyter Notebook REST API][]
provided by notebook servers managed by JupyterHub if one of the following is true:
1. The token is for the same user as the owner of the notebook
2. The token is tied to an admin user or service **and** `c.JupyterHub.admin_access` is set to `True`
provided by notebook servers managed by JupyterHub if it has the necessary `access:servers` scope.
(api-pagination)=
## Paginating API requests
```{versionadded} 2.0
```
Pagination is available through the `offset` and `limit` query parameters on
list endpoints, which can be used to return ideally sized windows of results.
Here's example code demonstrating pagination on the `GET /users`
endpoint to fetch the first 20 records.
```python
import os
import requests
api_url = 'http://127.0.0.1:8081/hub/api'
r = requests.get(
api_url + '/users?offset=0&limit=20',
headers={
"Accept": "application/jupyterhub-pagination+json",
"Authorization": f"token {token}",
},
)
r.raise_for_status()
r.json()
```
For backward-compatibility, the default structure of list responses is unchanged.
However, this lacks pagination information (e.g. is there a next page),
so if you have enough users that they won't fit in the first response,
it is a good idea to opt-in to the new paginated list format.
There is a new schema for list responses which include pagination information.
You can request this by including the header:
```
Accept: application/jupyterhub-pagination+json
```
with your request, in which case a response will look like:
```python
{
"items": [
{
"name": "username",
"kind": "user",
...
},
],
"_pagination": {
"offset": 0,
"limit": 20,
"total": 50,
"next": {
"offset": 20,
"limit": 20,
"url": "http://127.0.0.1:8081/hub/api/users?limit=20&offset=20"
}
}
}
```
where the list results (same as pre-2.0) will be in `items`,
and pagination info will be in `_pagination`.
The `next` field will include the `offset`, `limit`, and `url` for requesting the next page.
`next` will be `null` if there is no next page.
Pagination is governed by two configuration options:
- `JupyterHub.api_page_default_limit` - the page size, if `limit` is unspecified in the request
and the new pagination API is requested
(default: 50)
- `JupyterHub.api_page_max_limit` - the maximum page size a request can ask for (default: 200)
Pagination is enabled on the `GET /users`, `GET /groups`, and `GET /proxy` REST endpoints.
## Enabling users to spawn multiple named-servers via the API
With JupyterHub version 0.8, support for multiple servers per user has landed.
Support for multiple servers per user was introduced in JupyterHub [version 0.8.](../changelog.md)
Prior to that, each user could only launch a single default server via the API
like this:
@@ -131,14 +285,14 @@ curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/us
```
With the named-server functionality, it's now possible to launch more than one
specifically named servers against a given user. This could be used, for instance,
specifically named servers against a given user. This could be used, for instance,
to launch each server based on a different image.
First you must enable named-servers by including the following setting in the `jupyterhub_config.py` file.
`c.JupyterHub.allow_named_servers = True`
If using the [zero-to-jupyterhub-k8s](https://github.com/jupyterhub/zero-to-jupyterhub-k8s) set-up to run JupyterHub,
If you are using the [zero-to-jupyterhub-k8s](https://github.com/jupyterhub/zero-to-jupyterhub-k8s) set-up to run JupyterHub,
then instead of editing the `jupyterhub_config.py` file directly, you could pass
the following as part of the `config.yaml` file, as per the [tutorial](https://zero-to-jupyterhub.readthedocs.io/en/latest/):
@@ -149,6 +303,7 @@ hub:
```
With that setting in place, a new named-server is activated like this:
```bash
curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/users/<user>/servers/<serverA>"
curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/users/<user>/servers/<serverB>"
@@ -163,15 +318,11 @@ will need to be able to handle the case of multiple servers per user and ensure
uniqueness of names, particularly if servers are spawned via docker containers
or kubernetes pods.
## Learn more about the API
You can see the full [JupyterHub REST API][] for details. This REST API Spec can
be viewed in a more [interactive style on swagger's petstore][].
Both resources contain the same information and differ only in its display.
Note: The Swagger specification is being renamed the [OpenAPI Initiative][].
You can see the full [JupyterHub REST API][] for more details.
[interactive style on swagger's petstore]: http://petstore.swagger.io/?url=https://raw.githubusercontent.com/jupyterhub/jupyterhub/master/docs/rest-api.yml#!/default
[OpenAPI Initiative]: https://www.openapis.org/
[JupyterHub REST API]: ./rest-api
[Jupyter Notebook REST API]: http://petstore.swagger.io/?url=https://raw.githubusercontent.com/jupyter/notebook/master/notebook/services/api/api.yaml
[openapi initiative]: https://www.openapis.org/
[jupyterhub rest api]: ./rest-api
[scopes]: ../rbac/scopes.md
[jupyter notebook rest api]: https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/jupyter/notebook/HEAD/notebook/services/api/api.yaml

50
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View File

@@ -1,27 +1,24 @@
# Running proxy separately from the hub
## Background
The thing which users directly connect to is the proxy, by default
`configurable-http-proxy`. The proxy either redirects users to the
The thing which users directly connect to is the proxy, which by default is
`configurable-http-proxy`. The proxy either redirects users to the
hub (for login and managing servers), or to their own single-user
servers. Thus, as long as the proxy stays running, access to existing
servers. Thus, as long as the proxy stays running, access to existing
servers continues, even if the hub itself restarts or goes down.
When you first configure the hub, you may not even realize this
because the proxy is automatically managed by the hub. This is great
for getting started and even most use, but everytime you restart the
hub, all user connections also get restarted. But it's also simple to
because the proxy is automatically managed by the hub. This is great
for getting started and even most use-cases, although, everytime you restart the
hub, all user connections are also restarted. However, it is also simple to
run the proxy as a service separate from the hub, so that you are free
to reconfigure the hub while only interrupting users who are waiting for their notebook server to start.
starting their notebook server.
The default JupyterHub proxy is
[configurable-http-proxy](https://github.com/jupyterhub/configurable-http-proxy),
and that page has some docs. If you are using a different proxy, such
as Traefik, these instructions are probably not relevant to you.
[configurable-http-proxy](https://github.com/jupyterhub/configurable-http-proxy). If you are using a different proxy, such
as [Traefik](https://github.com/traefik/traefik), these instructions are probably not relevant to you.
## Configuration options
@@ -37,24 +34,25 @@ it yourself).
token for authenticating communication with the proxy.
`c.ConfigurableHTTPProxy.api_url = 'http://localhost:8001'` should be
set to the URL which the hub uses to connect *to the proxy's API*.
set to the URL which the hub uses to connect _to the proxy's API_.
## Proxy configuration
You need to configure a service to start the proxy. An example
command line for this is `configurable-http-proxy --ip=127.0.0.1
--port=8000 --api-ip=127.0.0.1 --api-port=8001
--default-target=http://localhost:8081
--error-target=http://localhost:8081/hub/error`. (Details for how to
do this is out of scope for this tutorial - for example it might be a
systemd service on within another docker cotainer). The proxy has no
You need to configure a service to start the proxy. An example
command line argument for this is:
```bash
$ configurable-http-proxy --ip=127.0.0.1 --port=8000 --api-ip=127.0.0.1 --api-port=8001 --default-target=http://localhost:8081 --error-target=http://localhost:8081/hub/error
```
(Details on how to do this is out of the scope of this tutorial. For example, it might be a
systemd service configured within another docker container). The proxy has no
configuration files, all configuration is via the command line and
environment variables.
`--api-ip` and `--api-port` (which tells the proxy where to listen) should match the hub's `ConfigurableHTTPProxy.api_url`.
`--ip`, `-port`, and other options configure the *user* connections to the proxy.
`--ip`, `-port`, and other options configure the _user_ connections to the proxy.
