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8 Commits

Author SHA1 Message Date
Min RK
7b2c89d46c Bump to 3.1.1 2023-01-27 12:53:10 +01:00
Min RK
2aa856269f Merge pull request #4318 from meeseeksmachine/auto-backport-of-pr-4316-on-3.x
Backport PR #4316 on branch 3.x (changelog for 3.1.1)
2023-01-27 12:52:36 +01:00
Min RK
9d7f3cd9a3 Backport PR #4316: changelog for 3.1.1 2023-01-27 11:49:35 +00:00
Min RK
767404ab33 Merge pull request #4317 from meeseeksmachine/auto-backport-of-pr-4303-on-3.x
Backport PR #4303 on branch 3.x (make sure event-schemas are installed)
2023-01-27 11:53:32 +01:00
Erik Sundell
3e608cfc38 Backport PR #4303: make sure event-schemas are installed 2023-01-27 10:42:41 +00:00
Min RK
193ebc970c Merge pull request #4315 from minrk/3.x
Backport PR #4302: sqlalchemy 2 compatibility
2023-01-27 11:31:16 +01:00
Min RK
3dccb5dd99 Backport PR #4302: sqlalchemy 2 compatibility 2023-01-27 11:02:54 +01:00
Min RK
43b0897d6e Bump to 3.1.1.dev 2023-01-27 09:14:47 +01:00
381 changed files with 16997 additions and 43617 deletions

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@@ -5,5 +5,6 @@ jupyterhub.sqlite
jupyterhub_config.py
node_modules
docs
.git
dist
build

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@@ -1,64 +0,0 @@
# dependabot.yaml reference: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
#
# Notes:
# - Status and logs from dependabot are provided at
# https://github.com/jupyterhub/jupyterhub/network/updates.
#
version: 2
updates:
# Maintain dependencies in our GitHub Workflows
- package-ecosystem: github-actions
directory: /
labels: [ci]
schedule:
interval: monthly
time: "05:00"
timezone: Etc/UTC
- package-ecosystem: npm
directory: /
groups:
# one big pull request for minor bumps
npm-minor:
patterns:
- "*"
update-types:
- minor
- patch
schedule:
interval: monthly
- package-ecosystem: npm
directory: /jsx
groups:
# one big pull request for minor bumps
jsx-minor:
patterns:
- "*"
update-types:
- minor
- patch
# group major bumps of react-related dependencies
jsx-react:
patterns:
- "react*"
- "redux*"
- "*react"
- "recompose"
update-types:
- major
# group major bumps of webpack-related dependencies
jsx-webpack:
patterns:
- "*webpack*"
- "@babel/*"
- "*-loader"
update-types:
- major
# group major bumps of jest-related dependencies
jsx-jest:
patterns:
- "*jest*"
- "*test*"
update-types:
- major
schedule:
interval: monthly

15
.github/dependabot.yml vendored Normal file
View File

@@ -0,0 +1,15 @@
# dependabot.yml reference: https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
#
# Notes:
# - Status and logs from dependabot are provided at
# https://github.com/jupyterhub/jupyterhub/network/updates.
#
version: 2
updates:
# Maintain dependencies in our GitHub Workflows
- package-ecosystem: github-actions
directory: "/"
schedule:
interval: weekly
time: "05:00"
timezone: "Etc/UTC"

