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Merge branch 'jupyterhub:main' into group_property_feature

Browse Source
This commit is contained in:
Vlad Vifor
2023-01-03 16:00:45 +01:00
committed by GitHub
parent a5a61893fb e4f72c9eeb
commit e84359cc23
163 changed files with 3487 additions and 2552 deletions
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9
.flake8
View File

@@ -3,14 +3,9 @@
# E: style errors
# W: style warnings
# C: complexity
# F401: module imported but unused
# F403: import *
# F811: redefinition of unused `name` from line `N`
# D: docstring warnings (unused pydocstyle extension)
# F841: local variable assigned but never used
# E402: module level import not at top of file
# I100: Import statements are in the wrong order
# I101: Imported names are in the wrong order. Should be
ignore = E, C, W, F401, F403, F811, F841, E402, I100, I101, D400
ignore = E, C, W, D, F841
builtins = c, get_config
exclude =
.cache,

2
.github/workflows/support-bot.yml vendored
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@@ -12,7 +12,7 @@ jobs:
action:
runs-on: ubuntu-latest
steps:
- uses: dessant/support-requests@v2
- uses: dessant/support-requests@v3
with:
github-token: ${{ github.token }}
support-label: "support"

14
.github/workflows/test-docs.yml vendored
View File

@@ -55,8 +55,20 @@ jobs:
- name: Install requirements
run: |
pip install -r docs/requirements.txt pytest -e .
pip install -r docs/requirements.txt pytest
- name: pytest docs/
run: |
pytest docs/
# readthedocs doesn't halt on warnings,
# so raise any warnings here
- name: build docs
run: |
cd docs
make html
- name: check links
run: |
cd docs
make linkcheck

29
.github/workflows/test.yml vendored
View File

@@ -83,14 +83,13 @@ jobs:
db: mysql
- python: "3.10"
db: postgres
- python: "3.10"
- python: "3.11"
subdomain: subdomain
- python: "3.10"
- python: "3.11"
ssl: ssl
- python: "3.10"
- python: "3.11"
selenium: selenium
- python: "3.11.0-rc.1"
- python: "3.10"
- python: "3.11"
main_dependencies: main_dependencies
steps:
@@ -141,16 +140,7 @@ jobs:
- name: Install Python dependencies
run: |
pip install --upgrade pip
if [[ "${{ matrix.python }}" == "3.11"* ]]; then
# greenlet is not actually required,
# but is an install dependency of sqlalchemy.
# It does not yet install on 3.11
# see: see https://github.com/gevent/gevent/issues/1867
pip install ./ci/mock-greenlet
fi
pip install --upgrade . -r dev-requirements.txt
pip install ".[test]"
if [ "${{ matrix.oldest_dependencies }}" != "" ]; then
# take any dependencies in requirements.txt such as tornado>=5.0
@@ -216,15 +206,6 @@ jobs:
DB=postgres bash ci/docker-db.sh
DB=postgres bash ci/init-db.sh
fi
- name: Setup Firefox
if: matrix.selenium
uses: browser-actions/setup-firefox@latest
with:
firefox-version: latest
- name: Setup Geckodriver
if: matrix.selenium
uses: browser-actions/setup-geckodriver@latest
- name: Configure selenium tests
if: matrix.selenium

2
.gitignore vendored
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@@ -9,6 +9,8 @@ docs/_build
docs/build
docs/source/_static/rest-api
docs/source/rbac/scope-table.md
docs/source/reference/metrics.md
.ipynb_checkpoints
jsx/build/
# ignore config file at the top-level of the repo

26
.pre-commit-config.yaml
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@@ -8,24 +8,38 @@
# - Run on all files: pre-commit run --all-files
# - Register git hooks: pre-commit install --install-hooks
#
ci:
# pre-commit.ci will open PRs updating our hooks once a month
autoupdate_schedule: monthly
repos:
# Autoformat: Python code, syntax patterns are modernized
- repo: https://github.com/asottile/pyupgrade
rev: v3.2.0
rev: v3.3.1
hooks:
- id: pyupgrade
args:
- --py36-plus
- --py37-plus
# Autoformat: Python code
- repo: https://github.com/PyCQA/autoflake
rev: v2.0.0
hooks:
- id: autoflake
# args ref: https://github.com/PyCQA/autoflake#advanced-usage
args:
- --in-place
# Autoformat: Python code
- repo: https://github.com/pycqa/isort
rev: 5.10.1
rev: 5.11.4
hooks:
- id: isort
# Autoformat: Python code
- repo: https://github.com/psf/black
rev: 22.10.0
rev: 22.12.0
hooks:
- id: black
@@ -37,7 +51,7 @@ repos:
# Autoformat and linting, misc. details
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.3.0
rev: v4.4.0
hooks:
- id: end-of-file-fixer
exclude: share/jupyterhub/static/js/admin-react.js
@@ -47,6 +61,6 @@ repos:
# Linting: Python code (see the file .flake8)
- repo: https://github.com/PyCQA/flake8
rev: "5.0.4"
rev: "6.0.0"
hooks:
- id: flake8

11
.readthedocs.yaml
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@@ -1,3 +1,7 @@
# Configuration on how ReadTheDocs (RTD) builds our documentation
# ref: https://readthedocs.org/projects/jupyterhub/
# ref: https://docs.readthedocs.io/en/stable/config-file/v2.html
#
version: 2
sphinx:
@@ -11,10 +15,11 @@ build:
python:
install:
- method: pip
path: .
- requirements: docs/requirements.txt
formats:
# Adding htmlzip enables a Downloads section in the rendered website's RTD
# menu where the html build can be downloaded. This doesn't require any
# additional configuration in docs/source/conf.py.
#
- htmlzip
- epub

2
README.md
View File

@@ -190,7 +190,7 @@ this a good choice for **testing JupyterHub on your desktop or laptop**.
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 by using a ssl enabled proxy.
configuration or by 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