`--default-target` and `--error-target` should point to the hub, and used when users navigate to the proxy originally.
@@ -63,10 +61,9 @@ match the token given to `c.ConfigurableHTTPProxy.auth_token`.
You should check the [configurable-http-proxy
options](https://github.com/jupyterhub/configurable-http-proxy) to see
what other options are needed, for example SSL options. Note that
these are configured in the hub if the hub is starting the proxy - you
need to move the options to here.
what other options are needed, for example, SSL options. Note that
these options are configured in the hub if the hub is starting the proxy, so you
need to configure the options there.
## Docker image
@@ -74,7 +71,6 @@ You can use [jupyterhub configurable-http-proxy docker
image](https://hub.docker.com/r/jupyterhub/configurable-http-proxy/)
to run the proxy.
## See also
* [jupyterhub configurable-http-proxy](https://github.com/jupyterhub/configurable-http-proxy)
- [jupyterhub configurable-http-proxy](https://github.com/jupyterhub/configurable-http-proxy)

332
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View File

@@ -0,0 +1,332 @@
# Starting servers with the JupyterHub API
Sometimes, when working with applications such as [BinderHub](https://binderhub.readthedocs.io), it may be necessary to launch Jupyter-based services on behalf of your users.
Doing so can be achieved through JupyterHub's [REST API](../reference/rest.md), which allows one to launch and manage servers on behalf of users through API calls instead of the JupyterHub UI.
This way, you can take advantage of other user/launch/lifecycle patterns that are not natively supported by the JupyterHub UI, all without the need to develop the server management features of JupyterHub Spawners and/or Authenticators.
This tutorial goes through working with the JupyterHub API to manage servers for users.
In particular, it covers how to:
1. [Check the status of servers](checking)
2. [Start servers](starting)
3. [Wait for servers to be ready](waiting)
4. [Communicate with servers](communicating)
5. [Stop servers](stopping)
At the end, we also provide sample Python code that can be used to implement these steps.
(checking)=
## Checking server status
First, request information about a particular user using a GET request:
```
GET /hub/api/users/:username
```
The response you get will include a `servers` field, which is a dictionary, as shown in this JSON-formatted response:
**Required scope: `read:servers`**
```json
{
"admin": false,
"groups": [],
"pending": null,
"server": null,
"name": "test-1",
"kind": "user",
"last_activity": "2021-08-03T18:12:46.026411Z",
"created": "2021-08-03T18:09:59.767600Z",
"roles": ["user"],
"servers": {}
}
```
Many JupyterHub deployments only use a 'default' server, represented as an empty string `''` for a name. An investigation of the `servers` field can yield one of two results. First, it can be empty as in the sample JSON response above. In such a case, the user has no running servers.
However, should the user have running servers, then the returned dict should contain various information, as shown in this response:
```json
"servers": {
"": {
"name": "",
"last_activity": "2021-08-03T18:48:35.934000Z",
"started": "2021-08-03T18:48:29.093885Z",
"pending": null,
"ready": true,
"url": "/user/test-1/",
"user_options": {},
"progress_url": "/hub/api/users/test-1/server/progress"
}
}
```
Key properties of a server:
name
: the server's name. Always the same as the key in `servers`.
ready
: boolean. If true, the server can be expected to respond to requests at `url`.
pending
: `null` or a string indicating a transitional state (such as `start` or `stop`).
Will always be `null` if `ready` is true or a string if false.
url
: The server's url path (e.g. `/users/:name/:servername/`) where the server can be accessed if `ready` is true.
progress_url
: The API URL path (starting with `/hub/api`) where the progress API can be used to wait for the server to be ready.
last_activity
: ISO8601 timestamp indicating when activity was last observed on the server.
started
: ISO801 timestamp indicating when the server was last started.
The two responses above are from a user with no servers and another with one `ready` server. The sample below is a response likely to be received when one requests a server launch while the server is not yet ready:
```json
"servers": {
"": {
"name": "",
"last_activity": "2021-08-03T18:48:29.093885Z",
"started": "2021-08-03T18:48:29.093885Z",
"pending": "spawn",
"ready": false,
"url": "/user/test-1/",
"user_options": {},
"progress_url": "/hub/api/users/test-1/server/progress"
}
}
```
Note that `ready` is `false` and `pending` has the value `spawn`, meaning that the server is not ready and attempting to access it may not work as it is still in the process of spawning. We'll get more into this below in [waiting for a server][].
[waiting for a server]: waiting
(starting)=
## Starting servers
To start a server, make this API request:
```
POST /hub/api/users/:username/servers/[:servername]
```
**Required scope: `servers`**
Assuming the request was valid, there are two possible responses:
201 Created
: This status code means the launch completed and the server is ready and is available at the server's URL immediately.
202 Accepted
: This is the more likely response, and means that the server has begun launching,
but is not immediately ready. As a result, the server shows `pending: 'spawn'` at this point and you should wait for it to start.
(waiting)=
## Waiting for a server to start
After receiving a `202 Accepted` response, you have to wait for the server to start.
Two approaches can be applied to establish when the server is ready:
1. {ref}`Polling the server model <polling>`
2. {ref}`Using the progress API <progress>`
(polling)=
### Polling the server model
The simplest way to check if a server is ready is to programmatically query the server model until two conditions are true:
1. The server name is contained in the `servers` response, and
2. `servers['servername']['ready']` is true.
The Python code snippet below can be used to check if a server is ready:
```python
def server_ready(hub_url, user, server_name="", token):
r = requests.get(
f"{hub_url}/hub/api/users/{user}/servers/{server_name}",
headers={"Authorization": f"token {token}"},
)
r.raise_for_status()
user_model = r.json()
servers = user_model.get("servers", {})
if server_name not in servers:
return False
server = servers[server_name]
if server['ready']:
print(f"Server {user}/{server_name} ready at {server['url']}")
return True
else:
print(f"Server {user}/{server_name} not ready, pending {server['pending']}")
return False
```
You can keep making this check until `ready` is true.
(progress)=
### Using the progress API
The most _efficient_ way to wait for a server to start is by using the progress API.
The progress URL is available in the server model under `progress_url` and has the form `/hub/api/users/:user/servers/:servername/progress`.
The default server progress can be accessed at `:user/servers//progress` or `:user/server/progress` as demonstrated in the following GET request:
```
GET /hub/api/users/:user/servers/:servername/progress
```
**Required scope: `read:servers`**
The progress API is an example of an [EventStream][] API.
Messages are _streamed_ and delivered in the form:
```
data: {"progress": 10, "message": "...", ...}
```
where the line after `data:` contains a JSON-serialized dictionary.
Lines that do not start with `data:` should be ignored.
[eventstream]: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#examples
Progress events have the form:
```python
{
"progress": 0-100,
"message": "",
"ready": True, # or False
}
```
progress
: integer, 0-100
message
: string message describing progress stages
ready
: present and true only for the last event when the server is ready
url
: only present if `ready` is true; will be the server's URL
The progress API can be used even with fully ready servers.
If the server is ready, there will only be one event, which will look like:
```json
{
"progress": 100,
"ready": true,
"message": "Server ready at /user/test-1/",
"html_message": "Server ready at <a href=\"/user/test-1/\">/user/test-1/</a>",
"url": "/user/test-1/"
}
```
where `ready` and `url` are the same as in the server model, and `ready` will always be true.
A significant advantage of the progress API is that it shows the status of the server through a stream of messages.