View File

@@ -1,7 +1,7 @@
# This is a GitHub workflow defining a set of jobs with a set of steps.
# ref: https://docs.github.com/en/actions/learn-github-actions/workflow-syntax-for-github-actions
#
# Test build release artifacts (PyPI package) and publish them on
# Test build release artifacts (PyPI package, Docker images) and publish them on
# pushed git tags.
#
name: Release
@@ -28,22 +28,18 @@ on:
- "**"
workflow_dispatch:
permissions:
contents: read
jobs:
build-release:
runs-on: ubuntu-22.04
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v5
- uses: actions/setup-python@v6
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: "3.11"
cache: pip
python-version: "3.9"
- uses: actions/setup-node@v5
- uses: actions/setup-node@v3
with:
node-version: "20"
node-version: "14"
- name: install build requirements
run: |
@@ -71,7 +67,7 @@ jobs:
docker run --rm -v $PWD/dist:/dist:ro docker.io/library/python:3.9-slim-bullseye bash -c 'pip install /dist/jupyterhub-*.tar.gz'
# ref: https://github.com/actions/upload-artifact#readme
- uses: actions/upload-artifact@v4
- uses: actions/upload-artifact@v3
with:
name: jupyterhub-${{ github.sha }}
path: "dist/*"
@@ -85,3 +81,145 @@ jobs:
run: |
pip install twine
twine upload --skip-existing dist/*
publish-docker:
runs-on: ubuntu-20.04
services:
# So that we can test this in PRs/branches
local-registry:
image: registry:2
ports:
- 5000:5000
steps:
- name: Should we push this image to a public registry?
run: |
if [ "${{ startsWith(github.ref, 'refs/tags/') || (github.ref == 'refs/heads/main') }}" = "true" ]; then
# Empty => Docker Hub
echo "REGISTRY=" >> $GITHUB_ENV
else
echo "REGISTRY=localhost:5000/" >> $GITHUB_ENV
fi
- uses: actions/checkout@v3
# Setup docker to build for multiple platforms, see:
# https://github.com/docker/build-push-action/tree/v2.4.0#usage
# https://github.com/docker/build-push-action/blob/v2.4.0/docs/advanced/multi-platform.md
- name: Set up QEMU (for docker buildx)
uses: docker/setup-qemu-action@e81a89b1732b9c48d79cd809d8d81d79c4647a18 # associated tag: v1.0.2
- name: Set up Docker Buildx (for multi-arch builds)
uses: docker/setup-buildx-action@8c0edbc76e98fa90f69d9a2c020dcb50019dc325
with:
# Allows pushing to registry on localhost:5000
driver-opts: network=host
- name: Setup push rights to Docker Hub
# This was setup by...
# 1. Creating a Docker Hub service account "jupyterhubbot"
# 2. Creating a access token for the service account specific to this
# repository: https://hub.docker.com/settings/security
# 3. Making the account part of the "bots" team, and granting that team
# permissions to push to the relevant images:
# https://hub.docker.com/orgs/jupyterhub/teams/bots/permissions
# 4. Registering the username and token as a secret for this repo:
# https://github.com/jupyterhub/jupyterhub/settings/secrets/actions
if: env.REGISTRY != 'localhost:5000/'
run: |
docker login -u "${{ secrets.DOCKERHUB_USERNAME }}" -p "${{ secrets.DOCKERHUB_TOKEN }}"
# image: jupyterhub/jupyterhub
#
# https://github.com/jupyterhub/action-major-minor-tag-calculator
# If this is a tagged build this will return additional parent tags.
# E.g. 1.2.3 is expanded to Docker tags
# [{prefix}:1.2.3, {prefix}:1.2, {prefix}:1, {prefix}:latest] unless
# this is a backported tag in which case the newer tags aren't updated.
# For branches this will return the branch name.
# If GITHUB_TOKEN isn't available (e.g. in PRs) returns no tags [].
- name: Get list of jupyterhub tags
id: jupyterhubtags
uses: jupyterhub/action-major-minor-tag-calculator@v2
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/jupyterhub:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub
uses: docker/build-push-action@c56af957549030174b10d6867f20e78cfd7debc5
with:
context: .
platforms: linux/amd64,linux/arm64
push: true
# tags parameter must be a string input so convert `gettags` JSON
# array into a comma separated list of tags
tags: ${{ join(fromJson(steps.jupyterhubtags.outputs.tags)) }}
# image: jupyterhub/jupyterhub-onbuild
#
- name: Get list of jupyterhub-onbuild tags
id: onbuildtags
uses: jupyterhub/action-major-minor-tag-calculator@v2
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/jupyterhub-onbuild:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub-onbuild:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub-onbuild
uses: docker/build-push-action@c56af957549030174b10d6867f20e78cfd7debc5
with:
build-args: |
BASE_IMAGE=${{ fromJson(steps.jupyterhubtags.outputs.tags)[0] }}
context: onbuild
platforms: linux/amd64,linux/arm64
push: true
tags: ${{ join(fromJson(steps.onbuildtags.outputs.tags)) }}
# image: jupyterhub/jupyterhub-demo
#
- name: Get list of jupyterhub-demo tags
id: demotags
uses: jupyterhub/action-major-minor-tag-calculator@v2
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/jupyterhub-demo:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub-demo:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub-demo
uses: docker/build-push-action@c56af957549030174b10d6867f20e78cfd7debc5
with:
build-args: |
BASE_IMAGE=${{ fromJson(steps.onbuildtags.outputs.tags)[0] }}
context: demo-image
# linux/arm64 currently fails:
# ERROR: Could not build wheels for argon2-cffi which use PEP 517 and cannot be installed directly
# ERROR: executor failed running [/bin/sh -c python3 -m pip install notebook]: exit code: 1
platforms: linux/amd64
push: true
tags: ${{ join(fromJson(steps.demotags.outputs.tags)) }}
# image: jupyterhub/singleuser
#
- name: Get list of jupyterhub/singleuser tags
id: singleusertags
uses: jupyterhub/action-major-minor-tag-calculator@v2
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/singleuser:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/singleuser:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub/singleuser
uses: docker/build-push-action@c56af957549030174b10d6867f20e78cfd7debc5
with:
build-args: |
JUPYTERHUB_VERSION=${{ github.ref_type == 'tag' && github.ref_name || format('git:{0}', github.sha) }}
context: singleuser
platforms: linux/amd64,linux/arm64
push: true
tags: ${{ join(fromJson(steps.singleusertags.outputs.tags)) }}

View File

@@ -12,7 +12,7 @@ jobs:
action:
runs-on: ubuntu-latest
steps:
- uses: dessant/support-requests@v4
- uses: dessant/support-requests@v2
with:
github-token: ${{ github.token }}
support-label: "support"
@@ -25,7 +25,7 @@ jobs:
Our goal is to sustain a positive experience for both users and developers. We use GitHub issues for specific discussions related to changing a repository's content, and let the forum be where we can more generally help and inspire each other.
Thank you for being an active member of our community! :heart:
Thanks you for being an active member of our community! :heart:
close-issue: true
lock-issue: false
issue-lock-reason: "off-topic"