49
RELEASE.md
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@@ -1,39 +1,42 @@
# How to make a release
`jupyterhub` is a package [available on
PyPI](https://pypi.org/project/jupyterhub/) and
[conda-forge](https://conda-forge.org/).
These are instructions on how to make a release on PyPI.
The PyPI release is done automatically by CI when a tag is pushed.
`jupyterhub` is a package available on [PyPI][] and [conda-forge][].
These are instructions on how to make a release.
For you to follow along according to these instructions, you need:
## Pre-requisites
- To have push rights to the [jupyterhub GitHub
repository](https://github.com/jupyterhub/jupyterhub).
- Push rights to [jupyterhub/jupyterhub][]
- Push rights to [conda-forge/jupyterhub-feedstock][]
## Steps to make a release
1. Create a PR updating `docs/source/changelog.md` with [github-activity][] and
continue only when its merged.
```shell
pip install github-activity
github-activity --heading-level=3 jupyterhub/jupyterhub
```
1. Checkout main and make sure it is up to date.
```shell
ORIGIN=${ORIGIN:-origin} # set to the canonical remote, e.g. 'upstream' if 'origin' is not the official repo
git checkout main
git fetch $ORIGIN main
git reset --hard $ORIGIN/main
git fetch origin main
git reset --hard origin/main
```
1. Make sure `docs/source/changelog.md` is up-to-date.
[github-activity][] can help with this.
1. Update the version with `tbump`.
You can see what will happen without making any changes with `tbump --dry-run ${VERSION}`
1. Update the version, make commits, and push a git tag with `tbump`.
```shell
pip install tbump
tbump --dry-run ${VERSION}
tbump ${VERSION}
```
This will tag and publish a release,
which will be finished on CI.
Following this, the [CI system][] will build and publish a release.
1. Reset the version back to dev, e.g. `2.1.0.dev` after releasing `2.0.0`
@@ -42,9 +45,11 @@ For you to follow along according to these instructions, you need:
```
1. Following the release to PyPI, an automated PR should arrive to
[conda-forge/jupyterhub-feedstock][],
check for the tests to succeed on this PR and then merge it to successfully
update the package for `conda` on the conda-forge channel.
[conda-forge/jupyterhub-feedstock][] with instructions.
[github-activity]: https://github.com/choldgraf/github-activity
[pypi]: https://pypi.org/project/jupyterhub/
[conda-forge]: https://anaconda.org/conda-forge/jupyterhub
[jupyterhub/jupyterhub]: https://github.com/jupyterhub/jupyterhub
[conda-forge/jupyterhub-feedstock]: https://github.com/conda-forge/jupyterhub-feedstock
[github-activity]: https://github.com/executablebooks/github-activity
[ci system]: https://github.com/jupyterhub/jupyterhub/actions/workflows/release.yml

1
ci/check_sdist.py
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@@ -3,7 +3,6 @@
import sys
import tarfile
from tarfile import TarFile
expected_files = [
"docs/requirements.txt",

4
ci/docker-db.sh
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@@ -22,7 +22,7 @@ if [[ "$DB" == "mysql" ]]; then
# ref server: https://hub.docker.com/_/mysql/
# ref client: https://dev.mysql.com/doc/refman/5.7/en/setting-environment-variables.html
#
DOCKER_RUN_ARGS="-p 3306:3306 --env MYSQL_ALLOW_EMPTY_PASSWORD=1 mysql:5.7"
DOCKER_RUN_ARGS="-p 3306:3306 --env MYSQL_ALLOW_EMPTY_PASSWORD=1 mysql:8.0"
READINESS_CHECK="mysql --user root --execute \q"
elif [[ "$DB" == "postgres" ]]; then
# Environment variables can influence both the postgresql server in the
@@ -36,7 +36,7 @@ elif [[ "$DB" == "postgres" ]]; then
# used by the postgresql client psql, so we configure the user based on how
# we want to connect.
#
DOCKER_RUN_ARGS="-p 5432:5432 --env "POSTGRES_USER=${PGUSER}" --env "POSTGRES_PASSWORD=${PGPASSWORD}" postgres:9.5"
DOCKER_RUN_ARGS="-p 5432:5432 --env "POSTGRES_USER=${PGUSER}" --env "POSTGRES_PASSWORD=${PGPASSWORD}" postgres:15.1"
READINESS_CHECK="psql --command \q"
else
echo '$DB must be mysql or postgres'

3
ci/init-db.sh
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@@ -19,8 +19,9 @@ else
fi
# Configure a set of databases in the database server for upgrade tests
# this list must be in sync with versions in test_db.py:test_upgrade
set -x
for SUFFIX in '' _upgrade_100 _upgrade_122 _upgrade_130 _upgrade_150 _upgrade_211; do
for SUFFIX in '' _upgrade_110 _upgrade_122 _upgrade_130 _upgrade_150 _upgrade_211; do
$SQL_CLIENT "DROP DATABASE jupyterhub${SUFFIX};" 2>/dev/null || true
$SQL_CLIENT "CREATE DATABASE jupyterhub${SUFFIX} ${EXTRA_CREATE_DATABASE_ARGS:-};"
done

3
ci/mock-greenlet/greenlet.py
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@@ -1,3 +0,0 @@
__version__ = "22.0.0.dev0"
raise ImportError("Don't actually have greenlet")

13
ci/mock-greenlet/pyproject.toml
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@@ -1,13 +0,0 @@
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[project]
name = "greenlet"
description = 'Mock greenlet to allow install on 3.11'
requires-python = ">=3.7"
dynamic = ["version"]
[tool.hatch.version]
path = "greenlet.py"

25
dev-requirements.txt
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@@ -1,25 +0,0 @@
-r requirements.txt
# temporary pin of attrs for jsonschema 0.3.0a1
# seems to be a pip bug
attrs>=17.4.0
beautifulsoup4
coverage
cryptography
html5lib # needed for beautifulsoup
jupyterlab >=3
mock
# nbclassic provides the '/tree/' handler, which we use in tests
# it is a transitive dependency via jupyterlab,
# but depend on it directly
nbclassic
pre-commit
pytest>=3.3
pytest-asyncio>=0.17
pytest-cov
requests-mock
selenium
tbump
# blacklist urllib3 releases affected by https://github.com/urllib3/urllib3/issues/1683
# I *think* this should only affect testing, not production
urllib3!=1.25.4,!=1.25.5
virtualenv

6
dockerfiles/README.md
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@@ -1,9 +1,11 @@
## What is Dockerfile.alpine
Dockerfile.alpine contains base image for jupyterhub. It does not work independently, but only as part of a full jupyterhub cluster
Dockerfile.alpine contains the base image for jupyterhub. It does not work independently, but only as part of a full jupyterhub cluster
## How to use it?
You will need:
1. A running configurable-http-proxy, whose API is accessible.
2. A jupyterhub_config file.
3. Authentication and other libraries required by the specific jupyterhub_config file.
@@ -15,6 +17,6 @@ Dockerfile.alpine contains base image for jupyterhub. It does not work independe
- put both containers on the same network (e.g. docker network create jupyterhub; docker run ... --net jupyterhub)
- tell jupyterhub where CHP is (e.g. c.ConfigurableHTTPProxy.api_url = 'http://chp:8001')
- tell jupyterhub not to start the proxy itself (c.ConfigurableHTTPProxy.should_start = False)
- Use dummy authenticator for ease of testing. Update following in jupyterhub_config file
- Use a dummy authenticator for ease of testing. Update following in jupyterhub_config file
- c.JupyterHub.authenticator_class = 'dummyauthenticator.DummyAuthenticator'
- c.DummyAuthenticator.password = "your strong password"

239
docs/Makefile
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@@ -1,209 +1,62 @@
# Makefile for Sphinx documentation
#
# Makefile for Sphinx documentation generated by sphinx-quickstart
# ----------------------------------------------------------------------------
# You can set these variables from the command line.
SPHINXOPTS = "-W"
SPHINXBUILD = sphinx-build
PAPER =
BUILDDIR = build
# User-friendly check for sphinx-build
ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
endif
# Internal variables.
PAPEROPT_a4 = -D latex_paper_size=a4
PAPEROPT_letter = -D latex_paper_size=letter
ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
# the i18n builder cannot share the environment and doctrees with the others
I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS ?= --color -W --keep-going
SPHINXBUILD ?= sphinx-build
SOURCEDIR = source
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@echo "Please use \`make <target>' where <target> is one of"
@echo " html to make standalone HTML files"
@echo " dirhtml to make HTML files named index.html in directories"
@echo " singlehtml to make a single large HTML file"
@echo " pickle to make pickle files"
@echo " json to make JSON files"
@echo " htmlhelp to make HTML files and a HTML help project"
@echo " qthelp to make HTML files and a qthelp project"
@echo " applehelp to make an Apple Help Book"
@echo " devhelp to make HTML files and a Devhelp project"
@echo " epub to make an epub"
@echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
@echo " latexpdf to make LaTeX files and run them through pdflatex"
@echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
@echo " text to make text files"
@echo " man to make manual pages"
@echo " texinfo to make Texinfo files"
@echo " info to make Texinfo files and run them through makeinfo"
@echo " gettext to make PO message catalogs"
@echo " changes to make an overview of all changed/added/deprecated items"
@echo " xml to make Docutils-native XML files"
@echo " pseudoxml to make pseudoxml-XML files for display purposes"
@echo " linkcheck to check all external links for integrity"
@echo " doctest to run all doctests embedded in the documentation (if enabled)"
@echo " coverage to run coverage check of the documentation (if enabled)"
@echo " spelling to run spell check on documentation"
@echo " metrics to generate documentation for metrics by inspecting the source code"
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS)
clean:
rm -rf $(BUILDDIR)/*
.PHONY: help Makefile metrics scopes
metrics: source/reference/metrics.rst
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option.
#
# Several sphinx-build commands can be used through this, for example:
#
# - make clean
# - make linkcheck
# - make spelling
#
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS)
source/reference/metrics.rst: generate-metrics.py
python3 generate-metrics.py
scopes: source/rbac/scope-table.md
source/rbac/scope-table.md: source/rbac/generate-scope-table.py
python3 source/rbac/generate-scope-table.py
# Manually added targets - related to code generation
# ----------------------------------------------------------------------------
# For local development:
# - builds the html
# - NOTE: If the pre-requisites for the html target is updated, also update the
# Read The Docs section in docs/source/conf.py.
#
html: metrics scopes
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
$(SPHINXBUILD) -b html "$(SOURCEDIR)" "$(BUILDDIR)/html" $(SPHINXOPTS)
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
dirhtml:
$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
metrics: source/reference/metrics.md
source/reference/metrics.md:
python3 generate-metrics.py
singlehtml:
$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
@echo
@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
scopes: source/rbac/scope-table.md
source/rbac/scope-table.md:
python3 source/rbac/generate-scope-table.py
pickle:
$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
@echo
@echo "Build finished; now you can process the pickle files."
json:
$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
@echo
@echo "Build finished; now you can process the JSON files."
# Manually added targets - related to development
# ----------------------------------------------------------------------------
htmlhelp:
$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
@echo
@echo "Build finished; now you can run HTML Help Workshop with the" \
".hhp project file in $(BUILDDIR)/htmlhelp."
qthelp:
$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
@echo
@echo "Build finished; now you can run "qcollectiongenerator" with the" \
".qhcp project file in $(BUILDDIR)/qthelp, like this:"
@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/JupyterHub.qhcp"
@echo "To view the help file:"
@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/JupyterHub.qhc"
applehelp:
$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
@echo
@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
@echo "N.B. You won't be able to view it unless you put it in" \
"~/Library/Documentation/Help or install it in your application" \
"bundle."
devhelp:
$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
@echo
@echo "Build finished."
@echo "To view the help file:"
@echo "# mkdir -p $$HOME/.local/share/devhelp/JupyterHub"
@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/JupyterHub"
@echo "# devhelp"
epub:
$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
@echo
@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
latex:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo
@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
@echo "Run \`make' in that directory to run these through (pdf)latex" \
"(use \`make latexpdf' here to do that automatically)."
latexpdf:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo "Running LaTeX files through pdflatex..."
$(MAKE) -C $(BUILDDIR)/latex all-pdf
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
latexpdfja:
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
@echo "Running LaTeX files through platex and dvipdfmx..."
$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
text:
$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
@echo
@echo "Build finished. The text files are in $(BUILDDIR)/text."
man:
$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
@echo
@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
texinfo:
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
@echo
@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
@echo "Run \`make' in that directory to run these through makeinfo" \
"(use \`make info' here to do that automatically)."
info:
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
@echo "Running Texinfo files through makeinfo..."
make -C $(BUILDDIR)/texinfo info
@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
gettext:
$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
@echo
@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
changes:
$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
@echo
@echo "The overview file is in $(BUILDDIR)/changes."
linkcheck:
$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
@echo
@echo "Link check complete; look for any errors in the above output " \
"or in $(BUILDDIR)/linkcheck/output.txt."
spelling:
$(SPHINXBUILD) -b spelling $(ALLSPHINXOPTS) $(BUILDDIR)/spelling
@echo
@echo "Spell check complete; look for any errors in the above output " \
"or in $(BUILDDIR)/spelling/output.txt."
doctest:
$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
@echo "Testing of doctests in the sources finished, look at the " \
"results in $(BUILDDIR)/doctest/output.txt."
coverage:
$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
@echo "Testing of coverage in the sources finished, look at the " \
"results in $(BUILDDIR)/coverage/python.txt."
xml:
$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
@echo
@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
pseudoxml:
$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
@echo
@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
# For local development:
# - requires sphinx-autobuild, see
# https://sphinxcontrib-spelling.readthedocs.io/en/latest/
# - builds and rebuilds html on changes to source, but does not re-generate
# metrics/scopes files
# - starts a livereload enabled webserver and opens up a browser
devenv: html
sphinx-autobuild -b html --open-browser "$(SOURCEDIR)" "$(BUILDDIR)/html"

18
docs/generate-metrics.py
View File

@@ -1,8 +1,6 @@
import os
from os.path import join
from pytablewriter import RstSimpleTableWriter
from pytablewriter.style import Style
from pytablewriter import MarkdownTableWriter
import jupyterhub.metrics
@@ -12,12 +10,11 @@ HERE = os.path.abspath(os.path.dirname(__file__))
class Generator:
@classmethod
def create_writer(cls, table_name, headers, values):
writer = RstSimpleTableWriter()
writer = MarkdownTableWriter()
writer.table_name = table_name
writer.headers = headers
writer.value_matrix = values
writer.margin = 1
[writer.set_style(header, Style(align="center")) for header in headers]
return writer
def _parse_metrics(self):
@@ -34,18 +31,17 @@ class Generator:
if not os.path.exists(generated_directory):
os.makedirs(generated_directory)
filename = f"{generated_directory}/metrics.rst"
filename = f"{generated_directory}/metrics.md"
table_name = ""
headers = ["Type", "Name", "Description"]
values = self._parse_metrics()
writer = self.create_writer(table_name, headers, values)
title = "List of Prometheus Metrics"
underline = "============================"
content = f"{title}\n{underline}\n{writer.dumps()}"
with open(filename, 'w') as f:
f.write(content)
print(f"Generated {filename}.")
f.write("# List of Prometheus Metrics\n\n")
f.write(writer.dumps())
f.write("\n")
print(f"Generated {filename}")
def main():

268
docs/make.bat
View File

@@ -1,263 +1,49 @@
@ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=--color -W --keep-going
)
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set BUILDDIR=build
set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source
set I18NSPHINXOPTS=%SPHINXOPTS% source
if NOT "%PAPER%" == "" (
set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS%
)
set SOURCEDIR=source
set BUILDDIR=_build
if "%1" == "" goto help
if "%1" == "help" (
:help
echo.Please use `make ^<target^>` where ^<target^> is one of
echo. html to make standalone HTML files
echo. dirhtml to make HTML files named index.html in directories
echo. singlehtml to make a single large HTML file
echo. pickle to make pickle files
echo. json to make JSON files
echo. htmlhelp to make HTML files and a HTML help project
echo. qthelp to make HTML files and a qthelp project
echo. devhelp to make HTML files and a Devhelp project
echo. epub to make an epub
echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter
echo. text to make text files
echo. man to make manual pages
echo. texinfo to make Texinfo files
echo. gettext to make PO message catalogs
echo. changes to make an overview over all changed/added/deprecated items
echo. xml to make Docutils-native XML files
echo. pseudoxml to make pseudoxml-XML files for display purposes
echo. linkcheck to check all external links for integrity
echo. doctest to run all doctests embedded in the documentation if enabled
echo. coverage to run coverage check of the documentation if enabled
goto end
)
if "%1" == "clean" (
for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
del /q /s %BUILDDIR%\*
goto end
)
if "%1" == "devenv" goto devenv
goto default
REM Check if sphinx-build is available and fallback to Python version if any
%SPHINXBUILD% 1>NUL 2>NUL
if errorlevel 9009 goto sphinx_python
goto sphinx_ok
:sphinx_python
set SPHINXBUILD=python -m sphinx.__init__
%SPHINXBUILD% 2> nul
:default
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.http://sphinx-doc.org/
echo.The 'sphinx-build' command was not found. Open and read README.md!
exit /b 1
)
:sphinx_ok
%SPHINXBUILD% -M %1 "%SOURCEDIR%" "%BUILDDIR%" %SPHINXOPTS%
goto end
if "%1" == "html" (
%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
if errorlevel 1 exit /b 1
:help
%SPHINXBUILD% -M help "%SOURCEDIR%" "%BUILDDIR%" %SPHINXOPTS%
goto end
:devenv
sphinx-autobuild >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/html.
goto end
echo.The 'sphinx-autobuild' command was not found. Open and read README.md!
exit /b 1
)
sphinx-autobuild -b html --open-browser "%SOURCEDIR%" "%BUILDDIR%/html"
goto end
if "%1" == "dirhtml" (
%SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
goto end
)
if "%1" == "singlehtml" (
%SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
goto end
)
if "%1" == "pickle" (
%SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can process the pickle files.
goto end
)
if "%1" == "json" (
%SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can process the JSON files.
goto end
)
if "%1" == "htmlhelp" (
%SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can run HTML Help Workshop with the ^
.hhp project file in %BUILDDIR%/htmlhelp.
goto end
)
if "%1" == "qthelp" (
%SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
if errorlevel 1 exit /b 1
echo.
echo.Build finished; now you can run "qcollectiongenerator" with the ^
.qhcp project file in %BUILDDIR%/qthelp, like this:
echo.^> qcollectiongenerator %BUILDDIR%\qthelp\JupyterHub.qhcp
echo.To view the help file:
echo.^> assistant -collectionFile %BUILDDIR%\qthelp\JupyterHub.ghc
goto end
)
if "%1" == "devhelp" (
%SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
if errorlevel 1 exit /b 1
echo.
echo.Build finished.
goto end
)
if "%1" == "epub" (
%SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The epub file is in %BUILDDIR%/epub.
goto end
)
if "%1" == "latex" (
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
if errorlevel 1 exit /b 1
echo.
echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
goto end
)
if "%1" == "latexpdf" (
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
cd %BUILDDIR%/latex
make all-pdf
cd %~dp0
echo.
echo.Build finished; the PDF files are in %BUILDDIR%/latex.
goto end
)
if "%1" == "latexpdfja" (
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
cd %BUILDDIR%/latex
make all-pdf-ja
cd %~dp0
echo.
echo.Build finished; the PDF files are in %BUILDDIR%/latex.
goto end
)
if "%1" == "text" (
%SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The text files are in %BUILDDIR%/text.