Below is an example of a typical complete stream from the API:
```
data: {"progress": 0, "message": "Server requested"}
data: {"progress": 50, "message": "Spawning server..."}
data: {"progress": 100, "ready": true, "message": "Server ready at /user/test-user/", "html_message": "Server ready at <a href=\"/user/test-user/\">/user/test-user/</a>", "url": "/user/test-user/"}
```
Here is a Python example for consuming an event stream:
```{literalinclude} ../../../examples/server-api/start-stop-server.py
:language: python
:pyobject: event_stream
```
(stopping)=
## Stopping servers
Servers can be stopped with a DELETE request:
```
DELETE /hub/api/users/:user/servers/[:servername]
```
**Required scope: `servers`**
Similar to when starting a server, issuing the DELETE request above might not stop the server immediately.
Instead, the DELETE request has two possible response codes:
204 Deleted
: This status code means the delete completed and the server is fully stopped.
It will now be absent from the user `servers` model.
202 Accepted
: This code means your request was accepted but is not yet completely processed.
The server has `pending: 'stop'` at this point.
There is no progress API for checking when a server actually stops.
The only way to wait for a server to stop is to poll it and wait for the server to disappear from the user `servers` model.
This Python code snippet can be used to stop a server and the wait for the process to complete:
```{literalinclude} ../../../examples/server-api/start-stop-server.py
:language: python
:pyobject: stop_server
```
(communicating)=
## Communicating with servers
JupyterHub tokens with the `access:servers` scope can be used to communicate with servers themselves.
The tokens can be the same as those you used to launch your service.
```{note}
Access scopes are new in JupyterHub 2.0.
To access servers in JupyterHub 1.x,
a token must be owned by the same user as the server,
*or* be an admin token if admin_access is enabled.
```
The URL returned from a server model is the URL path suffix,
e.g. `/user/:name/` to append to the jupyterhub base URL.
The returned URL is of the form `{hub_url}{server_url}`,
where `hub_url` would be `http://127.0.0.1:8000` by default and `server_url` is `/user/myname`.
When combined, the two give a full URL of `http://127.0.0.1:8000/user/myname`.
## Python example
The JupyterHub repo includes a complete example in {file}`examples/server-api`
that ties all theses steps together.
In summary, the processes involved in managing servers on behalf of users are:
1. Get user information from `/user/:name`.
2. The server model includes a `ready` state to tell you if it's ready.
3. If it's not ready, you can follow up with `progress_url` to wait for it.
4. If it is ready, you can use the `url` field to link directly to the running server.
The example below demonstrates starting and stopping servers via the JupyterHub API,
including waiting for them to start via the progress API and waiting for them to stop by polling the user model.
```{literalinclude} ../../../examples/server-api/start-stop-server.py
:language: python
:start-at: def event_stream
:end-before: def main
```

296
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View File

@@ -1,17 +1,5 @@
# Services
With version 0.7, JupyterHub adds support for **Services**.
This section provides the following information about Services:
- [Definition of a Service](#definition-of-a-service)
- [Properties of a Service](#properties-of-a-service)
- [Hub-Managed Services](#hub-managed-services)
- [Launching a Hub-Managed Service](#launching-a-hub-managed-service)
- [Externally-Managed Services](#externally-managed-services)
- [Writing your own Services](#writing-your-own-services)
- [Hub Authentication and Services](#hub-authentication-and-services)
## Definition of a Service
When working with JupyterHub, a **Service** is defined as a process that interacts
@@ -45,17 +33,25 @@ A Service may have the following properties:
- `url: str (default - None)` - The URL where the service is/should be. If a
url is specified for where the Service runs its own web server,
the service will be added to the proxy at `/services/:name`
- `api_token: str (default - None)` - For Externally-Managed Services you need to specify
- `api_token: str (default - None)` - For Externally-Managed Services you need to specify
an API token to perform API requests to the Hub
- `display: bool (default - True)` - When set to true, display a link to the
service's URL under the 'Services' dropdown in user's hub home page.
- `oauth_no_confirm: bool (default - False)` - When set to true,
skip the OAuth confirmation page when users access this service.
By default, when users authenticate with a service using JupyterHub,
they are prompted to confirm that they want to grant that service
access to their credentials.
Skipping the confirmation page is useful for admin-managed services that are considered part of the Hub
and shouldn't need extra prompts for login.
If a service is also to be managed by the Hub, it has a few extra options:
- `command: (str/Popen list`) - Command for JupyterHub to spawn the service.
- Only use this if the service should be a subprocess.
- If command is not specified, the Service is assumed to be managed
externally.
- If a command is specified for launching the Service, the Service will
be started and managed by the Hub.
- `command: (str/Popen list)` - Command for JupyterHub to spawn the service. - Only use this if the service should be a subprocess. - If command is not specified, the Service is assumed to be managed
externally. - If a command is specified for launching the Service, the Service will
be started and managed by the Hub.
- `environment: dict` - additional environment variables for the Service.
- `user: str` - the name of a system user to manage the Service. If
unspecified, run as the same user as the Hub.
@@ -89,11 +85,21 @@ Hub-Managed Service would include:
This example would be configured as follows in `jupyterhub_config.py`:
```python
c.JupyterHub.load_roles = [
{
"name": "idle-culler",
"scopes": [
"read:users:activity", # read user last_activity
"servers", # start and stop servers
# 'admin:users' # needed if culling idle users as well
]
}
]
c.JupyterHub.services = [
{
'name': 'cull-idle',
'admin': True,
'command': [sys.executable, '/path/to/cull-idle.py', '--timeout']
'name': 'idle-culler',
'command': [sys.executable, '-m', 'jupyterhub_idle_culler', '--timeout=3600']
}
]
```
@@ -103,12 +109,14 @@ parameters, which describe the environment needed to start the Service process:
- `environment: dict` - additional environment variables for the Service.
- `user: str` - name of the user to run the server if different from the Hub.
Requires Hub to be root.
Requires Hub to be root.
- `cwd: path` directory in which to run the Service, if different from the
Hub directory.
Hub directory.
The Hub will pass the following environment variables to launch the Service:
(service-env)=
```bash
JUPYTERHUB_SERVICE_NAME: The name of the service
JUPYTERHUB_API_TOKEN: API token assigned to the service
@@ -117,21 +125,24 @@ JUPYTERHUB_BASE_URL: Base URL of the Hub (https://mydomain[:port]/)
JUPYTERHUB_SERVICE_PREFIX: URL path prefix of this service (/services/:service-name/)
JUPYTERHUB_SERVICE_URL: Local URL where the service is expected to be listening.
Only for proxied web services.
JUPYTERHUB_OAUTH_SCOPES: JSON-serialized list of scopes to use for allowing access to the service
(deprecated in 3.0, use JUPYTERHUB_OAUTH_ACCESS_SCOPES).
JUPYTERHUB_OAUTH_ACCESS_SCOPES: JSON-serialized list of scopes to use for allowing access to the service (new in 3.0).
JUPYTERHUB_OAUTH_CLIENT_ALLOWED_SCOPES: JSON-serialized list of scopes that can be requested by the oauth client on behalf of users (new in 3.0).
```
For the previous 'cull idle' Service example, these environment variables
would be passed to the Service when the Hub starts the 'cull idle' Service:
```bash
JUPYTERHUB_SERVICE_NAME: 'cull-idle'
JUPYTERHUB_SERVICE_NAME: 'idle-culler'
JUPYTERHUB_API_TOKEN: API token assigned to the service
JUPYTERHUB_API_URL: http://127.0.0.1:8080/hub/api
JUPYTERHUB_BASE_URL: https://mydomain[:port]
JUPYTERHUB_SERVICE_PREFIX: /services/cull-idle/
JUPYTERHUB_SERVICE_PREFIX: /services/idle-culler/
```
See the JupyterHub GitHub repo for additional information about the
[`cull-idle` example](https://github.com/jupyterhub/jupyterhub/tree/master/examples/cull-idle).
See the GitHub repo for additional information about the [jupyterhub_idle_culler][].