View File

@@ -29,9 +29,6 @@ on:
- "**"
workflow_dispatch:
permissions:
contents: read
env:
# UTF-8 content may be interpreted as ascii and causes errors without this.
LANG: C.UTF-8
@@ -39,82 +36,27 @@ env:
jobs:
validate-rest-api-definition:
runs-on: ubuntu-22.04
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v5
- uses: actions/setup-node@v5
with:
node-version: "20"
cache: npm
- uses: actions/checkout@v3
- name: Validate REST API definition
run: |
npx @redocly/cli lint
uses: char0n/swagger-editor-validate@v1.3.2
with:
definition-file: docs/source/_static/rest-api.yml
test-docs:
runs-on: ubuntu-22.04
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v5
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
# make rediraffecheckdiff requires git history to compare current
# commit with the main branch and previous releases.
fetch-depth: 0
- uses: actions/setup-python@v6
with:
python-version: "3.11"
cache: pip
cache-dependency-path: |
requirements.txt
docs/requirements.txt
python-version: "3.9"
- name: Install requirements
run: |
pip install -e . -r docs/requirements.txt pytest
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
# Output broken and permanently redirected links in a readable format
- name: check links
uses: manics/action-sphinx-linkcheck-summary@main
with:
docs-dir: docs
build-dir: docs/_build
# make rediraffecheckdiff compares files for different changesets
# these diff targets aren't always available
# - compare with base ref (usually 'main', always on 'origin') for pull requests
# - only compare with tags when running against jupyterhub/jupyterhub
# to avoid errors on forks, which often lack tags
- name: check redirects for this PR
if: github.event_name == 'pull_request'
run: |
cd docs
export REDIRAFFE_BRANCH=origin/${{ github.base_ref }}
make rediraffecheckdiff
# this should check currently published 'stable' links for redirects
- name: check redirects since last release
if: github.repository == 'jupyterhub/jupyterhub'
run: |
cd docs
export REDIRAFFE_BRANCH=$(git describe --tags --abbrev=0)
make rediraffecheckdiff
# longer-term redirect check (fixed version) for older links
- name: check redirects since 3.0.0
if: github.repository == 'jupyterhub/jupyterhub'
run: |
cd docs
export REDIRAFFE_BRANCH=3.0.0
make rediraffecheckdiff

View File

@@ -25,24 +25,28 @@ permissions:
jobs:
# The ./jsx folder contains React based source code files that are to compile
# to share/jupyterhub/static/js/admin-react.js. The ./jsx folder includes
# tests also has tests that this job is meant to run with `npm test`
# tests also has tests that this job is meant to run with `yarn test`
# according to the documentation in jsx/README.md.
test-jsx-admin-react:
runs-on: ubuntu-22.04
runs-on: ubuntu-20.04
timeout-minutes: 5
steps:
- uses: actions/checkout@v5
- uses: actions/setup-node@v5
- uses: actions/checkout@v3
- uses: actions/setup-node@v3
with:
node-version: "20"
node-version: "14"
- name: install jsx
- name: Install yarn
run: |
npm install -g yarn
- name: yarn
run: |
cd jsx
npm ci
yarn
- name: test
- name: yarn test
run: |
cd jsx
npm test
yarn test