goto end
)
if "%1" == "man" (
%SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The manual pages are in %BUILDDIR%/man.
goto end
)
if "%1" == "texinfo" (
%SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo.
goto end
)
if "%1" == "gettext" (
%SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The message catalogs are in %BUILDDIR%/locale.
goto end
)
if "%1" == "changes" (
%SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
if errorlevel 1 exit /b 1
echo.
echo.The overview file is in %BUILDDIR%/changes.
goto end
)
if "%1" == "linkcheck" (
%SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
if errorlevel 1 exit /b 1
echo.
echo.Link check complete; look for any errors in the above output ^
or in %BUILDDIR%/linkcheck/output.txt.
goto end
)
if "%1" == "doctest" (
%SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
if errorlevel 1 exit /b 1
echo.
echo.Testing of doctests in the sources finished, look at the ^
results in %BUILDDIR%/doctest/output.txt.
goto end
)
if "%1" == "coverage" (
%SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage
if errorlevel 1 exit /b 1
echo.
echo.Testing of coverage in the sources finished, look at the ^
results in %BUILDDIR%/coverage/python.txt.
goto end
)
if "%1" == "xml" (
%SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The XML files are in %BUILDDIR%/xml.
goto end
)
if "%1" == "pseudoxml" (
%SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml.
goto end
)
:end
popd

15
docs/requirements.txt
View File

@@ -1,12 +1,21 @@
-r ../requirements.txt
# We install the jupyterhub package to help autodoc-traits inspect it and
# generate documentation.
#
# FIXME: If there is a way for this requirements.txt file to pass a flag that
# the build system can intercept to not build the javascript artifacts,
# then do so so. That would mean that installing the documentation can
# avoid needing node/npm installed.
#
--editable .
alabaster_jupyterhub
autodoc-traits
myst-parser
pre-commit
pydata-sphinx-theme
pytablewriter>=0.56
ruamel.yaml
sphinx>=1.7
sphinx>=4
sphinx-copybutton
sphinx-jsonschema
sphinxext-opengraph
sphinxext-rediraffe

2
docs/source/_static/rest-api.yml
View File

@@ -6,7 +6,7 @@ info:
description: The REST API for JupyterHub
license:
name: BSD-3-Clause
version: 3.1.0.dev
version: 3.2.0.dev
servers:
- url: /hub/api
security:

308
docs/source/admin/capacity-planning.md Normal file
View File

@@ -0,0 +1,308 @@
# Capacity planning
General capacity planning advice for JupyterHub is hard to give,
because it depends almost entirely on what your users are doing,
and what JupyterHub users do varies _wildly_ in terms of resource consumption.
**There is no single answer to "I have X users, what resources do I need?" or "How many users can I support with this machine?"**
Here are three _typical_ Jupyter use patterns that require vastly different resources:
- **Learning**: negligible resources because computation is mostly idle,
e.g. students learning programming for the first time
- **Production code**: very intense, sustained load, e.g. training machine learning models
- **Bursting**: _mostly_ idle, but needs a lot of resources for short periods of time
(interactive research often looks like this)
But just because there's no single answer doesn't mean we can't help.
So we have gathered here some useful information to help you make your decisions
about what resources you need based on how your users work,
including the relative invariants in terms of resources that JupyterHub itself needs.
## JupyterHub infrastructure
JupyterHub consists of a few components that are always running.
These take up very little resources,
especially relative to the resources consumed by users when you have more than a few.
As an example, an instance of mybinder.org (running JupyterHub 1.5.0),
running with typically ~100-150 users has:
| Component | CPU (mean/peak) | Memory (mean/peak) |
| --------- | --------------- | ------------------ |
| Hub | 4% / 13% | (230 MB / 260 MB) |
| Proxy | 6% / 13% | (47 MB / 65 MB) |
So it would be pretty generous to allocate ~25% of one CPU core
and ~500MB of RAM to overall JupyterHub infrastructure.
The rest is going to be up to your users.
Per-user overhead from JupyterHub is typically negligible
up to at least a few hundred concurrent active users.
```{figure} ../images/mybinder-hub-components-cpu-memory.png
JupyterHub component resource usage for mybinder.org.
```
## Factors to consider
### Static vs elastic resources
A big factor in planning resources is:
**how much does it cost to change your mind?**
If you are using a single shared machine with local storage,
migrating to a new one because it turns out your users don't fit might be very costly.
You will have to get a new machine, set it up, and maybe even migrate user data.
On the other hand, if you are using ephemeral resources,
such as node pools in Kubernetes,
changing resource types costs close to nothing
because nodes can automatically be added or removed as needed.
Take that cost into account when you are picking how much memory or cpu to allocate to users.
Static resources (like [the-littlest-jupyterhub][]) provide for more **stable, predictable costs**,
but elastic resources (like [zero-to-jupyterhub][]) tend to provide **lower overall costs**
(especially when deployed with monitoring allowing cost optimizations over time),
but which are **less predictable**.
[the-littlest-jupyterhub]: https://the-littlest-jupyterhub.readthedocs.io
[zero-to-jupyterhub]: https://z2jh.jupyter.org
(limits-requests)=
### Limit vs Request for resources
Many scheduling tools like Kubernetes have two separate ways of allocating resources to users.
A **Request** or **Reservation** describes how much resources are _set aside_ for each user.
Often, this doesn't have any practical effect other than deciding when a given machine is considered 'full'.
If you are using expandable resources like an autoscaling Kubernetes cluster,
a new node must be launched and added to the pool if you 'request' more resources than fit on currently running nodes (a cluster **scale-up event**).
If you are running on a single VM, this describes how many users you can run at the same time, full stop.
A **Limit**, on the other hand, enforces a limit to how much resources any given user can consume.
For more information on what happens when users try to exceed their limits, see [](oversubscription).
In the strictest, safest case, you can have these two numbers be the same.
That means that each user is _limited_ to fit within the resources allocated to it.
This avoids **[oversubscription](oversubscription)** of resources (allowing use of more than you have available),
at the expense (in a literal, this-costs-money sense) of reserving lots of usually-idle capacity.
However, you often find that a small fraction of users use more resources than others.
In this case you may give users limits that _go beyond the amount of resources requested_.
This is called **oversubscribing** the resources available to users.
Having a gap between the request and the limit means you can fit a number of _typical_ users on a node (based on the request),
but still limit how much a runaway user can gobble up for themselves.
(oversubscription)=
### Oversubscribed CPU is okay, running out of memory is bad
An important consideration when assigning resources to users is: **What happens when users need more than I've given them?**
A good summary to keep in mind:
> When tasks don't get enough CPU, things are slow.
> When they don't get enough memory, things are broken.
This means it's **very important that users have enough memory**,
but much less important that they always have exclusive access to all the CPU they can use.
This relates to [Limits and Requests](limits-requests),
because these are the consequences of your limits and/or requests not matching what users actually try to use.
A table of mismatched resource allocation situations and their consequences:
| issue | consequence |
| -------------------------------------------------------- | ------------------------------------------------------------------------------------- |
| Requests too high | Unnecessarily high cost and/or low capacity. |
| CPU limit too low | Poor performance experienced by users |
| CPU oversubscribed (too-low request + too-high limit) | Poor performance across the system; may crash, if severe |
| Memory limit too low | Servers killed by Out-of-Memory Killer (OOM); lost work for users |
| Memory oversubscribed (too-low request + too-high limit) | System memory exhaustion - all kinds of hangs and crashes and weird errors. Very bad. |
Note that the 'oversubscribed' problem case is where the request is lower than _typical_ usage,
meaning that the total reserved resources isn't enough for the total _actual_ consumption.
This doesn't mean that _all_ your users exceed the request,
just that the _limit_ gives enough room for the _average_ user to exceed the request.
All of these considerations are important _per node_.
Larger nodes means more users per node, and therefore more users to average over.
It also means more chances for multiple outliers on the same node.
### Example case for oversubscribing memory
Take for example, this system and sampling of user behavior:
- System memory = 8G
- memory request = 1G, limit = 3G
- typical 'heavy' user: 2G
- typical 'light' user: 0.5G
This will assign 8 users to those 8G of RAM (remember: only requests are used for deciding when a machine is 'full').
As long as the total of 8 users _actual_ usage is under 8G, everything is fine.
But the _limit_ allows a total of 24G to be used,
which would be a mess if everyone used their full limit.
But _not_ everyone uses the full limit, which is the point!
This pattern is fine if 1/8 of your users are 'heavy' because _typical_ usage will be ~0.7G,
and your total usage will be ~5G (`1 × 2 + 7 × 0.5 = 5.5`).
But if _50%_ of your users are 'heavy' you have a problem because that means your users will be trying to use 10G (`4 × 2 + 4 × 0.5 = 10`),
which you don't have.
You can make guesses at these numbers, but the only _real_ way to get them is to measure (see [](measuring)).
### CPU:memory ratio
Most of the time, you'll find that only one resource is the limiting factor for your users.
Most often it's memory, but for certain tasks, it could be CPU (or even GPUs).
Many cloud deployments have just one or a few fixed ratios of cpu to memory
(e.g. 'general purpose', 'high memory', and 'high cpu').
Setting your secondary resource allocation according to this ratio
after selecting the more important limit results in a balanced resource allocation.
For instance, some of Google Cloud's ratios are:
| node type | GB RAM / CPU core |
| ----------- | ----------------- |
| n2-highmem | 8 |
| n2-standard | 4 |
| n2-highcpu | 1 |
(idleness)=
### Idleness
Jupyter being an interactive tool means people tend to spend a lot more time reading and thinking than actually running resource-intensive code.
This significantly affects how much _cpu_ resources a typical active user needs,
but often does not significantly affect the _memory_.
Ways to think about this:
- More idle users means unused CPU.
This generally means setting your CPU _limit_ higher than your CPU _request_.
- What do your users do when they _are_ running code?
Is it typically single-threaded local computation in a notebook?
If so, there's little reason to set a limit higher than 1 CPU core.
- Do typical computations take a long time, or just a few seconds?
Longer typical computations means it's more likely for users to be trying to use the CPU at the same moment,
suggesting a higher _request_.
- Even with idle users, parallel computation adds up quickly - one user fully loading 4 cores and 3 using almost nothing still averages to more than a full CPU core per user.
- Long-running intense computations suggest higher requests.
Again, using mybinder.org as an example—we run around 100 users on 8-core nodes,
and still see fairly _low_ overall CPU usage on each user node.
The limit here is actually Kubernetes' pods per node, not memory _or_ CPU.
This is likely a extreme case, as many Binder users come from clicking links on webpages
without any actual intention of running code.
```{figure} ../images/mybinder-load5.png
mybinder.org node CPU usage is low with 50-150 users sharing just 8 cores
```
### Concurrent users and culling idle servers
Related to [][idleness], all of these resource consumptions and limits are calculated based on **concurrently active users**,
not total users.
You might have 10,000 users of your JupyterHub deployment, but only 100 of them running at any given time.
That 100 is the main number you need to use for your capacity planning.
JupyterHub costs scale very little based on the number of _total_ users,
up to a point.
There are two important definitions for **active user**:
- Are they _actually_ there (i.e. a human interacting with Jupyter, or running code that might be )
- Is their server running (this is where resource reservations and limits are actually applied)
Connecting those two definitions (how long are servers running if their humans aren't using them) is an important area of deployment configuration, usually implemented via the [JupyterHub idle culler service][idle-culler].
[idle-culler]: https://github.com/jupyterhub/jupyterhub-idle-culler
There are a lot of considerations when it comes to culling idle users that will depend:
- How much does it save me to shut down user servers? (e.g. keeping an elastic cluster small, or keeping a fixed-size deployment available to active users)
- How much does it cost my users to have their servers shut down? (e.g. lost work if shutdown prematurely)
- How easy do I want it to be for users to keep their servers running? (e.g. Do they want to run unattended simulations overnight? Do you want them to?)
Like many other things in this guide, there are many correct answers leading to different configuration choices.
For more detail on culling configuration and considerations, consult the [JupyterHub idle culler documentation][idle-culler].
## More tips
### Start strict and generous, then measure
A good tip, in general, is to give your users as much resources as you can afford that you think they _might_ use.
Then, use resource usage metrics like prometheus to analyze what your users _actually_ need,
and tune accordingly.
Remember: **Limits affect your user experience and stability. Requests mostly affect your costs**.
For example, a sensible starting point (lacking any other information) might be:
```yaml
request:
cpu: 0.5
mem: 2G
limit:
cpu: 1
mem: 2G
```
(more memory if significant computations are likely - machine learning models, data analysis, etc.)
Some actions
- If you see out-of-memory killer events, increase the limit (or talk to your users!)
- If you see typical memory well below your limit, reduce the request (but not the limit)
- If _nobody_ uses that much memory, reduce your limit
- If CPU is your limiting scheduling factor and your CPUs are mostly idle,
reduce the cpu request (maybe even to 0!).
- If CPU usage continues to be low, increase the limit to 2 or 4 to allow bursts of parallel execution.
(measuring)=
### Measuring user resource consumption
It is _highly_ recommended to deploy monitoring services such as [Prometheus][]
and [Grafana][] to get a view of your users' resource usage.
This is the only way to truly know what your users need.
JupyterHub has some experimental [grafana dashboards][] you can use as a starting point,
to keep an eye on your resource usage.
Here are some sample charts from (again from mybinder.org),
showing >90% of users using less than 10% CPU and 200MB,
but a few outliers near the limit of 1 CPU and 2GB of RAM.
This is the kind of information you can use to tune your requests and limits.
![Snapshot from JupyterHub's Grafana dashboards on mybinder.org](../images/mybinder-user-resources.png)
[prometheus]: https://prometheus.io
[grafana]: https://grafana.com
[grafana dashboards]: https://github.com/jupyterhub/grafana-dashboards
### Measuring costs
Measuring costs may be as important as measuring your users activity.
If you are using a cloud provider, you can often use cost thresholds and quotas to instruct them to notify you if your costs are too high,
e.g. "Have AWS send me an email if I hit X spending trajectory on week 3 of the month."
You can then use this information to tune your resources based on what you can afford.
You can mix this information with user resource consumption to figure out if you have a problem,
e.g. "my users really do need X resources, but I can only afford to give them 80% of X."
This information may prove useful when asking your budget-approving folks for more funds.
### Additional resources
There are lots of other resources for cost and capacity planning that may be specific to JupyterHub and/or your cloud provider.
Here are some useful links to other resources
- [Zero to JupyterHub](https://z2jh.jupyter.org) documentation on
- [projecting costs](https://z2jh.jupyter.org/en/latest/administrator/cost.html)
- [configuring user resources](https://z2jh.jupyter.org/en/latest/jupyterhub/customizing/user-resources.html)
- Cloud platform cost calculators:
- [Google Cloud](https://cloud.google.com/products/calculator/)
- [Amazon AWS](https://calculator.aws)
- [Microsoft Azure](https://azure.microsoft.com/en-us/pricing/calculator/)

139
docs/source/admin/upgrading.md Normal file
View File

@@ -0,0 +1,139 @@
# 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
should consult the distribution's documentation on how to upgrade. This documentation is
for those who have set up their JupyterHub without using a distribution.
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.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 the authenticators & spawners you use, so
read the changelogs for those too!
## Notify your users
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 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 or sign in.
## Backup database & config
Before doing an upgrade, it is critical to back up:
1. 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.
2. Your `jupyterhub_config.py` file.
3. 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.
## Shut down JupyterHub
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.
## Upgrade JupyterHub packages
There are two environments where the `jupyterhub` package is installed:
1. 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.
2. 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 the same environment as the hub environment. The hub
launched the `jupyterhub-singleuser` command in this environment,
which in turn starts the notebook server.
You need to make sure the version of the `jupyterhub` package matches
in both these environments. If you installed `jupyterhub` with pip,
you can upgrade it with:
```bash
python3 -m pip install --upgrade jupyterhub==<version>
```
Where `<version>` is the version of JupyterHub you are upgrading to.
If you used `conda` to install `jupyterhub`, you should upgrade it
with:
```bash
conda install -c conda-forge jupyterhub==<version>
```
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
or upgrade them separately.
## Upgrade JupyterHub database
Once new packages are installed, you need to upgrade the JupyterHub
database. From the hub environment, in the same directory as your
`jupyterhub_config.py` file, you should run:
```bash
jupyterhub upgrade-db
```
This should find the location of your database, and run the necessary upgrades
for it.
### SQLite database disadvantages
SQLite has some disadvantages when it comes to upgrading JupyterHub. These
are:
- `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.
### What happens if I delete my database?
Losing the Hub database is often not a big deal. Information that
resides only in the Hub database includes:
- active login tokens (user cookies, service tokens)
- users added via JupyterHub UI, instead of config files
- info about running servers
If the following conditions are true, you should be fine clearing the
Hub database and starting over:
- users specified in the config file, or login using an external
authentication provider (Google, GitHub, LDAP, etc)
- user servers are stopped during the upgrade
- don't mind causing users to log in again after the upgrade
## Start JupyterHub
Once the database upgrade is completed, start the `jupyterhub`
process again.
1. Log in and start the server to make sure things work as
expected.
2. Check the logs for any errors or deprecation warnings. You
might have to update your `jupyterhub_config.py` file to
deal with any deprecated options.
Congratulations, your JupyterHub has been upgraded!

154
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@@ -1,154 +0,0 @@
====================
Upgrading JupyterHub
====================
JupyterHub offers easy upgrade pathways between minor versions. This
document describes how to do these upgrades.
If you use :ref:`a JupyterHub distribution <index/distributions>`, you
should consult the distribution's documentation on how to upgrade. This
document is applicable if you have set up your own JupyterHub without using a
distribution.
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.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 the authenticators & spawners you use, so
read the changelogs for those too!