## Externally-Managed Services
@@ -151,6 +162,8 @@ c.JupyterHub.services = [
{
'name': 'my-web-service',
'url': 'https://10.0.1.1:1984',
# any secret >8 characters, you'll use api_token to
# authenticate api requests to the hub from your service
'api_token': 'super-secret',
}
]
@@ -173,7 +186,7 @@ information to the Service via the environment variables described above. A
flexible Service, whether managed by the Hub or not, can make use of these
same environment variables.
When you run a service that has a url, it will be accessible under a
When you run a service that has a URL, it will be accessible under a
`/services/` prefix, such as `https://myhub.horse/services/my-service/`. For
your service to route proxied requests properly, it must take
`JUPYTERHUB_SERVICE_PREFIX` into account when routing requests. For example, a
@@ -188,18 +201,38 @@ extra slash you might get unexpected behavior. For example if your service has a
## Hub Authentication and Services
JupyterHub 0.7 introduces some utilities for using the Hub's authentication
mechanism to govern access to your service. When a user logs into JupyterHub,
the Hub sets a **cookie (`jupyterhub-services`)**. The service can use this
cookie to authenticate requests.
JupyterHub provides some utilities for using the Hub's authentication
mechanism to govern access to your service.
JupyterHub ships with a reference implementation of Hub authentication that
Requests to all JupyterHub services are made with OAuth tokens.
These can either be requests with a token in the `Authorization` header,
or url parameter `?token=...`,
or browser requests which must complete the OAuth authorization code flow,
which results in a token that should be persisted for future requests
(persistence is up to the service,
but an encrypted cookie confined to the service path is appropriate,
and provided by default).
:::{versionchanged} 2.0
The shared `jupyterhub-services` cookie is removed.
OAuth must be used to authenticate browser requests with services.
:::
JupyterHub includes a reference implementation of Hub authentication that
can be used by services. You may go beyond this reference implementation and
create custom hub-authenticating clients and services. We describe the process
below.
The reference, or base, implementation is the [`HubAuth`][HubAuth] class,
which implements the requests to the Hub.
The reference, or base, implementation is the {class}`.HubAuth` class,
which implements the API requests to the Hub that resolve a token to a User model.
There are two levels of authentication with the Hub:
- {class}`.HubAuth` - the most basic authentication,
for services that should only accept API requests authorized with a token.
- {class}`.HubOAuth` - For services that should use oauth to authenticate with the Hub.
This should be used for any service that serves pages that should be visited with a browser.
To use HubAuth, you must set the `.api_token` instance variable. This can be
done either programmatically when constructing the class, or via the
@@ -214,11 +247,9 @@ and [service-whoiami](https://github.com/jupyterhub/jupyterhub/tree/master/examp
(TODO: Where is this API TOKen set?)
Most of the logic for authentication implementation is found in the
[`HubAuth.user_for_cookie`][HubAuth.user_for_cookie]
and in the
[`HubAuth.user_for_token`][HubAuth.user_for_token]
methods, which makes a request of the Hub, and returns:
Most of the logic for authentication implementation is found in the
{meth}`.HubAuth.user_for_token` methods,
which makes a request of the Hub, and returns:
- None, if no user could be identified, or
- a dict of the following form:
@@ -227,7 +258,9 @@ methods, which makes a request of the Hub, and returns:
{
"name": "username",
"groups": ["list", "of", "groups"],
"admin": False, # or True
"scopes": [
"access:servers!server=username/",
],
}
```
@@ -237,79 +270,45 @@ action.
HubAuth also caches the Hub's response for a number of seconds,
configurable by the `cookie_cache_max_age` setting (default: five minutes).
If your service would like to make further requests _on behalf of users_,
it should use the token issued by this OAuth process.
If you are using tornado,
you can access the token authenticating the current request with {meth}`.HubAuth.get_token`.
:::{versionchanged} 2.2
{meth}`.HubAuth.get_token` adds support for retrieving
tokens stored in tornado cookies after the completion of OAuth.
Previously, it only retrieved tokens from URL parameters or the Authorization header.
Passing `get_token(handler, in_cookie=False)` preserves this behavior.
:::
### Flask Example
For example, you have a Flask service that returns information about a user.
JupyterHub's HubAuth class can be used to authenticate requests to the Flask
service. See the `service-whoami-flask` example in the
[JupyterHub GitHub repo](https://github.com/jupyterhub/jupyterhub/tree/master/examples/service-whoami-flask)
[JupyterHub GitHub repo](https://github.com/jupyterhub/jupyterhub/tree/HEAD/examples/service-whoami-flask)
for more details.
```python
from functools import wraps
import json
import os
from urllib.parse import quote
from flask import Flask, redirect, request, Response
from jupyterhub.services.auth import HubAuth
prefix = os.environ.get('JUPYTERHUB_SERVICE_PREFIX', '/')
auth = HubAuth(
api_token=os.environ['JUPYTERHUB_API_TOKEN'],
cookie_cache_max_age=60,
)
app = Flask(__name__)
def authenticated(f):
"""Decorator for authenticating with the Hub"""
@wraps(f)
def decorated(*args, **kwargs):
cookie = request.cookies.get(auth.cookie_name)
token = request.headers.get(auth.auth_header_name)
if cookie:
user = auth.user_for_cookie(cookie)
elif token:
user = auth.user_for_token(token)
else:
user = None
if user:
return f(user, *args, **kwargs)
else:
# redirect to login url on failed auth
return redirect(auth.login_url + '?next=%s' % quote(request.path))
return decorated
@app.route(prefix)
@authenticated
def whoami(user):
return Response(
json.dumps(user, indent=1, sort_keys=True),
mimetype='application/json',
)
```{literalinclude} ../../../examples/service-whoami-flask/whoami-flask.py
:language: python
```
### Authenticating tornado services with JupyterHub
Since most Jupyter services are written with tornado,
we include a mixin class, [`HubAuthenticated`][HubAuthenticated],
we include a mixin class, [`HubOAuthenticated`][huboauthenticated],
for quickly authenticating your own tornado services with JupyterHub.
Tornado's `@web.authenticated` method calls a Handler's `.get_current_user`
method to identify the user. Mixing in `HubAuthenticated` defines
`get_current_user` to use HubAuth. If you want to configure the HubAuth
instance beyond the default, you'll want to define an `initialize` method,
Tornado's {py:func}`~.tornado.web.authenticated` decorator calls a Handler's {py:meth}`~.tornado.web.RequestHandler.get_current_user`
method to identify the user. Mixing in {class}`.HubAuthenticated` defines
{meth}`~.HubAuthenticated.get_current_user` to use HubAuth. If you want to configure the HubAuth
instance beyond the default, you'll want to define an {py:meth}`~.tornado.web.RequestHandler.initialize` method,
such as:
```python
class MyHandler(HubAuthenticated, web.RequestHandler):
hub_users = {'inara', 'mal'}
class MyHandler(HubOAuthenticated, web.RequestHandler):
def initialize(self, hub_auth):
self.hub_auth = hub_auth
@@ -319,66 +318,97 @@ class MyHandler(HubAuthenticated, web.RequestHandler):
...
```
The HubAuth class will automatically load the desired configuration from the Service
[environment variables](service-env).
The HubAuth will automatically load the desired configuration from the Service
environment variables.
:::{versionchanged} 2.0
If you want to limit user access, you can whitelist users through either the
`.hub_users` attribute or `.hub_groups`. These are sets that check against the
username and user group list, respectively. If a user matches neither the user
list nor the group list, they will not be allowed access. If both are left
undefined, then any user will be allowed.
Access scopes are used to govern access to services.
Prior to 2.0,
sets of users and groups could be used to grant access
by defining `.hub_groups` or `.hub_users` on the authenticated handler.
These are ignored if the 2.0 `.hub_scopes` is defined.