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@@ -28,6 +28,7 @@ on:
env:
# UTF-8 content may be interpreted as ascii and causes errors without this.
LANG: C.UTF-8
PYTEST_ADDOPTS: "--verbose --color=yes"
SQLALCHEMY_WARN_20: "1"
permissions:
@@ -36,7 +37,7 @@ permissions:
jobs:
# Run "pytest jupyterhub/tests" in various configurations
pytest:
runs-on: ubuntu-22.04
runs-on: ubuntu-20.04
timeout-minutes: 15
strategy:
@@ -65,7 +66,7 @@ jobs:
# unencrypted HTTP
#
# main_dependencies:
# Tests everything when we use the latest available dependencies
# Tests everything when the we use the latest available dependencies
# from: traitlets.
#
# NOTE: Since only the value of these parameters are presented in the
@@ -74,39 +75,22 @@ jobs:
# Python versions available at:
# https://github.com/actions/python-versions/blob/HEAD/versions-manifest.json
include:
- python: "3.8"
- python: "3.7"
oldest_dependencies: oldest_dependencies
legacy_notebook: legacy_notebook
- python: "3.8"
jupyter_server: "1.*"
subset: singleuser
legacy_notebook: legacy_notebook
- python: "3.9"
db: mysql
- python: "3.10"
db: postgres
- python: "3.12"
- python: "3.11"
subdomain: subdomain
serverextension: serverextension
- python: "3.11"
ssl: ssl
serverextension: serverextension
- python: "3.11"
jupyverse: jupyverse
subset: singleuser
selenium: selenium
- python: "3.11"
subdomain: subdomain
noextension: noextension
subset: singleuser
- python: "3.11"
ssl: ssl
noextension: noextension
subset: singleuser
- python: "3.11"
browser: browser
- python: "3.11"
subdomain: subdomain
browser: browser
- python: "3.12"
main_dependencies: main_dependencies
steps:
@@ -120,7 +104,7 @@ jobs:
fi
if [ "${{ matrix.db }}" == "mysql" ]; then
echo "MYSQL_HOST=127.0.0.1" >> $GITHUB_ENV
echo "JUPYTERHUB_TEST_DB_URL=mysql+mysqldb://root@127.0.0.1:3306/jupyterhub" >> $GITHUB_ENV
echo "JUPYTERHUB_TEST_DB_URL=mysql+mysqlconnector://root@127.0.0.1:3306/jupyterhub" >> $GITHUB_ENV
fi
if [ "${{ matrix.ssl }}" == "ssl" ]; then
echo "SSL_ENABLED=1" >> $GITHUB_ENV
@@ -131,77 +115,56 @@ jobs:
echo "PGPASSWORD=hub[test/:?" >> $GITHUB_ENV
echo "JUPYTERHUB_TEST_DB_URL=postgresql://test_user:hub%5Btest%2F%3A%3F@127.0.0.1:5432/jupyterhub" >> $GITHUB_ENV
fi
if [ "${{ matrix.serverextension }}" != "" ]; then
echo "JUPYTERHUB_SINGLEUSER_EXTENSION=1" >> $GITHUB_ENV
elif [ "${{ matrix.noextension }}" != "" ]; then
echo "JUPYTERHUB_SINGLEUSER_EXTENSION=0" >> $GITHUB_ENV
if [ "${{ matrix.jupyter_server }}" != "" ]; then
echo "JUPYTERHUB_SINGLEUSER_APP=jupyterhub.tests.mockserverapp.MockServerApp" >> $GITHUB_ENV
fi
if [ "${{ matrix.jupyverse }}" != "" ]; then
echo "JUPYTERHUB_SINGLEUSER_APP=jupyverse" >> $GITHUB_ENV
fi
- uses: actions/checkout@v5
# NOTE: actions/setup-node@v5 make use of a cache within the GitHub base
- uses: actions/checkout@v3
# NOTE: actions/setup-node@v3 make use of a cache within the GitHub base
# environment and setup in a fraction of a second.
- name: Install Node
uses: actions/setup-node@v5
- name: Install Node v14
uses: actions/setup-node@v3
with:
node-version: "20"
node-version: "14"
- name: Install Javascript dependencies
run: |
npm install
npm install -g configurable-http-proxy yarn
npm list
# NOTE: actions/setup-python@v6 make use of a cache within the GitHub base
# NOTE: actions/setup-python@v4 make use of a cache within the GitHub base
# environment and setup in a fraction of a second.
- name: Install Python ${{ matrix.python }}
uses: actions/setup-python@v6
uses: actions/setup-python@v4
with:
python-version: "${{ matrix.python }}"
cache: pip
cache-dependency-path: |
pyproject.toml
requirements.txt
ci/oldest-dependencies/requirements.old
- name: Install Python dependencies
run: |
pip install --upgrade pip
pip install -e ".[test]"
if [ "${{ matrix.oldest_dependencies }}" != "" ]; then
# frozen env with oldest dependencies
# make sure our `>=` pins really do express our minimum supported versions
pip install -r ci/oldest-dependencies/requirements.old -e .
else
pip install --pre -e ".[test]" "pycurl; python_version >= '3.10'"
# take any dependencies in requirements.txt such as tornado>=5.0
# and transform them to tornado==5.0 so we can run tests with
# the earliest-supported versions
cat requirements.txt | grep '>=' | sed -e 's@>=@==@g' > oldest-requirements.txt
pip install -r oldest-requirements.txt
fi
if [ "${{ matrix.main_dependencies }}" != "" ]; then
# Tests are broken:
# https://github.com/jupyterhub/jupyterhub/issues/4418
# pip install git+https://github.com/ipython/traitlets#egg=traitlets --force
pip install git+https://github.com/ipython/traitlets#egg=traitlets --force
pip install --upgrade --pre sqlalchemy
fi
if [ "${{ matrix.legacy_notebook }}" != "" ]; then
pip uninstall jupyter_server --yes
pip install 'notebook<7'
fi
if [ "${{ matrix.jupyter_server }}" != "" ]; then
pip install "jupyter_server==${{ matrix.jupyter_server }}"
fi
if [ "${{ matrix.jupyverse }}" != "" ]; then
pip install "jupyverse[jupyterlab,auth-jupyterhub]"
pip install -e .
fi
if [ "${{ matrix.db }}" == "mysql" ]; then
pip install mysqlclient
pip install mysql-connector-python
fi
if [ "${{ matrix.db }}" == "postgres" ]; then
pip install psycopg2-binary
fi
if [ "${{ matrix.serverextension }}" != "" ]; then
pip install 'jupyter-server>=2'
fi
pip freeze
@@ -245,17 +208,44 @@ jobs:
DB=postgres bash ci/docker-db.sh
DB=postgres bash ci/init-db.sh
fi
- name: Setup Firefox
if: matrix.selenium
uses: browser-actions/setup-firefox@latest
with:
firefox-version: latest
- name: Configure browser tests
if: matrix.browser
run: echo "PYTEST_ADDOPTS=$PYTEST_ADDOPTS -m browser" >> "${GITHUB_ENV}"
- name: Setup Geckodriver
if: matrix.selenium
uses: browser-actions/setup-geckodriver@latest
- name: Ensure browsers are installed for playwright
if: matrix.browser
run: python -m playwright install --with-deps firefox
- name: Configure selenium tests
if: matrix.selenium
run: echo "PYTEST_ADDOPTS=$PYTEST_ADDOPTS -m selenium" >> "${GITHUB_ENV}"
- name: Run pytest
run: |
pytest -k "${{ matrix.subset }}" --maxfail=2 --cov=jupyterhub jupyterhub/tests
pytest --maxfail=2 --cov=jupyterhub jupyterhub/tests
- uses: codecov/codecov-action@v5
- uses: codecov/codecov-action@v3
docker-build:
runs-on: ubuntu-20.04
timeout-minutes: 20
steps:
- uses: actions/checkout@v3
- name: build images
run: |
docker build -t jupyterhub/jupyterhub .
docker build -t jupyterhub/jupyterhub-onbuild onbuild
docker build -t jupyterhub/jupyterhub:alpine -f dockerfiles/Dockerfile.alpine .
docker build -t jupyterhub/singleuser singleuser
- name: smoke test jupyterhub
run: |
docker run --rm -t jupyterhub/jupyterhub jupyterhub --help
- name: verify static files
run: |
docker run --rm -t -v $PWD/dockerfiles:/io jupyterhub/jupyterhub python3 /io/test.py