Notify your users
=================
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 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 or sign in.
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 use SQLite (the default), you should backup the ``jupyterhub.sqlite`` file.
#. Your ``jupyterhub_config.py`` file.
#. 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.
Shut down JupyterHub
====================
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.
Upgrade JupyterHub packages
===========================
There are two environments where the ``jupyterhub`` package is installed:
#. 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*: 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 the same environment as the hub environment. The hub
launched the ``jupyterhub-singleuser`` command in this environment,
which in turn starts the notebook server.
You need to make sure the version of the ``jupyterhub`` package matches
in both these environments. If you installed ``jupyterhub`` with pip,
you can upgrade it with:
.. code-block:: bash
python3 -m pip install --upgrade jupyterhub==<version>
Where ``<version>`` is the version of JupyterHub you are upgrading to.
If you used ``conda`` to install ``jupyterhub``, you should upgrade it
with:
.. code-block:: bash
conda install -c conda-forge jupyterhub==<version>
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
or upgrade them separately.
Upgrade JupyterHub database
===========================
Once new packages are installed, you need to upgrade the JupyterHub
database. From the hub environment, in the same directory as your
``jupyterhub_config.py`` file, you should run:
.. code-block:: bash
jupyterhub upgrade-db
This should find the location of your database, and run the necessary upgrades
for it.
SQLite database disadvantages
-----------------------------
SQLite has some disadvantages when it comes to upgrading JupyterHub. These
are:
- ``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.
What happens if I delete my database?
-------------------------------------
Losing the Hub database is often not a big deal. Information that
resides only in the Hub database includes:
- active login tokens (user cookies, service tokens)
- users added via JupyterHub UI, instead of config files
- info about running servers
If the following conditions are true, you should be fine clearing the
Hub database and starting over:
- users specified in the config file, or login using an external
authentication provider (Google, GitHub, LDAP, etc)
- user servers are stopped during the upgrade
- don't mind causing users to log in again after the upgrade
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
expected.
#. Check the logs for any errors or deprecation warnings. You
might have to update your ``jupyterhub_config.py`` file to
deal with any deprecated options.
Congratulations, your JupyterHub has been upgraded!

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@@ -0,0 +1,13 @@
# Application configuration
## Module: {mod}`jupyterhub.app`
```{eval-rst}
.. automodule:: jupyterhub.app
```
### {class}`JupyterHub`
```{eval-rst}
.. autoconfigurable:: JupyterHub
```

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@@ -1,15 +0,0 @@
=========================
Application configuration
=========================
Module: :mod:`jupyterhub.app`
=============================
.. automodule:: jupyterhub.app
.. currentmodule:: jupyterhub.app
:class:`JupyterHub`
-------------------
.. autoconfigurable:: JupyterHub

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# Authenticators
## Module: {mod}`jupyterhub.auth`
```{eval-rst}
.. automodule:: jupyterhub.auth
```
### {class}`Authenticator`
```{eval-rst}
.. autoconfigurable:: Authenticator
:members:
```
### {class}`LocalAuthenticator`
```{eval-rst}
.. autoconfigurable:: LocalAuthenticator
:members:
```
### {class}`PAMAuthenticator`
```{eval-rst}
.. autoconfigurable:: PAMAuthenticator
```
### {class}`DummyAuthenticator`
```{eval-rst}
.. autoconfigurable:: DummyAuthenticator
```

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@@ -1,32 +0,0 @@
==============
Authenticators
==============
Module: :mod:`jupyterhub.auth`
==============================
.. automodule:: jupyterhub.auth
.. currentmodule:: jupyterhub.auth
:class:`Authenticator`
----------------------
.. autoconfigurable:: Authenticator
:members:
:class:`LocalAuthenticator`
---------------------------
.. autoconfigurable:: LocalAuthenticator
:members:
:class:`PAMAuthenticator`
-------------------------
.. autoconfigurable:: PAMAuthenticator
:class:`DummyAuthenticator`
---------------------------
.. autoconfigurable:: DummyAuthenticator

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@@ -0,0 +1,37 @@
(api-index)=
# JupyterHub API
<!--
Below is a MyST field list, using MyST substitution, as supported
by enabling the respective MyST extensions in docs/source/conf.py.
-->
:Date: {{ date }}
:Release: {{ version }}
JupyterHub also provides a REST API for administration of the Hub and users.
The documentation on [Using JupyterHub's REST API](../reference/rest.md) provides
information on:
- what you can do with the API
- creating an API token
- adding API tokens to the config files
- making an API request programmatically using the requests library
- learning more about JupyterHub's API
JupyterHub API Reference:
```{toctree}
:maxdepth: 3
app
auth
spawner
proxy
user
service
services.auth
```
[openapi initiative]: https://www.openapis.org/

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@@ -1,33 +0,0 @@
.. _api-index:
##############
JupyterHub API
##############
:Release: |release|
:Date: |today|
JupyterHub also provides a REST API for administration of the Hub and users.
The documentation on `Using JupyterHub's REST API <../reference/rest.html>`_ provides
information on:
- what you can do with the API
- creating an API token
- adding API tokens to the config files
- making an API request programmatically using the requests library
- learning more about JupyterHub's API
JupyterHub API Reference:
.. toctree::
app
auth
spawner
proxy
user
service
services.auth
.. _OpenAPI Initiative: https://www.openapis.org/

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# Proxies
## Module: {mod}`jupyterhub.proxy`
```{eval-rst}
.. automodule:: jupyterhub.proxy
```
### {class}`Proxy`
```{eval-rst}
.. autoconfigurable:: Proxy
:members:
```
### {class}`ConfigurableHTTPProxy`
```{eval-rst}
.. autoconfigurable:: ConfigurableHTTPProxy
:members: debug, auth_token, check_running_interval, api_url, command
```

22
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@@ -1,22 +0,0 @@
=======
Proxies
=======
Module: :mod:`jupyterhub.proxy`
===============================
.. automodule:: jupyterhub.proxy
.. currentmodule:: jupyterhub.proxy
:class:`Proxy`
--------------
.. autoconfigurable:: Proxy
:members:
:class:`ConfigurableHTTPProxy`
------------------------------
.. autoconfigurable:: ConfigurableHTTPProxy
:members: debug, auth_token, check_running_interval, api_url, command

14
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@@ -0,0 +1,14 @@
# Services
## Module: {mod}`jupyterhub.services.service`
```{eval-rst}
.. automodule:: jupyterhub.services.service
```
### {class}`Service`
```{eval-rst}
.. autoconfigurable:: Service
:members: name, admin, url, api_token, managed, kind, command, cwd, environment, user, oauth_client_id, server, prefix, proxy_spec
```

16
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@@ -1,16 +0,0 @@
========
Services
========
Module: :mod:`jupyterhub.services.service`
==========================================
.. automodule:: jupyterhub.services.service
.. currentmodule:: jupyterhub.services.service
:class:`Service`
----------------
.. autoconfigurable:: Service
:members: name, admin, url, api_token, managed, kind, command, cwd, environment, user, oauth_client_id, server, prefix, proxy_spec

40
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@@ -0,0 +1,40 @@
# Services Authentication
## Module: {mod}`jupyterhub.services.auth`
```{eval-rst}
.. automodule:: jupyterhub.services.auth
```
### {class}`HubAuth`
```{eval-rst}
.. autoconfigurable:: HubAuth
:members:
```
### {class}`HubOAuth`
```{eval-rst}
.. autoconfigurable:: HubOAuth
:members:
```
### {class}`HubAuthenticated`
```{eval-rst}
.. autoclass:: HubAuthenticated
:members:
```
### {class}`HubOAuthenticated`
```{eval-rst}
.. autoclass:: HubOAuthenticated
```
### {class}`HubOAuthCallbackHandler`
```{eval-rst}
.. autoclass:: HubOAuthCallbackHandler
```

40
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@@ -1,40 +0,0 @@
=======================
Services Authentication
=======================
Module: :mod:`jupyterhub.services.auth`
=======================================
.. automodule:: jupyterhub.services.auth
.. currentmodule:: jupyterhub.services.auth
:class:`HubAuth`
----------------
.. autoconfigurable:: HubAuth
:members:
:class:`HubOAuth`
-----------------
.. autoconfigurable:: HubOAuth
:members:
:class:`HubAuthenticated`
-------------------------
.. autoclass:: HubAuthenticated
:members:
:class:`HubOAuthenticated`
--------------------------
.. autoclass:: HubOAuthenticated
:class:`HubOAuthCallbackHandler`
--------------------------------
.. autoclass:: HubOAuthCallbackHandler

20
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@@ -0,0 +1,20 @@
# Spawners
## Module: {mod}`jupyterhub.spawner`
```{eval-rst}
.. automodule:: jupyterhub.spawner
```
### {class}`Spawner`
```{eval-rst}
.. autoconfigurable:: Spawner
:members: options_from_form, poll, start, stop, get_args, get_env, get_state, template_namespace, format_string, create_certs, move_certs
```
### {class}`LocalProcessSpawner`
```{eval-rst}
.. autoconfigurable:: LocalProcessSpawner
```

21
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@@ -1,21 +0,0 @@
========
Spawners
========
Module: :mod:`jupyterhub.spawner`
=================================
.. automodule:: jupyterhub.spawner
.. currentmodule:: jupyterhub.spawner
:class:`Spawner`
----------------
.. autoconfigurable:: Spawner
:members: options_from_form, poll, start, stop, get_args, get_env, get_state, template_namespace, format_string, create_certs, move_certs
:class:`LocalProcessSpawner`
----------------------------
.. autoconfigurable:: LocalProcessSpawner

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@@ -0,0 +1,34 @@
# Users
## Module: {mod}`jupyterhub.user`
```{eval-rst}
.. automodule:: jupyterhub.user
```
### {class}`UserDict`
```{eval-rst}
.. autoclass:: UserDict
:members:
```
### {class}`User`
```{eval-rst}
.. autoclass:: User
:members: escaped_name
.. attribute:: name
The user's name
.. attribute:: server
The user's Server data object if running, None otherwise.
Has ``ip``, ``port`` attributes.
.. attribute:: spawner
The user's :class:`~.Spawner` instance.
```

36
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@@ -1,36 +0,0 @@
=====
Users
=====
Module: :mod:`jupyterhub.user`
==============================
.. automodule:: jupyterhub.user
.. currentmodule:: jupyterhub.user
:class:`UserDict`
-----------------
.. autoclass:: UserDict
:members:
:class:`User`
-------------
.. autoclass:: User
:members: escaped_name
.. attribute:: name
The user's name
.. attribute:: server
The user's Server data object if running, None otherwise.
Has ``ip``, ``port`` attributes.
.. attribute:: spawner
The user's :class:`~.Spawner` instance.

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318
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@@ -1,72 +1,78 @@
# 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 sys
# 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',
'sphinx-jsonschema',
'myst_parser',
]
myst_heading_anchors = 2
myst_enable_extensions = [
'colon_fence',
'deflist',
]
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = 'JupyterHub'
copyright = '2016, Project Jupyter team'
author = 'Project Jupyter team'
# Autopopulate version
from os.path import dirname
docs = dirname(dirname(__file__))
root = dirname(docs)
sys.path.insert(0, root)
import jupyterhub
# The short X.Y version.
version = '%i.%i' % jupyterhub.version_info[:2]
# The full version, including alpha/beta/rc tags.
release = jupyterhub.__version__
language = "en"
exclude_patterns = []
pygments_style = 'sphinx'
todo_include_todos = False
# Set the default role so we can use `foo` instead of ``foo``
default_role = 'literal'
from contextlib import redirect_stdout
from io import StringIO
import subprocess
from docutils import nodes
from sphinx.directives.other import SphinxDirective
# -- Config -------------------------------------------------------------
import jupyterhub
from jupyterhub.app import JupyterHub
# create a temp instance of JupyterHub just to get the output of the generate-config
# and help --all commands.
# -- 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}"
# -- 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"]
# default_role let's use use `foo` instead of ``foo`` in rST
default_role = "literal"
# -- MyST configuration ------------------------------------------------------
# ref: https://myst-parser.readthedocs.io/en/latest/configuration.html
#
myst_heading_anchors = 2
myst_enable_extensions = [
# available extensions: https://myst-parser.readthedocs.io/en/latest/syntax/optional.html
"colon_fence",
"deflist",
"fieldlist",
"substitution",
]
myst_substitutions = {
# date example: Dev 07, 2022
"date": datetime.date.today().strftime("%b %d, %Y").title(),
"version": jupyterhub.__version__,
}
# -- 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()
@@ -83,8 +89,8 @@ class ConfigDirective(SphinxDirective):
# 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, '$HOME', 1)
home_dir = os.environ["HOME"]
generated_config = generated_config.replace(home_dir, "$HOME", 1)
par = nodes.literal_block(text=generated_config)
return [par]
@@ -100,39 +106,55 @@ class HelpAllDirective(SphinxDirective):
def run(self):
# The output of the help command for this version
buffer = StringIO()
with redirect_stdout(buffer):
jupyterhub_app.print_help('--help-all')
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, '$HOME', 1)
home_dir = os.environ["HOME"]
all_help = all_help.replace(home_dir, "$HOME", 1)
par = nodes.literal_block(text=all_help)
return [par]
def setup(app):
app.add_css_file('custom.css')
app.add_directive('jupyterhub-generate-config', ConfigDirective)
app.add_directive('jupyterhub-help-all', HelpAllDirective)
app.add_css_file("custom.css")
app.add_directive("jupyterhub-generate-config", ConfigDirective)
app.add_directive("jupyterhub-help-all", HelpAllDirective)
source_suffix = ['.rst', '.md']
# source_encoding = 'utf-8-sig'
# -- 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)
# -- Options for HTML output ----------------------------------------------
# The theme to use for HTML and HTML Help pages.
html_theme = 'pydata_sphinx_theme'
# -- 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 # noqa
except ImportError:
pass
else:
extensions.append("sphinxcontrib.spelling")
spelling_word_list_filename = "spelling_wordlist.txt"
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']
htmlhelp_basename = 'JupyterHubdoc'
# -- 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": [
{
@@ -149,111 +171,53 @@ html_theme_options = {
"use_edit_page_button": True,
"navbar_align": "left",
}
html_context = {
"github_user": "jupyterhub",
"github_repo": "jupyterhub",
"github_version": "main",
"doc_path": "docs",
"doc_path": "docs/source",
}
# -- 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',
'JupyterHub Documentation',
'Project Jupyter team',
'manual',
)
# -- 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
r"https?://(localhost|127.0.0.1).*", # ignore localhost references in auto-links
]
linkcheck_anchors_ignore = [
"/#!",
"/#%21",
]
# latex_logo = None
# latex_use_parts = False
# latex_show_pagerefs = False
# latex_show_urls = False
# latex_appendices = []
# latex_domain_indices = True
# -- manual page output -------------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [(master_doc, 'jupyterhub', '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',
'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 -------------------------------------------------------------
# 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),
"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
# -- 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 both metrics and rest-api, since RTD doesn't run make
from subprocess import check_call as sh
sh(['make', 'metrics', 'scopes'], cwd=docs)
# -- Spell checking -------------------------------------------------------
try:
import sphinxcontrib.spelling
except ImportError:
pass
else:
extensions.append("sphinxcontrib.spelling")
spelling_word_list_filename = 'spelling_wordlist.txt'
# -- 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",
}

2
docs/source/contributing/community.md
View File

@@ -19,7 +19,7 @@ We use [our Gitter channel](https://gitter.im/jupyterhub/jupyterhub) for online,
[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 are using a specific JupyterHub distribution (such as [Zero to JupyterHub on Kubernetes](https://github.com/jupyterhub/zero-to-jupyterhub-k8s) or [The Littlest JupyterHub](https://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}

76
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View File

@@ -0,0 +1,76 @@
(contributing-docs)=
# Contributing Documentation
Documentation is often more important than code. This page helps
you get set up on how to contribute to JupyterHub's documentation.
## Building documentation locally
We use [sphinx](https://www.sphinx-doc.org) to build our documentation. It takes
our documentation source files (written in [markdown](https://daringfireball.net/projects/markdown/) or [reStructuredText](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.
1. Make sure you have successfully completed {ref}`contributing/setup`.
2. Install the packages required to build the docs.
```bash
python3 -m pip install -r docs/requirements.txt
```
3. Build the html version of the docs. This is the most commonly used
output format, so verifying it renders correctly is usually good
enough.
```bash
cd docs
make html
```
This step will display any syntax or formatting errors in the documentation,
along with the filename / line number in which they occurred. Fix them,
and re-run the `make html` command to re-render the documentation.
4. View the rendered documentation by opening `_build/html/index.html` in
a web browser.
:::{tip}
**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`
:::
(contributing-docs-conventions)=
## Documentation conventions
This section lists various conventions we use in our documentation. This is a
living document that grows over time, so feel free to add to it / change it!
Our entire documentation does not yet fully conform to these conventions yet,
so help in making it so would be appreciated!
### `pip` invocation
There are many ways to invoke a `pip` command, we recommend the following
approach:
```bash
python3 -m pip
```
This invokes pip explicitly using the python3 binary that you are
currently using. This is the **recommended way** to invoke pip
in our documentation, since it is least likely to cause problems
with python3 and pip being from different environments.
For more information on how to invoke `pip` commands, see
[the pip documentation](https://pip.pypa.io/en/stable/).

79
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View File

@@ -1,79 +0,0 @@
.. _contributing/docs:
==========================
Contributing Documentation
==========================
Documentation is often more important than code. This page helps
you get set up on how to contribute documentation to JupyterHub.
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
<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 successfully completed :ref:`contributing/setup`.
#. Install the packages required to build the docs.
.. code-block:: bash
python3 -m pip install -r docs/requirements.txt
#. Build the html version of the docs. This is the most commonly used
output format, so verifying it renders as you should is usually good
enough.
.. code-block:: bash
cd docs
make html
This step will display any syntax or formatting errors in the documentation,
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.
.. tip::
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>``.
.. _contributing/docs/conventions:
Documentation conventions
=========================
This section lists various conventions we use in our documentation. This is a
living document that grows over time, so feel free to add to it / change it!
Our entire documentation does not yet fully conform to these conventions yet,
so help in making it so would be appreciated!
``pip`` invocation
------------------
There are many ways to invoke a ``pip`` command, we recommend the following
approach:
.. code-block:: bash
python3 -m pip
This invokes pip explicitly using the python3 binary that you are
currently using. This is the **recommended way** to invoke pip
in our documentation, since it is least likely to cause problems
with python3 and pip being from different environments.
For more information on how to invoke ``pip`` commands, see
`the pip documentation <https://pip.pypa.io/en/stable/>`_.

20
docs/source/contributing/index.md Normal file
View File

@@ -0,0 +1,20 @@
# 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
```