:::
:::{seealso}
{meth}`.HubAuth.check_scopes`
:::
### Implementing your own Authentication with JupyterHub
If you don't want to use the reference implementation
(e.g. you find the implementation a poor fit for your Flask app),
you can implement authentication via the Hub yourself.
We recommend looking at the [`HubAuth`][HubAuth] class implementation for reference,
JupyterHub is a standard OAuth2 provider,
so you can use any OAuth 2 client implementation appropriate for your toolkit.
See the [FastAPI example][] for an example of using JupyterHub as an OAuth provider with [FastAPI][],
without using any code imported from JupyterHub.
On completion of OAuth, you will have an access token for JupyterHub,
which can be used to identify the user and the permissions (scopes)
the user has authorized for your service.
You will only get to this stage if the user has the required `access:services!service=$service-name` scope.
To retrieve the user model for the token, make a request to `GET /hub/api/user` with the token in the Authorization header.
For example, using flask:
```{literalinclude} ../../../examples/service-whoami-flask/whoami-flask.py
:language: python
```
We recommend looking at the [`HubOAuth`][huboauth] class implementation for reference,
and taking note of the following process:
1. retrieve the cookie `jupyterhub-services` from the request.
2. Make an API request `GET /hub/api/authorizations/cookie/jupyterhub-services/cookie-value`,
where cookie-value is the url-encoded value of the `jupyterhub-services` cookie.
This request must be authenticated with a Hub API token in the `Authorization` header.
For example, with [requests][]:
1. retrieve the token from the request.
2. Make an API request `GET /hub/api/user`,
with the token in the `Authorization` header.
```python
r = requests.get(
'/'.join((["http://127.0.0.1:8081/hub/api",
"authorizations/cookie/jupyterhub-services",
quote(encrypted_cookie, safe=''),
]),
headers = {
'Authorization' : 'token %s' % api_token,
},
)
r.raise_for_status()
user = r.json()
```
For example, with [requests][]:
```python
r = requests.get(
"http://127.0.0.1:8081/hub/api/user",
headers = {
'Authorization' : f'token {api_token}',
},
)
r.raise_for_status()
user = r.json()
```
3. On success, the reply will be a JSON model describing the user:
```json
```python
{
"name": "inara",
"groups": ["serenity", "guild"]
# groups may be omitted, depending on permissions
"groups": ["serenity", "guild"],
# scopes is new in JupyterHub 2.0
"scopes": [
"access:services",
"read:users:name",
"read:users!user=inara",
"..."
]
}
```
The `scopes` field can be used to manage access.
Note: a user will have access to a service to complete oauth access to the service for the first time.
Individual permissions may be revoked at any later point without revoking the token,
in which case the `scopes` field in this model should be checked on each access.
The default required scopes for access are available from `hub_auth.oauth_scopes` or `$JUPYTERHUB_OAUTH_ACCESS_SCOPES`.
An example of using an Externally-Managed Service and authentication is
in the [nbviewer README][nbviewer example] section on securing the notebook viewer,
and an example of its configuration is found [here](https://github.com/jupyter/nbviewer/blob/master/nbviewer/providers/base.py#L94).
and an example of its configuration is found [here](https://github.com/jupyter/nbviewer/blob/ed942b10a52b6259099e2dd687930871dc8aac22/nbviewer/providers/base.py#L95).
nbviewer can also be run as a Hub-Managed Service as described [nbviewer README][nbviewer example]
section on securing the notebook viewer.
[requests]: http://docs.python-requests.org/en/master/
[services_auth]: ../api/services.auth.html
[HubAuth]: ../api/services.auth.html#jupyterhub.services.auth.HubAuth
[HubAuth.user_for_cookie]: ../api/services.auth.html#jupyterhub.services.auth.HubAuth.user_for_cookie
[HubAuth.user_for_token]: ../api/services.auth.html#jupyterhub.services.auth.HubAuth.user_for_token
[HubAuthenticated]: ../api/services.auth.html#jupyterhub.services.auth.HubAuthenticated
[nbviewer example]: https://github.com/jupyter/nbviewer#securing-the-notebook-viewer
[fastapi example]: https://github.com/jupyterhub/jupyterhub/tree/HEAD/examples/service-fastapi
[fastapi]: https://fastapi.tiangolo.com
[jupyterhub_idle_culler]: https://github.com/jupyterhub/jupyterhub-idle-culler

182
docs/source/reference/spawners.md
View File

@@ -4,10 +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
@@ -15,11 +14,11 @@ Custom Spawners for JupyterHub can be found on the [JupyterHub wiki](https://git
Some examples include:
- [DockerSpawner](https://github.com/jupyterhub/dockerspawner) for spawning user servers in Docker containers
* `dockerspawner.DockerSpawner` for spawning identical Docker containers for
- `dockerspawner.DockerSpawner` for spawning identical Docker containers for
each user
* `dockerspawner.SystemUserSpawner` for spawning Docker containers with an
- `dockerspawner.SystemUserSpawner` for spawning Docker containers with an
environment and home directory for each user
* both `DockerSpawner` and `SystemUserSpawner` also work with Docker Swarm for
- both `DockerSpawner` and `SystemUserSpawner` also work with Docker Swarm for
launching containers on remote machines
- [SudoSpawner](https://github.com/jupyterhub/sudospawner) enables JupyterHub to
run without being root, by spawning an intermediate process via `sudo`
@@ -27,26 +26,25 @@ Some examples include:
servers using batch systems
- [YarnSpawner](https://github.com/jupyterhub/yarnspawner) for spawning notebook
servers in YARN containers on a Hadoop cluster
- [RemoteSpawner](https://github.com/zonca/remotespawner) to spawn notebooks
and a remote server and tunnel the port via SSH
- [SSHSpawner](https://github.com/NERSC/sshspawner) to spawn notebooks
on a remote server using SSH
- [KubeSpawner](https://github.com/jupyterhub/kubespawner) to spawn notebook servers on kubernetes cluster.
## Spawner control methods
### 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.
The return value of `Spawner.start` should be the (ip, port) of the running server.
**NOTE:** When writing coroutines, *never* `yield` in between a database change and a commit.
The return value of `Spawner.start` should be the `(ip, port)` of the running server,
or a full URL as a string.
Most `Spawner.start` functions will look similar to this example:
```python
def start(self):
async def start(self):
self.ip = '127.0.0.1'
self.port = random_port()
# get environment variables,
@@ -58,8 +56,10 @@ def start(self):
cmd.extend(self.cmd)
cmd.extend(self.get_args())
yield self._actually_start_server_somehow(cmd, env)
return (self.ip, self.port)
await self._actually_start_server_somehow(cmd, env)
# url may not match self.ip:self.port, but it could!
url = self._get_connectable_url()
return url
```
When `Spawner.start` returns, the single-user server process should actually be running,
@@ -67,20 +67,71 @@ not just requested. JupyterHub can handle `Spawner.start` being very slow
(such as PBS-style batch queues, or instantiating whole AWS instances)
via relaxing the `Spawner.start_timeout` config value.
#### Note on IPs and ports
`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_.
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}`,
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,
such as `ip = '0.0.0.0'` and a fixed port, e.g. `8888`.
The defaults can be changed in the class,
preserving configuration with traitlets:
```python
from traitlets import default
from jupyterhub.spawner import Spawner
class MySpawner(Spawner):
@default("ip")
def _default_ip(self):
return '0.0.0.0'
@default("port")
def _default_port(self):
return 8888
async def start(self):
env = self.get_env()
cmd = []
# get jupyterhub command to run,
# typically ['jupyterhub-singleuser']
cmd.extend(self.cmd)
cmd.extend(self.get_args())
remote_server_info = await self._actually_start_server_somehow(cmd, env)
url = self.get_public_url_from(remote_server_info)
return url
```
#### Exception handling
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.