12
.gitignore vendored
View File

@@ -7,21 +7,17 @@ node_modules
dist
docs/_build
docs/build
docs/source/reference/metrics.md
docs/source/_static/rest-api
docs/source/rbac/scope-table.md
.ipynb_checkpoints
.virtual_documents
jsx/build/
# ignore config file at the top-level of the repo
# but not sub-dirs
/jupyterhub_config.py
jupyterhub_cookie_secret
jupyterhub.sqlite
jupyterhub.sqlite*
package-lock.json
share/jupyterhub/static/components
share/jupyterhub/static/css/style.css
share/jupyterhub/static/css/style.css.map
share/jupyterhub/static/css/style.min.css
share/jupyterhub/static/css/style.min.css.map
share/jupyterhub/static/js/admin-react.js*
@@ -38,5 +34,3 @@ docs/source/reference/metrics.rst
oldest-requirements.txt
jupyterhub-proxy.pid
examples/server-api/service-token
*.hot-update*

View File

@@ -8,48 +8,45 @@
# - 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 and lint Python code
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.12.11
# Autoformat: Python code, syntax patterns are modernized
- repo: https://github.com/asottile/pyupgrade
rev: v3.2.2
hooks:
- id: ruff
types_or:
- python
- jupyter
args: ["--fix", "--show-fixes"]
- id: ruff-format
types_or:
- python
- jupyter
- id: pyupgrade
args:
- --py36-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
hooks:
- id: isort
# Autoformat: Python code
- repo: https://github.com/psf/black
rev: 22.10.0
hooks:
- id: black
# Autoformat: markdown, yaml, javascript (see the file .prettierignore)
- repo: https://github.com/rbubley/mirrors-prettier
rev: v3.6.2
- repo: https://github.com/pre-commit/mirrors-prettier
rev: v3.0.0-alpha.4
hooks:
- id: prettier
exclude: .*/templates/.*|docs/source/_static/rest-api.yml|docs/source/rbac/scope-table.md
# autoformat HTML templates
- repo: https://github.com/djlint/djLint
rev: v1.36.4
hooks:
- id: djlint-reformat-jinja
files: ".*templates/.*.html"
types_or: ["html"]
exclude: redoc.html
- id: djlint-jinja
files: ".*templates/.*.html"
types_or: ["html"]
# Autoformat and linting, misc. details
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v6.0.0
rev: v4.4.0
hooks:
- id: end-of-file-fixer
exclude: share/jupyterhub/static/js/admin-react.js
@@ -57,30 +54,8 @@ repos:
- id: check-case-conflict
- id: check-executables-have-shebangs
# source docs: rest-api.yml and scope-table.md are autogenerated
- repo: local
# Linting: Python code (see the file .flake8)
- repo: https://github.com/PyCQA/flake8
rev: "6.0.0"
hooks:
- id: update-api-and-scope-docs
name: Update rest-api.yml and scope-table.md based on scopes.py
language: python
additional_dependencies: ["pytablewriter", "ruamel.yaml"]
entry: python docs/source/rbac/generate-scope-table.py
args:
- --update
files: jupyterhub/scopes.py
pass_filenames: false
# run eslint in the jsx directory
# need to pass through 'jsx:install-run' hook in
# top-level package.json to ensure dependencies are installed
# eslint pre-commit hook doesn't really work with eslint 9,
# so use `npm run lint:fix`
- id: jsx-eslint
name: eslint in jsx/
entry: npm run jsx:install-run lint:fix
pass_filenames: false
language: node
files: "jsx/.*"
# can't run on pre-commit; hangs, for some reason
stages:
- manual
- id: flake8

View File

@@ -1,4 +1,2 @@
share/jupyterhub/templates/
share/jupyterhub/static/js/admin-react.js
jupyterhub/singleuser/templates/
docs/source/_templates/

View File

@@ -8,13 +8,13 @@ sphinx:
configuration: docs/source/conf.py
build:
os: ubuntu-24.04
os: ubuntu-20.04
tools:
python: "3.13"
nodejs: "16"
python: "3.9"
python:
install:
- path: .
- requirements: docs/requirements.txt
formats:

View File

@@ -12,29 +12,3 @@ Please see our documentation on
- [Testing JupyterHub and linting code](https://jupyterhub.readthedocs.io/en/latest/contributing/tests.html)
If you need some help, feel free to ask on [Gitter](https://gitter.im/jupyterhub/jupyterhub) or [Discourse](https://discourse.jupyter.org/).
## Our Copyright Policy
Jupyter uses a shared copyright model. Each contributor maintains copyright
over their contributions to Jupyter. But, it is important to note that these
contributions are typically only changes to the repositories. Thus, the Jupyter
source code, in its entirety is not the copyright of any single person or
institution. Instead, it is the collective copyright of the entire Jupyter
Development Team. If individual contributors want to maintain a record of what
changes/contributions they have specific copyright on, they should indicate
their copyright in the commit message of the change, when they commit the
change to one of the Jupyter repositories.
With this in mind, the following banner should be used in any source code file
to indicate the copyright and license terms:
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
### About the Jupyter Development Team
The Jupyter Development Team is the set of all contributors to the Jupyter project.
This includes all of the Jupyter subprojects.
The team that coordinates JupyterHub subproject can be found here:
https://compass.hub.jupyter.org/page/governance.html