21
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View File

@@ -1,21 +0,0 @@
============
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

9
docs/source/contributing/security.md Normal file
View File

@@ -0,0 +1,9 @@
# Reporting security issues in Jupyter or JupyterHub
If you find a security vulnerability in Jupyter or JupyterHub,
whether it is a failure of the security model described in {doc}`../reference/websecurity`
or a failure in implementation,
please report it to <mailto:security@ipython.org>.
If you prefer to encrypt your security reports,
you can use {download}`this PGP public key </ipython_security.asc>`.

10
docs/source/contributing/security.rst
View File

@@ -1,10 +0,0 @@
Reporting security issues in Jupyter or JupyterHub
==================================================
If you find a security vulnerability in Jupyter or JupyterHub,
whether it is a failure of the security model described in :doc:`../reference/websecurity`
or a failure in implementation,
please report it to security@ipython.org.
If you prefer to encrypt your security reports,
you can use :download:`this PGP public key </ipython_security.asc>`.

175
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View File

@@ -0,0 +1,175 @@
(contributing/setup)=
# 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)
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 3.6 installed locally. If you haven’t
installed Python before, the recommended way to install it is to use
[Miniforge](https://github.com/conda-forge/miniforge#download).
### 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.
### Install git
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.
## Setting up a development install
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.
:::{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.
```bash
git clone https://github.com/jupyterhub/jupyterhub
cd jupyterhub
```
2. Make sure the `python` you installed and the `npm` you installed
are available to you on the command line.
```bash
python -V
```
This should return a version number greater than or equal to 3.6.
```bash
npm -v
```
This should return a version number greater than or equal to 5.0.
3. Install `configurable-http-proxy` (required to run and test the default JupyterHub configuration) and `yarn` (required to build some components):
```bash
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.
If you do not have access to sudo, you may instead run the following commands:
```bash
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.
If you are using conda you can instead run:
```bash
conda install configurable-http-proxy yarn
```
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.
```bash
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!
```bash
jupyterhub
```
7. You can access JupyterHub from your browser at
`http://localhost:8000` now.
Happy developing!
## Using DummyAuthenticator & SimpleLocalProcessSpawner
To simplify testing of JupyterHub, it is helpful to use
{class}`~jupyterhub.auth.DummyAuthenticator` instead of the default JupyterHub
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
configuration:
```bash
jupyterhub -f testing/jupyterhub_config.py
```
The default JupyterHub [authenticator](https://jupyterhub.readthedocs.io/en/stable/reference/authenticators.html#the-default-pam-authenticator)
& [spawner](https://jupyterhub.readthedocs.io/en/stable/api/spawner.html#localprocessspawner)
require your system to have user accounts for each user you want to log in to
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
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
parts, use only SimpleLocalProcessSpawner. Similarly, if you are working on
just spawner-related parts, use only DummyAuthenticator.
## Troubleshooting
This section lists common ways setting up your development environment may
fail, and how to fix them. Please add to the list if you encounter yet
another way it can fail!
### `lessc` not found
If the `python3 -m pip install --editable .` command fails and complains about
`lessc` being unavailable, you may need to explicitly install some
additional JavaScript dependencies:
```bash
npm install
```
This will fetch client-side JavaScript dependencies necessary to compile
CSS.
You may also need to manually update JavaScript and CSS after some
development updates, with:
```bash
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
```

191
docs/source/contributing/setup.rst
View File

@@ -1,191 +0,0 @@
.. _contributing/setup:
================================
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>`_
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 3.6 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,
and **not** the ‘Python 2’ version!
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``.
Install git
-----------
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.
Setting up a development install
================================
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.
.. 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
git clone https://github.com/jupyterhub/jupyterhub
cd jupyterhub
2. Make sure the ``python`` you installed and the ``npm`` you installed
are available to you on the command line.
.. code:: bash
python -V
This should return a version number greater than or equal to 3.6.
.. code:: bash
npm -v
This should return a version number greater than or equal to 5.0.
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 yarn
If you get an error that says ``Error: EACCES: permission denied``,
you might need to prefix the command with ``sudo``. If you do not
have access to sudo, you may instead run the following commands:
.. code:: bash
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.
If you are using conda you can instead run:
.. code:: bash
conda install configurable-http-proxy yarn
4. Install the python packages required for JupyterHub development.
.. code:: bash
python3 -m pip install -r dev-requirements.txt
python3 -m pip install -r requirements.txt
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. 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.
.. code:: bash
python3 -m pip install --editable .
7. You are now ready to start JupyterHub!
.. code:: bash
jupyterhub
8. You can access JupyterHub from your browser at
``http://localhost:8000`` now.
Happy developing!
Using DummyAuthenticator & SimpleLocalProcessSpawner
====================================================
To simplify testing of JupyterHub, it’s helpful to use
:class:`~jupyterhub.auth.DummyAuthenticator` instead of the default JupyterHub
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
configuration:
.. code:: bash
jupyterhub -f testing/jupyterhub_config.py
The default JupyterHub `authenticator
<https://jupyterhub.readthedocs.io/en/stable/reference/authenticators.html#the-default-pam-authenticator>`_
& `spawner
<https://jupyterhub.readthedocs.io/en/stable/api/spawner.html#localprocessspawner>`_
require your system to have user accounts for each user you want to log in to
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
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
parts, use only SimpleLocalProcessSpawner. Similarly, if you are working on
just spawner-related parts, use only DummyAuthenticator.
Troubleshooting
===============
This section lists common ways setting up your development environment may
fail, and how to fix them. Please add to the list if you encounter yet
another way it can fail!
``lessc`` not found
-------------------
If the ``python3 -m pip install --editable .`` command fails and complains about
``lessc`` being unavailable, you may need to explicitly install some
additional JavaScript dependencies:
.. code:: bash
npm install
This will fetch client-side JavaScript dependencies necessary to compile
CSS.
You may also need to manually update JavaScript and CSS after some
development updates, with:
.. code:: bash
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

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View File

@@ -0,0 +1,130 @@
(contributing-tests)=
# 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 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
1. Make sure you have completed {ref}`contributing/setup`.
Once you are done, you would be able to run `jupyterhub` from the command line and access it from your web browser.
This ensures that the dev environment is properly set up for tests to run.
2. You can run all tests in JupyterHub
```bash
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:
```bash
pytest -v --cov=jupyterhub jupyterhub/tests
```
3. You can also run tests in just a specific file:
```bash
pytest -v jupyterhub/tests/<test-file-name>
```
4. To run a specific test only, you can do:
```bash
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
developing a single test.
For example, to run the test `test_shutdown` in the file `test_api.py`,
you would run:
```bash
pytest -v jupyterhub/tests/test_api.py::test_shutdown
```
For more details, 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:
1. `test_api.py` tests the REST API
2. `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)
Refer to the [pytest fixtures documentation](https://pytest.readthedocs.io/en/latest/fixture.html) to learn how to use fixtures that exists already and to create new ones.
## Troubleshooting Test Failures
### All the tests are failing
Make sure you have completed all the steps in {ref}`contributing/setup` successfully, and are able to access JupyterHub from your browser at http://localhost:8000 after starting `jupyterhub` in your command line.
## Code formatting and linting
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.
```bash
pip install pre-commit
pre-commit install --install-hooks
```
To run pre-commit manually you would do:
```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.