Alternatively `.jupyterhub_message` is rendered as unformatted text.
If both attributes are not present, the Exception will be shown to the user as unformatted text.
### 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)`
to check if the local process is still running.
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
`Spawner.stop` should stop the process. It must be a tornado coroutine, which should return when the process has finished exiting.
## Spawner state
JupyterHub should be able to stop and restart without tearing down
@@ -90,7 +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):
@@ -112,7 +163,6 @@ def clear_state(self):
self.pid = 0
```
## Spawner options form
(new in 0.4)
@@ -129,7 +179,7 @@ If the `Spawner.options_form` is defined, when a user tries to start their serve
If `Spawner.options_form` is undefined, the user's server is spawned directly, and no spawn page is rendered.
See [this example](https://github.com/jupyterhub/jupyterhub/blob/master/examples/spawn-form/jupyterhub_config.py) for a form that allows custom CLI args for the local spawner.
See [this example](https://github.com/jupyterhub/jupyterhub/blob/HEAD/examples/spawn-form/jupyterhub_config.py) for a form that allows custom CLI args for the local spawner.
### `Spawner.options_from_form`
@@ -170,8 +220,7 @@ which would return:
When `Spawner.start` is called, this dictionary is accessible as `self.user_options`.
[Spawner]: https://github.com/jupyterhub/jupyterhub/blob/master/jupyterhub/spawner.py
[spawner]: https://github.com/jupyterhub/jupyterhub/blob/HEAD/jupyterhub/spawner.py
## Writing a custom spawner
@@ -212,6 +261,75 @@ Additionally, configurable attributes for your spawner will
appear in jupyterhub help output and auto-generated configuration files
via `jupyterhub --generate-config`.
## Environment variables and command-line arguments
Spawners mainly do one thing: launch a command in an environment.
The command-line is constructed from user configuration:
- Spawner.cmd (default: `['jupyterhub-singleuser']`)
- Spawner.args (CLI args to pass to the cmd, default: empty)
where the configuration:
```python
c.Spawner.cmd = ["my-singleuser-wrapper"]
c.Spawner.args = ["--debug", "--flag"]
```
would result in spawning the command:
```bash
my-singleuser-wrapper --debug --flag
```
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,
such as `--ip={Spawner.ip}` and `--port={Spawner.port}`.
These have now all been moved to environment variables,
and from JupyterHub 2.0,
the command-line launched by JupyterHub is fully specified by overridable configuration `Spawner.cmd + Spawner.args`.
Most process configuration is passed via environment variables.
Additional variables can be specified via the `Spawner.environment` configuration.
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_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_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_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_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)
- 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.
## Spawners, resource limits, and guarantees (Optional)
@@ -220,14 +338,14 @@ 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`](https://github.com/jupyterhub/systemdspawner).
### Memory Limits & Guarantees
`c.Spawner.mem_limit`: A **limit** specifies the *maximum amount of memory*
`c.Spawner.mem_limit`: A **limit** specifies the _maximum amount of memory_
that may be allocated, though there is no promise that the maximum amount will
be available. In supported spawners, you can set `c.Spawner.mem_limit` to
limit the total amount of memory that a single-user notebook server can
@@ -235,8 +353,8 @@ allocate. Attempting to use more memory than this limit will cause errors. The
single-user notebook server can discover its own memory limit by looking at
the environment variable `MEM_LIMIT`, which is specified in absolute bytes.
`c.Spawner.mem_guarantee`: Sometimes, a **guarantee** of a *minimum amount of
memory* is desirable. In this case, you can set `c.Spawner.mem_guarantee` to
`c.Spawner.mem_guarantee`: Sometimes, a **guarantee** of a _minimum amount of
memory_ is desirable. In this case, you can set `c.Spawner.mem_guarantee` to
to provide a guarantee that at minimum this much memory will always be
available for the single-user notebook server to use. The environment variable
`MEM_GUARANTEE` will also be set in the single-user notebook server.
@@ -250,7 +368,7 @@ 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.
@@ -271,7 +389,7 @@ utilize these certs, there are two methods of interest on the base `Spawner`
class: `.create_certs` and `.move_certs`.
The first method, `.create_certs` will sign a key-cert pair using an internally
trusted authority for notebooks. During this process, `.create_certs` can
trusted authority for notebooks. During this process, `.create_certs` can
apply `ip` and `dns` name information to the cert via an `alt_names` `kwarg`.
This is used for certificate authentication (verification). Without proper
verification, the `Notebook` will be unable to communicate with the `Hub` and

43
docs/source/reference/technical-overview.md
View File

@@ -2,7 +2,7 @@
The **Technical Overview** section gives you a high-level view of:
- JupyterHub's Subsystems: Hub, Proxy, Single-User Notebook Server
- JupyterHub's major Subsystems: Hub, Proxy, Single-User Notebook Server
- how the subsystems interact
- the process from JupyterHub access to user login
- JupyterHub's default behavior
@@ -11,16 +11,16 @@ The **Technical Overview** section gives you a high-level view of:
The goal of this section is to share a deeper technical understanding of
JupyterHub and how it works.
## The Subsystems: Hub, Proxy, Single-User Notebook Server
## The Major Subsystems: Hub, Proxy, Single-User Notebook Server
JupyterHub is a set of processes that together provide a single user Jupyter
Notebook server for each person in a group. Three major subsystems are started
JupyterHub is a set of processes that together, provide a single-user Jupyter
Notebook server for each person in a group. Three subsystems are started
by the `jupyterhub` command line program:
- **Hub** (Python/Tornado): manages user accounts, authentication, and
coordinates Single User Notebook Servers using a Spawner.
coordinates Single User Notebook Servers using a [Spawner](./spawners.md).
- **Proxy**: the public facing part of JupyterHub that uses a dynamic proxy
- **Proxy**: the public-facing part of JupyterHub that uses a dynamic proxy
to route HTTP requests to the Hub and Single User Notebook Servers.
[configurable http proxy](https://github.com/jupyterhub/configurable-http-proxy)
(node-http-proxy) is the default proxy.
@@ -28,7 +28,7 @@ by the `jupyterhub` command line program:
- **Single-User Notebook Server** (Python/Tornado): a dedicated,
single-user, Jupyter Notebook server is started for each user on the system
when the user logs in. The object that starts the single-user notebook
servers is called a **Spawner**.
servers is called a **[Spawner](./spawners.md)**.
![JupyterHub subsystems](../images/jhub-parts.png)
@@ -41,8 +41,8 @@ The basic principles of operation are:
- The Hub spawns the proxy (in the default JupyterHub configuration)
- The proxy forwards all requests to the Hub by default
- The Hub handles login, and spawns single-user notebook servers on demand
- The Hub configures the proxy to forward url prefixes to single-user notebook
- The Hub handles login and spawns single-user notebook servers on demand
- The Hub configures the proxy to forward URL prefixes to single-user notebook
servers
The proxy is the only process that listens on a public interface. The Hub sits
@@ -50,17 +50,16 @@ behind the proxy at `/hub`. Single-user servers sit behind the proxy at
`/user/[username]`.
Different **[authenticators](./authenticators.md)** control access
to JupyterHub. The default one (PAM) uses the user accounts on the server where
to JupyterHub. The default one [(PAM)](https://en.wikipedia.org/wiki/Pluggable_authentication_module) uses the user accounts on the server where
JupyterHub is running. If you use this, you will need to create a user account
on the system for each user on your team. Using other authenticators, you can
on the system for each user on your team. However, using other authenticators you can
allow users to sign in with e.g. a GitHub account, or with any single-sign-on
system your organization has.
Next, **[spawners](./spawners.md)** control how JupyterHub starts
the individual notebook server for each user. The default spawner will
start a notebook server on the same machine running under their system username.