59
COPYING.md Normal file
View File

@@ -0,0 +1,59 @@
# The Jupyter multi-user notebook server licensing terms
Jupyter multi-user notebook server is licensed under the terms of the Modified BSD License
(also known as New or Revised or 3-Clause BSD), as follows:
- Copyright (c) 2014-, Jupyter Development Team
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this
list of conditions and the following disclaimer in the documentation and/or
other materials provided with the distribution.
Neither the name of the Jupyter Development Team nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
## About the Jupyter Development Team
The Jupyter Development Team is the set of all contributors to the Jupyter project.
This includes all of the Jupyter subprojects.
The core team that coordinates development on GitHub can be found here:
https://github.com/jupyter/.
## Our Copyright Policy
Jupyter uses a shared copyright model. Each contributor maintains copyright
over their contributions to Jupyter. But, it is important to note that these
contributions are typically only changes to the repositories. Thus, the Jupyter
source code, in its entirety is not the copyright of any single person or
institution. Instead, it is the collective copyright of the entire Jupyter
Development Team. If individual contributors want to maintain a record of what
changes/contributions they have specific copyright on, they should indicate
their copyright in the commit message of the change, when they commit the
change to one of the Jupyter repositories.
With this in mind, the following banner should be used in any source code file
to indicate the copyright and license terms:
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.

103
Dockerfile Normal file
View File

@@ -0,0 +1,103 @@
# An incomplete base Docker image for running JupyterHub
#
# Add your configuration to create a complete derivative Docker image.
#
# Include your configuration settings by starting with one of two options:
#
# Option 1:
#
# FROM jupyterhub/jupyterhub:latest
#
# And put your configuration file jupyterhub_config.py in /srv/jupyterhub/jupyterhub_config.py.
#
# Option 2:
#
# Or you can create your jupyterhub config and database on the host machine, and mount it with:
#
# docker run -v $PWD:/srv/jupyterhub -t jupyterhub/jupyterhub
#
# NOTE
# If you base on jupyterhub/jupyterhub-onbuild
# your jupyterhub_config.py will be added automatically
# from your docker directory.
ARG BASE_IMAGE=ubuntu:22.04
FROM $BASE_IMAGE AS builder
USER root
ENV DEBIAN_FRONTEND noninteractive
RUN apt-get update \
&& apt-get install -yq --no-install-recommends \
build-essential \
ca-certificates \
locales \
python3-dev \
python3-pip \
python3-pycurl \
python3-venv \
nodejs \
npm \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
RUN python3 -m pip install --upgrade setuptools pip build wheel
RUN npm install --global yarn
# copy everything except whats in .dockerignore, its a
# compromise between needing to rebuild and maintaining
# what needs to be part of the build
COPY . /src/jupyterhub/
WORKDIR /src/jupyterhub
# Build client component packages (they will be copied into ./share and
# packaged with the built wheel.)
RUN python3 -m build --wheel
RUN python3 -m pip wheel --wheel-dir wheelhouse dist/*.whl
FROM $BASE_IMAGE
USER root
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update \
&& apt-get install -yq --no-install-recommends \
ca-certificates \
curl \
gnupg \
locales \
python3-pip \
python3-pycurl \
nodejs \
npm \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
ENV SHELL=/bin/bash \
LC_ALL=en_US.UTF-8 \
LANG=en_US.UTF-8 \
LANGUAGE=en_US.UTF-8
RUN locale-gen $LC_ALL
# always make sure pip is up to date!
RUN python3 -m pip install --no-cache --upgrade setuptools pip
RUN npm install -g configurable-http-proxy@^4.2.0 \
&& rm -rf ~/.npm
# install the wheels we built in the first stage
COPY --from=builder /src/jupyterhub/wheelhouse /tmp/wheelhouse
RUN python3 -m pip install --no-cache /tmp/wheelhouse/*
RUN mkdir -p /srv/jupyterhub/
WORKDIR /srv/jupyterhub/
EXPOSE 8000
LABEL maintainer="Jupyter Project <jupyter@googlegroups.com>"
LABEL org.jupyter.service="jupyterhub"
CMD ["jupyterhub"]

11
LICENSE
View File

@@ -1,11 +0,0 @@
Copyright 2014-, Jupyter Development Team
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

View File

@@ -1,13 +1,29 @@
# using setuptools-scm means we only need to handle _non-tracked files here_
include README.md
include COPYING.md
include setupegg.py
include bower-lite
include package.json
include package-lock.json
include *requirements.txt
include Dockerfile
# include untracked js/css artifacts, components
graft onbuild
graft jsx
graft jupyterhub
graft scripts
graft share
graft singleuser
graft ci
# prune some large unused files from components.
# these patterns affect source distributions (sdists)
# we have stricter exclusions from installation in setup.py:get_data_files
# Documentation
graft docs
prune docs/node_modules
# Intermediate javascript files
prune jsx/node_modules
prune jsx/build
# prune some large unused files from components
prune share/jupyterhub/static/components/bootstrap/dist/css
exclude share/jupyterhub/static/components/bootstrap/dist/fonts/*.svg
prune share/jupyterhub/static/components/font-awesome/css
@@ -17,3 +33,11 @@ prune share/jupyterhub/static/components/jquery/external
prune share/jupyterhub/static/components/jquery/src
prune share/jupyterhub/static/components/moment/lang
prune share/jupyterhub/static/components/moment/min
# Patterns to exclude from any directory
global-exclude *~
global-exclude *.pyc
global-exclude *.pyo
global-exclude .git
global-exclude .ipynb_checkpoints
global-exclude .bower.json