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@@ -1,131 +0,0 @@
.. _contributing/tests:
===================================
Testing JupyterHub and linting code
===================================
Unit tests help confirm that JupyterHub works as intended, including after modifications are made. Additionally, they help in clarifying our expectations for our code.
JupyterHub uses `pytest <https://pytest.org>`_ for all 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`. 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 -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:
.. code-block:: bash
pytest -v --cov=jupyterhub jupyterhub/tests
#. You can also run tests in just a specific file:
.. code-block:: bash
pytest -v jupyterhub/tests/<test-file-name>
#. To run a specific test only, you can do:
.. code-block:: bash
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
developing a single test.
For example, to run the test ``test_shutdown`` in the file ``test_api.py``,
you would run:
.. code-block:: bash
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
=============================
All the tests are failing
-------------------------
Make sure you have completed all the steps in :ref:`contributing/setup` successfully, and are able to access JupyterHub from your browser at http://localhost:8000 after starting ``jupyterhub`` in your command line.
Code formatting and linting
===========================
JupyterHub has adopted automatic code formatting and linting.
As long as your code is valid, the pre-commit hook should take care of how it should look.
You can invoke the pre-commit hook manually at any time with:
.. code:: bash
pre-commit run
This should run any auto formatting on your code and tell you about any errors it couldn't fix automatically.
You may also install `black integration <https://github.com/psf/black#editor-integration>`_
into your text editor to format code automatically.
If you have already committed files before running pre-commit you can fix everything using:
.. code:: bash
pre-commit run --all-files
And committing the changes.

42
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@@ -0,0 +1,42 @@
# 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].
## 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:
```
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
```
[json schemas]: https://json-schema.org/
[logging]: https://docs.python.org/3/library/logging.html
[telemetry system]: https://github.com/jupyter/telemetry

46
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@@ -1,46 +0,0 @@
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

2
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View File

@@ -1 +1,3 @@
```{eval-rst}
.. jsonschema:: ../../../jupyterhub/event-schemas/server-actions/v1.yaml
```

42
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View File

@@ -20,13 +20,13 @@ Please submit pull requests to update information or to add new institutions or
- [GitHub organization](https://github.com/data-8)
- [NERSC](http://www.nersc.gov/)
- [NERSC](https://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/)
- [Press release on Jupyter and Cori](https://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)
- [Research IT](https://research-it.berkeley.edu)
- [JupyterHub server supports campus research computation](https://research-it.berkeley.edu/blog/17/01/24/free-fully-loaded-jupyterhub-server-supports-campus-research-computation)
### University of California Davis
@@ -71,25 +71,21 @@ easy to do with RStudio too.
### 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](https://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)
- [JupyterHub User Guide](https://curc.readthedocs.io/en/latest/gateways/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/)
- [Parallel Processing with JupyterHub tutorial](https://curc.readthedocs.io/en/latest/gateways/parallel-programming-jupyter.html)
### George Washington University
- [Jupyter Hub](http://go.gwu.edu/jupyter) with university single-sign-on. Deployed early 2017.
- [JupyterHub](https://go.gwu.edu/jupyter) with university single-sign-on. Deployed early 2017.
### HTCondor
@@ -101,7 +97,7 @@ easy to do with RStudio too.
### IllustrisTNG Simulation Project
- [JupyterHub/Lab-based analysis platform, part of the TNG public data release](http://www.tng-project.org/data/)
- [JupyterHub/Lab-based analysis platform, part of the TNG public data release](https://www.tng-project.org/data/)
### MIT and Lincoln Labs
@@ -121,17 +117,13 @@ easy to do with RStudio too.
### Paderborn University
- [Data Science (DICE) group](https://dice.cs.uni-paderborn.de/)
- [Data Science (DICE) group](https://dice-research.org)
- [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"
### University of Rochester CIRC
- [JupyterHub Userguide](https://info.circ.rochester.edu/Web_Applications/JupyterHub.html) - Slurm, beehive
### University of California San Diego
- San Diego Supercomputer Center - Andrea Zonca
@@ -144,7 +136,7 @@ easy to do with RStudio too.
- [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)
- [datahub.ucsd.edu](https://datahub.ucsd.edu)
### TACC University of Texas
@@ -180,7 +172,7 @@ easy to do with RStudio too.
### Rackspace Carina
- https://getcarina.com/blog/learning-how-to-whale/
- http://carolynvanslyck.com/talk/carina/jupyterhub/#/ (but carolynvanslyck is currently down; checked 10/26/22)
- https://carolynvanslyck.com/talk/carina/jupyterhub/#/ (but carolynvanslyck is currently down; checked 10/26/22)
### Hadoop
@@ -189,12 +181,12 @@ easy to do with RStudio too.
## Miscellaneous
- https://medium.com/@ybarraud/setting-up-jupyterhub-with-sudospawner-and-anaconda-844628c0dbee#.rm3yt87e1
- [Mailing list UT deployment](https://groups.google.com/forum/#!topic/jupyter/nkPSEeMr8c0)
- [Mailing list UT deployment](https://groups.google.com/g/jupyter/c/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
- [Deploy JupyterHub to Docker Swarm](https://jupyterhub.surge.sh)
- https://www.laketide.com/building-your-lab-part-3/
- https://estrellita.hatenablog.com/entry/2015/07/31/083202
- https://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/)

8
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@@ -11,6 +11,8 @@ This section will help you learn how to:
- configure JupyterHub using command line options
- find information and examples for some common deployments
(generate-config-file)=
## Generate a default config file
On startup, JupyterHub will look by default for a configuration file,
@@ -44,7 +46,7 @@ jupyterhub -f /etc/jupyterhub/jupyterhub_config.py
```
The IPython documentation provides additional information on the
[config system](http://ipython.readthedocs.io/en/stable/development/config.html)
[config system](https://ipython.readthedocs.io/en/stable/development/config.html)
that Jupyter uses.
## Configure using command line options
@@ -77,8 +79,8 @@ 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 illustrations, are:

3
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View File

@@ -16,7 +16,8 @@ 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?
**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).

24
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View File

@@ -1,19 +1,19 @@
Get 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
```{toctree}
:maxdepth: 2
config-basics
networking-basics
security-basics
authenticators-users-basics
spawners-basics
services-basics
faq
institutional-faq
config-basics
networking-basics
security-basics
authenticators-users-basics
spawners-basics
services-basics
faq
institutional-faq
```

33
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View File

@@ -8,10 +8,16 @@ broken down by their roles within organizations.
### 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
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?
@@ -26,7 +32,7 @@ Here is a quick breakdown of these three tools:
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
It also connects users with infrastructure they wish to access. It can provide
remote access to Jupyter Notebooks and JupyterLab for many people.
## For management
@@ -35,7 +41,7 @@ Here is a quick breakdown of these three tools:
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
the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also gives administrators
some control over access to resources, security, environments, and authentication.
### Is JupyterHub mature? Why should we trust it?
@@ -99,12 +105,12 @@ that we currently suggest are:
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
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.
**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.
@@ -118,14 +124,15 @@ as more resources are needed - allowing you to utilize the benefits of a flexibl
### Is JupyterHub secure?
The short answer: yes. JupyterHub as a standalone application has been battle-tested at an institutional
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).
[JupyterHub on Kubernetes security page](https://z2jh.jupyter.org/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
@@ -134,11 +141,11 @@ in these cases, and the security of your JupyterHub deployment will often depend
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.
individuals with experience running secure JupyterHub deployments and will be very glad to help you out.
### Does JupyterHub provide computing or data infrastructure?
No - JupyterHub manages user sessions and can _control_ computing infrastructure, but it does not provide these
**No** - JupyterHub manages user sessions and can _control_ computing infrastructure, but it does not provide these
things itself. You are expected to run JupyterHub on your own infrastructure (local or in the cloud). Moreover,
JupyterHub has no internal concept of "data", but is designed to be able to communicate with data repositories
(again, either locally or remotely) for use within interactive computing sessions.
@@ -191,7 +198,7 @@ complex computing infrastructures from the interactive sessions of a JupyterHub.
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
to serve as a gateway to high-performance computing 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?

6
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View File

@@ -41,9 +41,9 @@ port.
## Set the Proxy's REST API communication URL (optional)
By default, this REST API listens on port 8001 of `localhost` only.
The Hub service talks to the proxy via a REST API on a secondary port. The
API URL can be configured separately to override the default settings.
By default, the proxy's REST API listens on port 8081 of `localhost` only.
The Hub service talks to the proxy via a REST API on a secondary port.
The REST API URL (hostname and port) can be configured separately and override the default settings.
### Set api_url

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@@ -0,0 +1,234 @@
# Security settings
:::{important}
You should not run JupyterHub without SSL encryption on a public network.
:::
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
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
is still a good idea to revoke existing tokens.
(ssl-encryption)=
## Enabling SSL encryption
Since JupyterHub includes authentication and allows arbitrary code execution,
you should not run it without SSL (HTTPS).
### 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`
configuration file as follows:
```python
c.JupyterHub.ssl_key = '/path/to/my.key'
c.JupyterHub.ssl_cert = '/path/to/my.cert'
```
Some cert files also contain the key, in which case only the cert is needed. It
is important that these files be put in a secure location on your server, where
they are not readable by regular users.
If you are using a **chain certificate**, see also chained certificate for SSL
in the JupyterHub [Troubleshooting FAQ](../troubleshooting.md).
### Using letsencrypt
It is also possible to use [letsencrypt](https://letsencrypt.org/) to obtain
a free, trusted SSL certificate. If you run letsencrypt using the default
options, the needed configuration is (replace `mydomain.tld` by your fully
qualified domain name):
```python
c.JupyterHub.ssl_key = '/etc/letsencrypt/live/{mydomain.tld}/privkey.pem'
c.JupyterHub.ssl_cert = '/etc/letsencrypt/live/{mydomain.tld}/fullchain.pem'
```
If the fully qualified domain name (FQDN) is `example.com`, the following
would be the needed configuration:
```python
c.JupyterHub.ssl_key = '/etc/letsencrypt/live/example.com/privkey.pem'
c.JupyterHub.ssl_cert = '/etc/letsencrypt/live/example.com/fullchain.pem'
```
### If SSL termination happens outside of the Hub
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.
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`:
```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:
```bash
export CONFIGPROXY_AUTH_TOKEN=$(openssl rand -hex 32)
```
This environment variable needs to be visible to the Hub and Proxy.
### Default if token is not set
If you do not set the Proxy authentication token, the Hub will generate a random
key itself. This means that any time you restart the Hub, you **must also
restart the Proxy**. If the proxy is a subprocess of the Hub, this should happen
automatically (this is the default configuration).
(cookie-secret)=
## Cookie secret
The cookie secret is an encryption key, used to encrypt the browser cookies,
which are used for authentication. Three common methods are described for
generating and configuring the cookie secret.
### Generating and storing as a cookie secret file
The cookie secret should be 32 random bytes, encoded as hex, and is typically
stored in a `jupyterhub_cookie_secret` file. Below, is an example command to generate the
`jupyterhub_cookie_secret` file:
```bash
openssl rand -hex 32 > /srv/jupyterhub/jupyterhub_cookie_secret
```
In most deployments of JupyterHub, you should point this to a secure location on
the file system, such as `/srv/jupyterhub/jupyterhub_cookie_secret`.
The location of the `jupyterhub_cookie_secret` file can be specified in the
`jupyterhub_config.py` file as follows:
```python
c.JupyterHub.cookie_secret_file = '/srv/jupyterhub/jupyterhub_cookie_secret'
```
If the cookie secret file doesn't exist when the Hub starts, a new cookie
secret is generated and stored in the file. The file must not be readable by
`group` or `other`, otherwise the server won't start. The recommended permissions
for the cookie secret file are `600` (owner-only rw).
### Generating and storing as an environment variable
If you would like to avoid the need for files, the value can be loaded in the
Hub process from the `JPY_COOKIE_SECRET` environment variable, which is a
hex-encoded string. You can set it this way:
```bash
export JPY_COOKIE_SECRET=$(openssl rand -hex 32)
```
For security reasons, this environment variable should only be visible to the
Hub. If you set it dynamically as above, all users will be logged out each time
the Hub starts.
### Generating and storing as a binary string
You can also set the cookie secret, as a binary string,
in the configuration file (`jupyterhub_config.py`) itself:
```python
c.JupyterHub.cookie_secret = bytes.fromhex('64 CHAR HEX STRING')
```
(cookies)=
## Cookies used by JupyterHub authentication
The following cookies are used by the Hub for handling user authentication.
This section was created based on this [post] from Discourse.
### jupyterhub-hub-login
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.
Resetting the Hub cookie secret effectively revokes this cookie.
This cookie is restricted to the path `/hub/`.
### jupyterhub-user-\<username>
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.
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.
Each OAuth access token is associated with a session id (see `jupyterhub-session-id` section
below).
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.
Resetting the Hub cookie secret effectively revokes this cookie.
This cookie is restricted to the path `/user/<username>`,
to ensure that only the user’s server receives it.
### jupyterhub-session-id
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 the 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.
[post]: https://discourse.jupyter.org/t/how-to-force-re-login-for-users/1998/6