The other main option is to start each server in a separate container, often
using Docker.
The other main option is to start each server in a separate container, often using [Docker](https://jupyterhub-dockerspawner.readthedocs.io/en/latest/).
## The Process from JupyterHub Access to User Login
@@ -72,20 +71,20 @@ When a user accesses JupyterHub, the following events take place:
- A single-user notebook server instance is [spawned](./spawners.md) for the
logged-in user
- When the single-user notebook server starts, the proxy is notified to forward
requests to `/user/[username]/*` to the single-user notebook server.
- A cookie is set on `/hub/`, containing an encrypted token. (Prior to version
requests made to `/user/[username]/*`, to the single-user notebook server.
- A [cookie](https://en.wikipedia.org/wiki/HTTP_cookie) is set on `/hub/`, containing an encrypted token. (Prior to version
0.8, a cookie for `/user/[username]` was used too.)
- The browser is redirected to `/user/[username]`, and the request is handled by
the single-user notebook server.
The single-user server identifies the user with the Hub via OAuth:
How does the single-user server identify the user with the Hub via OAuth?
- on request, the single-user server checks a cookie
- if no cookie is set, redirect to the Hub for verification via OAuth
- after verification at the Hub, the browser is redirected back to the
- On request, the single-user server checks a cookie
- If no cookie is set, the single-user server redirects to the Hub for verification via OAuth
- After verification at the Hub, the browser is redirected back to the
single-user server
- the token is verified and stored in a cookie
- if no user is identified, the browser is redirected back to `/hub/login`
- The token is verified and stored in a cookie
- If no user is identified, the browser is redirected back to `/hub/login`
## Default Behavior
@@ -111,7 +110,7 @@ working directory:
This file needs to persist so that a **Hub** server restart will avoid
invalidating cookies. Conversely, deleting this file and restarting the server
effectively invalidates all login cookies. The cookie secret file is discussed
in the [Cookie Secret section of the Security Settings document](../getting-started/security-basics.md).
in the [Cookie Secret section of the Security Settings document](../getting-started/security-basics.rst).
The location of these files can be specified via configuration settings. It is
recommended that these files be stored in standard UNIX filesystem locations,

33
docs/source/reference/templates.md
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@@ -1,8 +1,8 @@
# Working with templates and UI
The pages of the JupyterHub application are generated from
[Jinja](http://jinja.pocoo.org/) templates. These allow the header, for
example, to be defined once and incorporated into all pages. By providing
[Jinja](http://jinja.pocoo.org/) templates. These allow the header, for
example, to be defined once and incorporated into all pages. By providing
your own templates, you can have complete control over JupyterHub's
appearance.
@@ -10,7 +10,7 @@ appearance.
JupyterHub will look for custom templates in all of the paths in the
`JupyterHub.template_paths` configuration option, falling back on the
[default templates](https://github.com/jupyterhub/jupyterhub/tree/master/share/jupyterhub/templates)
[default templates](https://github.com/jupyterhub/jupyterhub/tree/HEAD/share/jupyterhub/templates)
if no custom template with that name is found. This fallback
behavior is new in version 0.9; previous versions searched only those paths
explicitly included in `template_paths`. You may override as many
@@ -20,8 +20,8 @@ or as few templates as you desire.
Jinja provides a mechanism to [extend templates](http://jinja.pocoo.org/docs/2.10/templates/#template-inheritance).
A base template can define a `block`, and child templates can replace or
supplement the material in the block. The
[JupyterHub templates](https://github.com/jupyterhub/jupyterhub/tree/master/share/jupyterhub/templates)
supplement the material in the block. The
[JupyterHub templates](https://github.com/jupyterhub/jupyterhub/tree/HEAD/share/jupyterhub/templates)
make extensive use of blocks, which allows you to customize parts of the
interface easily.
@@ -32,8 +32,8 @@ In general, a child template can extend a base template, `page.html`, by beginni
```
This works, unless you are trying to extend the default template for the same
file name. Starting in version 0.9, you may refer to the base file with a
`templates/` prefix. Thus, if you are writing a custom `page.html`, start the
file name. Starting in version 0.9, you may refer to the base file with a
`templates/` prefix. Thus, if you are writing a custom `page.html`, start the
file with this block:
```html
@@ -41,7 +41,7 @@ file with this block:
```
By defining `block`s with same name as in the base template, child templates
can replace those sections with custom content. The content from the base
can replace those sections with custom content. The content from the base
template can be included with the `{{ super() }}` directive.
### Example
@@ -52,10 +52,7 @@ text about the server starting up, place this content in a file named
`JupyterHub.template_paths` configuration option.
```html
{% extends "templates/spawn_pending.html" %}
{% block message %}
{{ super() }}
{% extends "templates/spawn_pending.html" %} {% block message %} {{ super() }}
<p>Patience is a virtue.</p>
{% endblock %}
```
@@ -69,9 +66,8 @@ To add announcements to be displayed on a page, you have two options:
### Announcement Configuration Variables
If you set the configuration variable `JupyterHub.template_vars =
{'announcement': 'some_text'}`, the given `some_text` will be placed on
the top of all pages. The more specific variables
If you set the configuration variable `JupyterHub.template_vars = {'announcement': 'some_text'}`, the given `some_text` will be placed on
the top of all pages. The more specific variables
`announcement_login`, `announcement_spawn`, `announcement_home`, and
`announcement_logout` are more specific and only show on their
respective pages (overriding the global `announcement` variable).
@@ -79,15 +75,14 @@ Note that changing these variables require a restart, unlike direct
template extension.
You can get the same effect by extending templates, which allows you
to update the messages without restarting. Set
to update the messages without restarting. Set
`c.JupyterHub.template_paths` as mentioned above, and then create a
template (for example, `login.html`) with:
```html
{% extends "templates/login.html" %}
{% set announcement = 'some message' %}
{% extends "templates/login.html" %} {% set announcement = 'some message' %}
```
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).

104
docs/source/reference/urls.md
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@@ -2,17 +2,15 @@
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.
## `/`
The top-level request is always a simple redirect to `/hub/`,
@@ -27,12 +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:
@@ -42,7 +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:
@@ -59,42 +57,42 @@ 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.
_Version added: 1.0_ named server UI is new in 1.0.
## `/hub/login`
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
@@ -107,11 +105,11 @@ 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/...`
If the user's server is _not_ running, this will be redirected to `/hub/user/:username/...`
## `/hub/user/:username[/:servername]`
@@ -120,8 +118,14 @@ This URL indicates a request for a user server that is not running
if the specified server were running).
Handling this URL depends on two conditions: whether a requested user is found
as a match and the state of the requested user's notebook server.
as a match and the state of the requested user's notebook server,
for example:
1. the server is not active
a. user matches
b. user doesn't match
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`
@@ -137,39 +141,37 @@ 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*
_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*
_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`
@@ -191,7 +193,7 @@ 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/...`.
@@ -202,12 +204,12 @@ and a POST request will trigger the actual spawn and redirect.
![The spawn form](../images/spawn-form.png)
*Version added: 1.0*
_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*
_Version changed: 1.0_
Prior to 1.0, this page redirected back to `/hub/user/:username`,
which was responsible for triggering spawn and rendering progress, etc.
@@ -216,7 +218,7 @@ which was responsible for triggering spawn and rendering progress, etc.
![The spawn pending page](../images/spawn-pending.png)
*Version added: 1.0* this URL is new in JupyterHub 1.0.
_Version added: 1.0_ this URL is new in JupyterHub 1.0.
This page renders the progress view for the given spawn request.
Once the server is ready,
@@ -244,7 +246,7 @@ 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

47
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@@ -5,24 +5,24 @@ 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.