View File

@@ -8,12 +8,22 @@
---
Please note that this repository is participating in a study into the sustainability of open source projects. Data will be gathered about this repository for approximately the next 12 months, starting from 2021-06-11.
Data collected will include the number of contributors, number of PRs, time taken to close/merge these PRs, and issues closed.
For more information, please visit
[our informational page](https://sustainable-open-science-and-software.github.io/) or download our [participant information sheet](https://sustainable-open-science-and-software.github.io/assets/PIS_sustainable_software.pdf).
---
# [JupyterHub](https://github.com/jupyterhub/jupyterhub)
[![Latest PyPI version](https://img.shields.io/pypi/v/jupyterhub?logo=pypi)](https://pypi.python.org/pypi/jupyterhub)
[![Latest conda-forge version](https://img.shields.io/conda/vn/conda-forge/jupyterhub?logo=conda-forge)](https://anaconda.org/conda-forge/jupyterhub)
[![Documentation build status](https://img.shields.io/readthedocs/jupyterhub?logo=read-the-docs)](https://jupyterhub.readthedocs.org/en/latest/)
[![GitHub Workflow Status - Test](https://img.shields.io/github/workflow/status/jupyterhub/jupyterhub/Test?logo=github&label=tests)](https://github.com/jupyterhub/jupyterhub/actions)
[![DockerHub build status](https://img.shields.io/docker/build/jupyterhub/jupyterhub?logo=docker&label=build)](https://hub.docker.com/r/jupyterhub/jupyterhub/tags)
[![Test coverage of code](https://codecov.io/gh/jupyterhub/jupyterhub/branch/main/graph/badge.svg)](https://codecov.io/gh/jupyterhub/jupyterhub)
[![GitHub](https://img.shields.io/badge/issue_tracking-github-blue?logo=github)](https://github.com/jupyterhub/jupyterhub/issues)
[![Discourse](https://img.shields.io/badge/help_forum-discourse-blue?logo=discourse)](https://discourse.jupyter.org/c/jupyterhub)
@@ -56,8 +66,9 @@ for administration of the Hub and its users.
### Check prerequisites
- A Linux/Unix based system
- [Python](https://www.python.org/downloads/) 3.8 or greater
- [Python](https://www.python.org/downloads/) 3.6 or greater
- [nodejs/npm](https://www.npmjs.com/)
- If you are using **`conda`**, the nodejs and npm dependencies will be installed for
you by conda.
@@ -110,13 +121,13 @@ Visit `http://localhost:8000` in your browser, and sign in with your system user
_Note_: To allow multiple users to sign in to the server, you will need to
run the `jupyterhub` command as a _privileged user_, such as root.
The [documentation](https://jupyterhub.readthedocs.io/en/latest/howto/configuration/config-sudo.html)
The [wiki](https://github.com/jupyterhub/jupyterhub/wiki/Using-sudo-to-run-JupyterHub-without-root-privileges)
describes how to run the server as a _less privileged user_, which requires
more configuration of the system.
## Configuration
The [Getting Started](https://jupyterhub.readthedocs.io/en/latest/tutorial/index.html#getting-started) section of the
The [Getting Started](https://jupyterhub.readthedocs.io/en/latest/getting-started/index.html) section of the
documentation explains the common steps in setting up JupyterHub.
The [**JupyterHub tutorial**](https://github.com/jupyterhub/jupyterhub-tutorial)
@@ -158,10 +169,10 @@ To start the Hub on a specific url and port `10.0.1.2:443` with **https**:
## Docker
A starter [**docker image for JupyterHub**](https://quay.io/repository/jupyterhub/jupyterhub)
A starter [**docker image for JupyterHub**](https://hub.docker.com/r/jupyterhub/jupyterhub/)
gives a baseline deployment of JupyterHub using Docker.
**Important:** This `quay.io/jupyterhub/jupyterhub` image contains only the Hub itself,
**Important:** This `jupyterhub/jupyterhub` image contains only the Hub itself,
with no configuration. In general, one needs to make a derivative image, with
at least a `jupyterhub_config.py` setting up an Authenticator and/or a Spawner.
To run the single-user servers, which may be on the same system as the Hub or
@@ -169,7 +180,7 @@ not, Jupyter Notebook version 4 or greater must be installed.
The JupyterHub docker image can be started with the following command:
docker run -p 8000:8000 -d --name jupyterhub quay.io/jupyterhub/jupyterhub jupyterhub
docker run -p 8000:8000 -d --name jupyterhub jupyterhub/jupyterhub jupyterhub
This command will create a container named `jupyterhub` that you can
**stop and resume** with `docker stop/start`.
@@ -219,7 +230,7 @@ docker container or Linux VM.
We use a shared copyright model that enables all contributors to maintain the
copyright on their contributions.
All code is licensed under the terms of the [revised BSD license](./LICENSE).
All code is licensed under the terms of the [revised BSD license](./COPYING.md).
## Help and resources
@@ -228,9 +239,9 @@ You can also talk with us on our JupyterHub [Gitter](https://gitter.im/jupyterhu
- [Reporting Issues](https://github.com/jupyterhub/jupyterhub/issues)
- [JupyterHub tutorial](https://github.com/jupyterhub/jupyterhub-tutorial)
- [Documentation for JupyterHub](https://jupyterhub.readthedocs.io/en/latest/)
- [Documentation for JupyterHub](https://jupyterhub.readthedocs.io/en/latest/) | [PDF (latest)](https://media.readthedocs.org/pdf/jupyterhub/latest/jupyterhub.pdf) | [PDF (stable)](https://media.readthedocs.org/pdf/jupyterhub/stable/jupyterhub.pdf)
- [Documentation for JupyterHub's REST API][rest api]
- [Documentation for Project Jupyter](http://jupyter.readthedocs.io/en/latest/index.html)
- [Documentation for Project Jupyter](http://jupyter.readthedocs.io/en/latest/index.html) | [PDF](https://media.readthedocs.org/pdf/jupyter/latest/jupyter.pdf)
- [Project Jupyter website](https://jupyter.org)
- [Project Jupyter community](https://jupyter.org/community)