254
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View File

@@ -1,254 +0,0 @@
Security settings
=================
.. important::
You should not run JupyterHub without SSL encryption on a public network.
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
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
is still a good idea to revoke existing tokens.
.. _ssl-encryption:
Enabling SSL encryption
-----------------------
Since JupyterHub includes authentication and allows arbitrary code execution,
you should not run it without SSL (HTTPS).
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``
configuration file as follows:
.. code-block:: python
c.JupyterHub.ssl_key = '/path/to/my.key'
c.JupyterHub.ssl_cert = '/path/to/my.cert'
Some cert files also contain the key, in which case only the cert is needed. It
is important that these files be put in a secure location on your server, where
they are not readable by regular users.
If you are using a **chain certificate**, see also chained certificate for SSL
in the JupyterHub `Troubleshooting FAQ <../troubleshooting.html>`_.
Using letsencrypt
~~~~~~~~~~~~~~~~~
It is also possible to use `letsencrypt <https://letsencrypt.org/>`_ to obtain
a free, trusted SSL certificate. If you run letsencrypt using the default
options, the needed configuration is (replace ``mydomain.tld`` by your fully
qualified domain name):
.. code-block:: python
c.JupyterHub.ssl_key = '/etc/letsencrypt/live/{mydomain.tld}/privkey.pem'
c.JupyterHub.ssl_cert = '/etc/letsencrypt/live/{mydomain.tld}/fullchain.pem'
If the fully qualified domain name (FQDN) is ``example.com``, the following
would be the needed configuration:
.. code-block:: python
c.JupyterHub.ssl_key = '/etc/letsencrypt/live/example.com/privkey.pem'
c.JupyterHub.ssl_cert = '/etc/letsencrypt/live/example.com/fullchain.pem'
If SSL termination happens outside of the Hub
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.
To achieve this, simply omit the configuration settings
``c.JupyterHub.ssl_key`` and ``c.JupyterHub.ssl_cert``
(setting them to ``None`` does not have the same effect, but results 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 don't 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,
which are used for authentication. Three common methods are described for
generating and configuring the cookie secret.
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. Below, is an example command to generate the
``jupyterhub_cookie_secret`` file:
.. code-block:: bash
openssl rand -hex 32 > /srv/jupyterhub/jupyterhub_cookie_secret
In most deployments of JupyterHub, you should point this to a secure location on
the file system, such as ``/srv/jupyterhub/jupyterhub_cookie_secret``.
The location of the ``jupyterhub_cookie_secret`` file can be specified in the
``jupyterhub_config.py`` file as follows:
.. code-block:: python
c.JupyterHub.cookie_secret_file = '/srv/jupyterhub/jupyterhub_cookie_secret'
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``, 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
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you would like to avoid the need for files, the value can be loaded in the
Hub process from the ``JPY_COOKIE_SECRET`` environment variable, which is a
hex-encoded string. You can set it this way:
.. code-block:: bash
export JPY_COOKIE_SECRET=$(openssl rand -hex 32)
For security reasons, this environment variable should only be visible to the
Hub. If you set it dynamically as above, all users will be logged out each time
the Hub starts.
Generating and storing 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:
Cookies used by JupyterHub authentication
-----------------------------------------
The following cookies are used by the Hub for handling user authentication.
This section was created based on this post_ from Discourse.
.. _post: https://discourse.jupyter.org/t/how-to-force-re-login-for-users/1998/6
jupyterhub-hub-login
~~~~~~~~~~~~~~~~~~~~
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.
Resetting the Hub cookie secret effectively revokes this cookie.
This cookie is restricted to the path ``/hub/``.
jupyterhub-user-<username>
~~~~~~~~~~~~~~~~~~~~~~~~~~
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.
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.
Each OAuth access token is associated with a session id (see ``jupyterhub-session-id`` section
below).
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.
Resetting the Hub cookie secret effectively revokes this cookie.
This cookie is restricted to the path ``/user/<username>``,
to ensure that only the user’s server receives it.
jupyterhub-session-id
~~~~~~~~~~~~~~~~~~~~~
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.

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Upon restarting JupyterHub, you should see a message like below in the
logs:
```
```none
Adding API token for <username>
```

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=====
About
=====
# 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
[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
```{toctree}
:maxdepth: 1
contributor-list
changelog
gallery-jhub-deployments
contributor-list
changelog
gallery-jhub-deployments
```

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# 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/capacity-planning
admin/upgrading
admin/log-messages
changelog
```

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=====================
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

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# JupyterHub
[JupyterHub] is the best way to serve [Jupyter notebook] for multiple users.
Because JupyterHub manages a separate Jupyter environment for each user,
it can be used in a class of students, a corporate data science group, or a scientific
research group. It is a multi-user **Hub** that spawns, manages, and proxies multiple
instances of the single-user [Jupyter notebook] server.
JupyterHub offers distributions for different use cases. As of now, you can find two main cases:
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.
JupyterHub can be used in a collaborative environment by both both small (0-100 users) and
large teams (more than 100 users) such as a class of students, corporate data science group
or scientific research group. It has distributions which are developed to serve the needs of
each of these teams respectively.
JupyterHub is made up of four subsystems:
- 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
Additionally, optional configurations can be added through a `config.py` file and manage users
kernels on an admin panel. A simplification of the whole system is displayed in the figure below:
```{image} images/jhub-fluxogram.jpeg
:align: center
:alt: JupyterHub subsystems
:width: 80%
```
JupyterHub performs the following functions:
- The Hub launches a proxy
- The proxy forwards all requests to the Hub by default
- The Hub handles user login and spawns single-user servers on demand
- The Hub configures the proxy to forward URL prefixes to the single-user
notebook servers
For convenient administration of the Hub, its users, and services,
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).
## Contents
(index/distributions)=
### Distributions
A JupyterHub **distribution** is tailored
towards 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.
Today, you can find two main use 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 a larger number of machines and 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) distribution.
This distribution runs JupyterHub on top of [Kubernetes](https://k8s.io).
_It is important to evaluate these distributions before you can continue with the
configuration of JupyterHub_.
### Installation Guide
```{toctree}
:maxdepth: 2
installation-guide
```
### Getting Started
```{toctree}
:maxdepth: 2
getting-started/index
```
### Technical Reference
```{toctree}
:maxdepth: 2
reference/index
```
### Administrators guide
```{toctree}
:maxdepth: 2
index-admin
```
### API Reference
```{toctree}
:maxdepth: 2
api/index
```
### RBAC Reference
```{toctree}
:maxdepth: 2
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) and [reporting guidelines](https://github.com/jupyter/governance/blob/HEAD/conduct/reporting_online.md)
help keep our community welcoming to as many people as possible.
```{toctree}
:maxdepth: 2
contributing/index
```
### About JupyterHub
```{toctree}
:maxdepth: 2
index-about
```
## Indices and tables
- {ref}`genindex`
- {ref}`modindex`
## Questions? Suggestions?
All questions and suggestions are welcome. Please feel free to use our [Jupyter Discourse Forum](https://discourse.jupyter.org/) to contact our team.
Looking forward to hearing from you!
[jupyter notebook]: https://jupyter-notebook.readthedocs.io/en/latest/
[jupyterhub]: https://github.com/jupyterhub/jupyterhub

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==========
JupyterHub
==========
`JupyterHub`_ is the best way to serve `Jupyter notebook`_ for multiple users.
Because JupyterHub manages a separate Jupyter environment for each user,
it can be used in a class of students, a corporate data science group, or a scientific
research group. It is a multi-user **Hub** that spawns, manages, and proxies multiple
instances of the single-user `Jupyter notebook`_ server.
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. `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
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
:width: 80%
:align: center
JupyterHub performs the following functions:
- The Hub launches a proxy
- The proxy forwards all requests to the Hub by default
- The Hub handles user login and spawns single-user servers on demand
- The Hub configures the proxy to forward URL prefixes to the single-user
notebook servers
For convenient administration of the Hub, its users, and services,
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>`_.
Contents
========
.. _index/distributions:
Distributions
-------------
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:
* `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: 2
installation-guide
Getting Started
---------------
.. toctree::
:maxdepth: 2
getting-started/index
Technical Reference
-------------------
.. toctree::
:maxdepth: 2
reference/index
Administrators guide
--------------------
.. toctree::
:maxdepth: 2
index-admin
API Reference
-------------
.. toctree::
:maxdepth: 2
api/index
RBAC Reference
--------------
.. toctree::
:maxdepth: 2
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: 2
index-about
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
Questions? Suggestions?
=======================
- `Jupyter mailing list <https://groups.google.com/forum/#!forum/jupyter>`_
- `Jupyter website <https://jupyter.org>`_
.. _JupyterHub: https://github.com/jupyterhub/jupyterhub
.. _Jupyter notebook: https://jupyter-notebook.readthedocs.io/en/latest/

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@@ -16,7 +16,7 @@ minor Windows compatibility issues (such as basic installation) **may** be accep
however. For Windows-based systems, we would recommend running JupyterHub in a
docker container or Linux VM.
[Additional Reference:](http://www.tornadoweb.org/en/stable/#installation)
[Additional Reference:](https://www.tornadoweb.org/en/stable/#installation)
Tornado's documentation on Windows platform support
## Planning your installation
@@ -28,7 +28,7 @@ Prior to beginning installation, it's helpful to consider some of the following:
- Spawner of singleuser notebook servers (Docker, Batch, etc.)
- Services (nbgrader, etc.)
- JupyterHub database (default SQLite; traditional RDBMS such as PostgreSQL,)
MySQL, or other databases supported by [SQLAlchemy](http://www.sqlalchemy.org))
MySQL, or other databases supported by [SQLAlchemy](https://www.sqlalchemy.org))
## Folders and File Locations

7
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@@ -0,0 +1,7 @@
---
orphan: true
---
# JupyterHub the hard way
This guide has moved to <https://github.com/jupyterhub/jupyterhub-the-hard-way/blob/HEAD/docs/installation-guide-hard.md>

6
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@@ -1,6 +0,0 @@
: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

14
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@@ -1,13 +1,13 @@
Installation
============
# 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
```{toctree}
:maxdepth: 3
quickstart
quickstart-docker
installation-basics
quickstart
quickstart-docker
installation-basics
```

65
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@@ -0,0 +1,65 @@
# Install JupyterHub with Docker
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, [JupyterLab](https://jupyterlab.readthedocs.io/) or Jupyter Notebook must be installed.
:::
:::{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 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/)
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.
## 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.
[docker]: https://www.docker.com/
[zero to jupyterhub]: https://z2jh.jupyter.org/en/latest/

69
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@@ -1,69 +0,0 @@
Install JupyterHub with Docker
==============================
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, `JupyterLab <https://jupyterlab.readthedocs.io/>`_ or Jupyter Notebook must be installed.
.. 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 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 are running Docker on a computer that has a public IP address, you must **secure it with ssl** by adding ssl options to your docker
configuration or using a ssl enabled proxy.
`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.
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/

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@@ -4,10 +4,10 @@
Before installing JupyterHub, you will need:
- a Linux/Unix based system
- a Linux/Unix-based system
- [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
[`conda`](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-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.
@@ -80,7 +80,7 @@ To start the Hub server, run the command:
jupyterhub
```
Visit `http://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

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@@ -2,7 +2,7 @@
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_.
`<resource>` in the RBAC scope design refers to the resource name in the [JupyterHub's API](../reference/rest-api.md) endpoints in most cases. For instance, `<resource>` equal to `users` corresponds to JupyterHub's API endpoints beginning with _/users_.
(scope-conventions-target)=
@@ -298,6 +298,6 @@ 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).
The scopes are also listed in the [](../reference/rest-api.md) 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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@@ -11,7 +11,7 @@ No other database records are affected.
## 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.
2. To upgrade the Hub, follow the [Upgrading JupyterHub](../admin/upgrading.md) 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.
```
@@ -45,7 +45,7 @@ OAuth token is issued by the Hub to a single-user server when the user logs in.
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.
API tokens can also be issued to users via API ([_/hub/token_](../reference/urls.md) or [_POST /users/:username/tokens_](../reference/rest-api.md)) and services via `jupyterhub_config.py` to perform API requests.
### With RBAC

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@@ -3,7 +3,7 @@
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)
2. Match the actions against the [JupyterHub's APIs](../reference/rest-api.md)
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

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@@ -31,8 +31,7 @@ popular services:
- Okpy
- OpenShift
A generic implementation, which you can use for OAuth authentication
with any provider, is also available.
A [generic implementation](https://github.com/jupyterhub/oauthenticator/blob/master/oauthenticator/generic.py), which you can use for OAuth authentication with any provider, is also available.
## The Dummy Authenticator
@@ -165,7 +164,7 @@ setup(
```
If you have added this metadata to your package,
users can select your authenticator with the configuration:
admins can select your authenticator with the configuration:
```python
c.JupyterHub.authenticator_class = 'myservice'
@@ -298,7 +297,7 @@ all group-management via the API is disabled.
## pre_spawn_start and post_spawn_stop hooks
Authenticators uses two hooks, {meth}`.Authenticator.pre_spawn_start` and
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

8
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@@ -240,8 +240,8 @@ httpd.conf amendments:
jupyterhub_config.py amendments:
```bash
--The public-facing URL of the whole JupyterHub application.
--This is the address on which the proxy will bind. Sets protocol, IP, base_url
c.JupyterHub.bind_url = 'http://127.0.0.1:8000/jhub/'
```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
c.JupyterHub.bind_url = 'http://127.0.0.1:8000/jhub/'
```