## Semi-trusted and untrusted users
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
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
[*proxy*][configurable-http-proxy]. If the Hub is serving untrusted
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
[_proxy_][configurable-http-proxy]. If the Hub is serving untrusted
users, many of the web's cross-site protections are not applied between
single-user servers and the Hub, or between single-user servers and each
other, since browsers see the whole thing (proxy, Hub, and single user
@@ -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.
@@ -40,25 +40,25 @@ server.
To protect all users from each other, JupyterHub administrators must
ensure that:
* A user **does not have permission** to modify their single-user notebook server,
- A user **does not have permission** to modify their single-user notebook server,
including:
- A user **may not** install new packages in the Python environment that runs
their single-user server.
- 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
- A user **may not** modify the configuration of the notebook server
(the `~/.jupyter` or `JUPYTER_CONFIG_DIR` directory).
If any additional services are run on the same domain as the Hub, the services
**must never** display user-authored HTML that is neither *sanitized* nor *sandboxed*
**must never** display user-authored HTML that is neither _sanitized_ nor _sandboxed_
(e.g. IFramed) to any user that lacks authentication as the author of a file.
## 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
@@ -76,16 +76,16 @@ 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
For most Spawners, `PATH` is not something users can influence, but care should
be taken to ensure that the Spawner does *not* evaluate shell configuration
be taken to ensure that the Spawner does _not_ evaluate shell configuration
files prior to launching the server.
### Isolate packages using virtualenv
@@ -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).
@@ -119,19 +119,18 @@ 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).
[configurable-http-proxy]: https://github.com/jupyterhub/configurable-http-proxy
## 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@iypthon.org). If you prefer to encrypt
[security@ipython.org](mailto:security@ipython.org). If you prefer to encrypt
your security reports, you can use [this PGP public
key](https://jupyter-notebook.readthedocs.io/en/stable/_downloads/ipython_security.asc).

235
docs/source/troubleshooting.md
View File

@@ -1,32 +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, whitelist,
group whitelist) 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
@@ -34,12 +11,12 @@ problem and how to resolve it.
If you have tried to start the JupyterHub proxy and it fails to start:
- check if the JupyterHub IP configuration setting is
``c.JupyterHub.ip = '*'``; if it is, try ``c.JupyterHub.ip = ''``
- Try starting with ``jupyterhub --ip=0.0.0.0``
`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
@@ -55,32 +32,62 @@ or add:
to the config file, `jupyterhub_config.py`.
### What is the default behavior when none of the lists (admin, whitelist, group whitelist) are set?
### 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
a password) will be allowed to start a server. The whitelist lets you limit
this to a particular set of users, and the admin_users lets you specify who
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 userlist 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
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:
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,
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?
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.
How can I kill the processes that are using these ports?
Run the following command:
sudo kill -9 $(sudo lsof -t -i:<service_port>)
Where `<service_port>` is the port used by the nbgrader course service. This configuration is specified in `jupyterhub_config.py`.
### Why am I getting a Spawn failed error message?
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 environment variables and virtualenv location?
When launching JupyterHub with `sudo jupyterhub` I get import errors and my environment variables don't work.
When launching services with `sudo ...` the shell won't have the same environment variables or `PATH`s in place. The most direct way to solve this issue is to use the full path to your python environment and add environment variables. For example:
```bash
sudo MY_ENV=abc123 \
/home/foo/venv/bin/python3 \
/srv/jupyterhub/jupyterhub
```
## 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.
@@ -88,11 +95,11 @@ There are two likely reasons for this:
1. The single-user server cannot connect to the Hub's API (networking
configuration problems)
2. The single-user server cannot *authenticate* its requests (invalid token)
2. The single-user server cannot _authenticate_ its requests (invalid token)
#### Symptoms
The main symptom is a failure to load *any* page served by the single-user
The main symptom is a failure to load _any_ page served by the single-user
server, met with a 500 error. This is typically the first page at `/user/<your_name>`
after logging in or clicking "Start my server". When a single-user notebook server
receives a request, the notebook server makes an API request to the Hub to
@@ -108,7 +115,7 @@ You should see a similar 200 message, as above, in the Hub log when you first
visit your single-user notebook server. If you don't see this message in the log, it
may mean that your single-user notebook server isn't connecting to your Hub.
If you see 403 (forbidden) like this, it's a token problem:
If you see 403 (forbidden) like this, it's likely a token problem:
```
403 GET /hub/api/authorizations/cookie/jupyterhub-token-name/[secret] (@10.0.1.4) 4.14ms
@@ -138,10 +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):
@@ -152,6 +159,42 @@ After this, when you start your server via JupyterHub, it will build a
new container. If this was the underlying cause of the issue, you should see
your server again.
##### Proxy settings (403 GET)
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 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).
Standard JupyterHub installations include a [jupyterhub-singleuser](https://github.com/jupyterhub/jupyterhub/blob/9fdab027daa32c9017845572ad9d5ba1722dbc53/setup.py#L116) command which is built from the `jupyterhub.singleuser:main` method. The `jupyterhub-singleuser` command is the default command when JupyterHub launches single-user Jupyter Notebooks. One of the goals of this command is to make sure the version of JupyterHub installed within the Jupyter Notebook coincides with the version of the JupyterHub server itself.
If you launch a Jupyter Notebook with the `jupyterhub-singleuser` command directly from the command line the Jupyter Notebook won't have access to the `JUPYTERHUB_API_TOKEN` and will return:
```
JUPYTERHUB_API_TOKEN env is required to run jupyterhub-singleuser.
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 you were to run the single-user Jupyter Notebook on the host, then:
export JUPYTERHUB_API_TOKEN=my_secret_token
jupyterhub-singleuser
With a docker container, pass in the environment variable with the run command:
docker run -d \
-p 8888:8888 \
-e JUPYTERHUB_API_TOKEN=my_secret_token \
jupyter/datascience-notebook:latest
[This example](https://github.com/jupyterhub/jupyterhub/tree/HEAD/examples/service-notebook/external) demonstrates how to combine the use of the `jupyterhub-singleuser` environment variables when launching a Notebook as an externally managed service.
## How do I...?
@@ -170,11 +213,10 @@ You would then set in your `jupyterhub_config.py` file the `ssl_key` and
c.JupyterHub.ssl_cert = your_host-chained.crt
c.JupyterHub.ssl_key = your_host.key
#### 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:
@@ -193,7 +235,7 @@ where `ssl_cert` is example-chained.crt and ssl_key to your private key.
Then restart JupyterHub.
See also [JupyterHub SSL encryption](getting-started.md#ssl-encryption).
See also {ref}`ssl-encryption`.
### Install JupyterHub without a network connection
@@ -207,7 +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.
@@ -226,7 +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.
@@ -239,7 +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.
@@ -252,15 +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 whitelist to be an empty list in` jupyterhub_config.py`
3. 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:
@@ -281,35 +322,9 @@ Or use syslog:
jupyterhub | logger -t jupyterhub
## Troubleshooting commands
The following commands provide additional detail about installed packages,
versions, and system information that may be helpful when troubleshooting
a JupyterHub deployment. The commands are:
- System and deployment information
```bash
jupyter troubleshooting
```
- Kernel information
```bash
jupyter kernelspec list
```
- Debug logs when running JupyterHub
```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:
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
@@ -324,16 +339,54 @@ SyntaxError: Missing parentheses in call to 'print'
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).
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.
provides an example single-user notebook server for use with DockerSpawner.
Additional single user notebook server images can be found at the [Jupyter
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,
versions, and system information that may be helpful when troubleshooting
a JupyterHub deployment. The commands are:
- System and deployment information
```bash
jupyter troubleshoot
```
- Kernel information
```bash
jupyter kernelspec list
```
- Debug logs when running JupyterHub
```bash
jupyterhub --debug
```