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@@ -0,0 +1,29 @@
# 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](generate-config-file)
section, the `jupyterhub_config.py` can be automatically generated via
> ```bash
> jupyterhub --generate-config
> ```
The following contains the output of that command for reference.
```{eval-rst}
.. jupyterhub-generate-config::
```
## JupyterHub help command output
This section contains the output of the command `jupyterhub --help-all`.
```{eval-rst}
.. jupyterhub-help-all::
```

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@@ -1,30 +0,0 @@
==============================
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::

17
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@@ -6,14 +6,14 @@ Only do this if you are very sure you must.
## Overview
There are many [Authenticators](./authenticators-users-basics) and [Spawners](./spawners-basics) available for JupyterHub. Some, such
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 the Hub itself as root.
Since JupyterHub needs to spawn processes as other users, the simplest way
is to run it as root, spawning user servers with [setuid](http://linux.die.net/man/2/setuid).
is to run it as root, spawning user servers with [setuid](https://linux.die.net/man/2/setuid).
But this isn't especially safe, because you have a process running on the
public web as root.
@@ -69,7 +69,8 @@ Cmnd_Alias JUPYTER_CMD = /usr/local/bin/sudospawner
rhea ALL=(JUPYTER_USERS) NOPASSWD:JUPYTER_CMD
```
It might be useful to modify `secure_path` to add commands in path.
It might be useful to modify `secure_path` to add commands in path. (Search for
`secure_path` in the [sudo docs](https://www.sudo.ws/man/1.8.14/sudoers.man.html)
As an alternative to adding every user to the `/etc/sudoers` file, you can
use a group in the last line above, instead of `JUPYTER_USERS`:
@@ -113,7 +114,7 @@ sudo: a password is required
## Enable PAM for non-root
By default, [PAM authentication](http://en.wikipedia.org/wiki/Pluggable_authentication_module)
By default, [PAM authentication](https://en.wikipedia.org/wiki/Pluggable_authentication_module)
is used by JupyterHub. To use PAM, the process may need to be able to read
the shadow password database.
@@ -158,16 +159,18 @@ sudo setcap 'cap_net_bind_service=+ep' /usr/bin/node
```
However, you may want to further understand the consequences of this.
([Further reading](https://man7.org/linux/man-pages/man7/capabilities.7.html))
You may also be interested in limiting the amount of CPU any process can use
on your server. `cpulimit` is a useful tool that is available for many Linux
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).
instructions](https://ubuntuforums.org/showthread.php?t=992706).
### Shadow group (FreeBSD)
**NOTE:** This has not been tested and may not work as expected.
**NOTE:** This has not been tested on FreeBSD and may not work as expected on
the FreeBSD platform. _Do not use in production without verifying that it works properly!_
```bash
$ ls -l /etc/spwd.db /etc/master.passwd
@@ -225,7 +228,7 @@ And try logging in.
## Troubleshooting: SELinux
If you still get a generic `Permission denied` `PermissionError`, it's possible SELinux is blocking you.
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 resolve this.
First, put this in a file named `sudo_exec_selinux.te`:

26
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@@ -7,7 +7,7 @@ environment in a custom way.
Since the `jupyterhub-singleuser` server extends the standard Jupyter notebook
server, most configuration and documentation that applies to Jupyter Notebook
applies to the single-user environments. Configuration of user environments
typically does not occur through JupyterHub itself, but rather through the system-wide
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, 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.
@@ -37,7 +37,11 @@ sudo python3 -m pip install numpy
to install the numpy package in the default Python 3 environment on your system
(typically `/usr/local`).
Alternatively, You may also use conda to install packages. To do this, ensure that the conda environment has appropriate users permissions needed to run Python code in the environment.
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
@@ -53,6 +57,24 @@ The typical locations for these config files are:
- **system-wide** in `/etc/{jupyter|ipython}`
- **env-wide** (environment wide) in `{sys.prefix}/etc/{jupyter|ipython}`.
### Jupyter environment configuration priority
When Jupyter runs in an environment (conda or virtualenv), it prefers to load configuration from the environment over each user's own configuration (e.g. in `~/.jupyter`).
This may cause issues if you use a _shared_ conda environment or virtualenv for users, because e.g. jupyterlab may try to write information like workspaces or settings to the environment instead of the user's own directory.
This could fail with something like `Permission denied: $PREFIX/etc/jupyter/lab`.
To avoid this issue, set `JUPYTER_PREFER_ENV_PATH=0` in the user environment:
```python
c.Spawner.environment.update(
{
"JUPYTER_PREFER_ENV_PATH": "0",
}
)
```
which tells Jupyter to prefer _user_ configuration paths (e.g. in `~/.jupyter`) to configuration set in the environment.
### 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`:

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@@ -0,0 +1,32 @@
# 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
technical-overview
urls
websecurity
authenticators
spawners
services
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
```

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@@ -1,32 +0,0 @@
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
technical-overview
urls
websecurity
authenticators
spawners
services
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) 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).
```{toctree}
:maxdepth: 2
metrics
```

20
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@@ -1,20 +0,0 @@
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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@@ -141,7 +141,7 @@ async def delete_route(self, routespec):
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
`routespect`, of dicts whose keys are the same three arguments passed to
`routespec`, of dicts whose keys are the same three arguments passed to
`add_route` (`routespec`, `target`, `data`)
```python
@@ -207,7 +207,7 @@ setup(
```
If you have added this metadata to your package,
users can select your proxy with the configuration:
admins can select your authenticator with the configuration:
```python
c.JupyterHub.proxy_class = 'mything'
@@ -219,7 +219,7 @@ instead of the full
c.JupyterHub.proxy_class = 'mypackage:MyProxy'
```
previously required.
as previously required.
Additionally, configurable attributes for your proxy will
appear in jupyterhub help output and auto-generated configuration files
via `jupyterhub --generate-config`.

4
docs/source/reference/rest.md
View File

@@ -193,7 +193,7 @@ r.json()
The same API token can also authorize access to the [Jupyter Notebook REST API][]
provided by notebook servers managed by JupyterHub if it has the necessary `access:users:servers` scope.
provided by notebook servers managed by JupyterHub if it has the necessary `access:servers` scope.
(api-pagination)=
@@ -294,7 +294,7 @@ First you must enable named-servers by including the following setting in the `j
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/):
the following as part of the `config.yaml` file, as per the [tutorial](https://z2jh.jupyter.org/en/latest/):
```bash
hub:

4
docs/source/reference/separate-proxy.md
View File

@@ -13,8 +13,8 @@ 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 currently
actively starting the hub.
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). If you are using a different proxy, such

21
docs/source/reference/services.md
View File

@@ -61,7 +61,7 @@ If a service is also to be managed by the Hub, it has a few extra options:
A **Hub-Managed Service** is started by the Hub, and the Hub is responsible
for the Service's actions. A Hub-Managed Service can only be a local
subprocess of the Hub. The Hub will take care of starting the process and
restarts it if it stops.
restart the service if the service stops.
While Hub-Managed Services share some similarities with notebook Spawners,
there are no plans for Hub-Managed Services to support the same spawning
@@ -234,8 +234,17 @@ There are two levels of authentication with the Hub:
- {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`, either programmatically when constructing the class,
or via the `JUPYTERHUB_API_TOKEN` environment variable.
To use HubAuth, you must set the `.api_token` instance variable. This can be
done either programmatically when constructing the class, or via the
`JUPYTERHUB_API_TOKEN` environment variable. A number of the examples in the
root of the jupyterhub git repository set the `JUPYTERHUB_API_TOKEN` variable
so consider having a look at those for futher reading
([cull-idle](https://github.com/jupyterhub/jupyterhub/tree/master/examples/cull-idle),
[external-oauth](https://github.com/jupyterhub/jupyterhub/tree/master/examples/external-oauth),
[service-notebook](https://github.com/jupyterhub/jupyterhub/tree/master/examples/service-notebook)
and [service-whoiami](https://github.com/jupyterhub/jupyterhub/tree/master/examples/service-whoami))
(TODO: Where is this API TOKen set?)
Most of the logic for authentication implementation is found in the
{meth}`.HubAuth.user_for_token` methods,
@@ -249,7 +258,7 @@ which makes a request of the Hub, and returns:
"name": "username",
"groups": ["list", "of", "groups"],
"scopes": [
"access:users:servers!server=username/",
"access:servers!server=username/",
],
}
```
@@ -391,12 +400,12 @@ 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 [nbviewer README][nbviewer example] section on securing the notebook viewer,
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/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/
[requests]: https://docs.python-requests.org/en/master/
[services_auth]: ../api/services.auth.html
[nbviewer example]: https://github.com/jupyter/nbviewer#securing-the-notebook-viewer
[fastapi example]: https://github.com/jupyterhub/jupyterhub/tree/HEAD/examples/service-fastapi

52
docs/source/reference/spawners.md
View File

@@ -224,7 +224,7 @@ When `Spawner.start` is called, this dictionary is accessible as `self.user_opti
## Writing a custom spawner
If you are interested in building a custom spawner, you can read [this tutorial](http://jupyterhub-tutorial.readthedocs.io/en/latest/spawners.html).
If you are interested in building a custom spawner, you can read [this tutorial](https://jupyterhub-tutorial.readthedocs.io/en/latest/spawners.html).
### Registering custom Spawners via entry points
@@ -297,36 +297,36 @@ Additional variables can be specified via the `Spawner.environment` configuratio
The process environment is returned by `Spawner.get_env`, which specifies the following environment variables:
- JUPYTERHUB*SERVICE_URL - the \_bind* URL where the server should launch its HTTP server (`http://127.0.0.1:12345`).
- `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/`,
- `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.
- `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.
- `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/`),
- `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
- `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,
- `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.
- `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.
@@ -340,7 +340,8 @@ 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
spawner, does not support limits and guarantees. One of the spawners
that supports limits and guarantees is the `systemdspawner`.
that supports limits and guarantees is the
[`systemdspawner`](https://github.com/jupyterhub/systemdspawner).
### Memory Limits & Guarantees
@@ -400,9 +401,10 @@ container `ip` prior to starting and pass that to `.create_certs` (TODO: edit).
In general though, this method will not need to be changed and the default
`ip`/`dns` (localhost) info will suffice.
When `.create_certs` is run, it will `.create_certs` in a default, central
location specified by `c.JupyterHub.internal_certs_location`. For `Spawners`
that need access to these certs elsewhere (i.e. on another host altogether),
the `.move_certs` method can be overridden to move the certs appropriately.
Again, using `DockerSpawner` as an example, this would entail moving certs
to a directory that will get mounted into the container this spawner starts.
When `.create_certs` is run, it will create the certificates in a default,
central location specified by `c.JupyterHub.internal_certs_location`. For
`Spawners` that need access to these certs elsewhere (i.e. on another host
altogether), the `.move_certs` method can be overridden to move the certs
appropriately. Again, using `DockerSpawner` as an example, this would entail
moving certs to a directory that will get mounted into the container this
spawner starts.

2
docs/source/reference/technical-overview.md
View File

@@ -110,7 +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.rst).
in the [Cookie Secret section of the Security Settings document](../getting-started/security-basics.md).
The location of these files can be specified via configuration settings. It is
recommended that these files be stored in standard UNIX filesystem locations,

37
docs/source/reference/templates.md
View File

@@ -1,28 +1,29 @@
# 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
[Jinja](https://jinja.palletsprojects.com) 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
your own template(s), you can have complete control over JupyterHub's
appearance.
## Custom Templates
JupyterHub will look for custom templates in all of the paths in the
`JupyterHub.template_paths` configuration option, falling back on the
JupyterHub will look for custom templates in all paths included in the
`JupyterHub.template_paths` configuration option, falling back on these
[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
if no custom template(s) with specified name(s) are found. This fallback
behavior is new in version 0.9; previous versions searched only the paths
explicitly included in `template_paths`. You may override as many
or as few templates as you desire.
## Extending Templates
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/HEAD/share/jupyterhub/templates)
make extensive use of blocks, which allows you to customize parts of the
Jinja provides a mechanism to [extend templates](https://jinja.palletsprojects.com/en/3.0.x/templates/#template-inheritance).
A base template can define `block`(s) within itself that child templates can fill up or
supply content to. The
[JupyterHub default templates](https://github.com/jupyterhub/jupyterhub/tree/HEAD/share/jupyterhub/templates)
make extensive use of blocks, thus allowing you to customize parts of the
interface easily.
In general, a child template can extend a base template, `page.html`, by beginning with:
@@ -40,15 +41,15 @@ file with this block:
{% extends "templates/page.html" %}
```
By defining `block`s with same name as in the base template, child templates
By defining `block`s with the same name as in the base template, child templates
can replace those sections with custom content. The content from the base
template can be included with the `{{ super() }}` directive.
template can be included in the child template with the `{{ super() }}` directive.
### Example
To add an additional message to the spawn-pending page, below the existing
text about the server starting up, place this content in a file named
`spawn_pending.html` in a directory included in the
text about the server starting up, place the content below in a file named
`spawn_pending.html`. This directory must also be included in the
`JupyterHub.template_paths` configuration option.
```html
@@ -61,7 +62,7 @@ text about the server starting up, place this content in a file named
To add announcements to be displayed on a page, you have two options:
- Extend the page templates as described above
- [Extend the page templates as described above](#extending-templates)
- Use configuration variables
### Announcement Configuration Variables
@@ -71,10 +72,10 @@ 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).
Note that changing these variables require a restart, unlike direct
Note that changing these variables requires a restart, unlike direct
template extension.
You can get the same effect by extending templates, which allows you
Alternatively, you can get the same effect by extending templates, which allows you
to update the messages without restarting. Set
`c.JupyterHub.template_paths` as mentioned above, and then create a
template (for example, `login.html`) with:

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