View File

@@ -1,5 +1,5 @@
# Reporting a Vulnerability
If you believe youve found a security vulnerability in a Jupyter
project, please report it!
See the [security documentation](https://jupyterhub.readthedocs.org/en/latest/contributing/security.html) for how.
project, please report it to security@ipython.org. If you prefer to
encrypt your security reports, you can use [this PGP public key](https://jupyter-notebook.readthedocs.io/en/stable/_downloads/1d303a645f2505a8fd283826fafc9908/ipython_security.asc).

View File

@@ -7,7 +7,6 @@ bower-lite
Since Bower's on its way out,
stage frontend dependencies from node_modules into components
"""
import json
import os
import shutil

View File

@@ -12,7 +12,7 @@ print(f"DATA_FILES_PATH={DATA_FILES_PATH}", end=" ")
DATA_FILES_PATH = Path(DATA_FILES_PATH)
assert DATA_FILES_PATH.is_dir(), DATA_FILES_PATH
for subpath in (
"templates/spawn.html",
"templates/page.html",
"static/css/style.min.css",
"static/components/jquery/dist/jquery.js",
"static/js/admin-react.js",
@@ -28,7 +28,6 @@ for subpath in (
"alembic.ini",
"alembic/versions/833da8570507_rbac.py",
"event-schemas/server-actions/v1.yaml",
"singleuser/templates/page.html",
):
path = jupyterhub_path / subpath
assert path.is_file(), path

View File

@@ -21,7 +21,7 @@ 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_110 _upgrade_122 _upgrade_130 _upgrade_150 _upgrade_211 _upgrade_311; 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

View File

@@ -1,13 +0,0 @@
alembic==1.4
async_generator==1.9
certipy==0.1.2
importlib_metadata==3.6; python_version < '3.10'
jinja2==2.11.0
jupyter_telemetry==0.1.0
oauthlib==3.0
pamela==1.1.0; sys_platform != 'win32'
prometheus_client==0.5.0
psutil==5.6.5; sys_platform == 'win32'
SQLAlchemy==1.4.1
tornado==5.1
traitlets==4.3.2

View File

@@ -1,20 +0,0 @@
# oldest-dependencies.txt is autogenerated.
# recreate with:
# cat requirements.txt | grep '>=' | sed -e 's@>=@==@g' > ci/legacy-env/oldest-dependencies.txt
-r ./oldest-dependencies.txt
# then `pip-compile` with Python 3.8
# below are additional pins to make this a working test env
# these are extracted from jupyterhub[test]
beautifulsoup4
coverage
playwright
pytest
pytest-cov
pytest-asyncio==0.17.*
requests-mock
virtualenv
# and any additional pins to make this a working test env
# e.g. pinning down a transitive dependency
notebook==6.*
markupsafe==2.0.*

View File

@@ -1,285 +0,0 @@
#
# This file is autogenerated by pip-compile with Python 3.8
# by the following command:
#
# pip-compile --output-file=requirements.old
#
alembic==1.4.0
# via -r ./oldest-dependencies.txt
appnope==0.1.3
# via
# ipykernel
# ipython
argon2-cffi==23.1.0
# via notebook
argon2-cffi-bindings==21.2.0
# via argon2-cffi
async-generator==1.9
# via -r ./oldest-dependencies.txt
attrs==23.1.0
# via
# jsonschema
# referencing
backcall==0.2.0
# via ipython
beautifulsoup4==4.12.2
# via -r requirements.in
bleach==6.0.0
# via nbconvert
certifi==2023.7.22
# via requests
certipy==0.1.2
# via -r ./oldest-dependencies.txt
cffi==1.15.1
# via
# argon2-cffi-bindings
# cryptography
charset-normalizer==3.2.0
# via requests
coverage[toml]==7.3.1
# via
# -r requirements.in
# pytest-cov
cryptography==41.0.4
# via pyopenssl
debugpy==1.8.0
# via ipykernel
decorator==5.1.1
# via
# ipython
# traitlets
defusedxml==0.7.1
# via nbconvert
distlib==0.3.7
# via virtualenv
entrypoints==0.4
# via
# jupyter-client
# nbconvert
exceptiongroup==1.1.3
# via pytest
fastjsonschema==2.18.0
# via nbformat
filelock==3.12.4
# via virtualenv
greenlet==2.0.2
# via
# playwright
# sqlalchemy
idna==3.4
# via requests
importlib-metadata==3.6.0 ; python_version < "3.10"
# via -r ./oldest-dependencies.txt
importlib-resources==6.1.0
# via
# jsonschema
# jsonschema-specifications
iniconfig==2.0.0
# via pytest
ipykernel==6.4.2
# via notebook
ipython==7.34.0
# via ipykernel
ipython-genutils==0.2.0
# via
# ipykernel
# notebook
# traitlets
jedi==0.19.0
# via ipython
jinja2==2.11.0
# via
# -r ./oldest-dependencies.txt
# nbconvert
# notebook
jsonschema==4.19.1
# via
# jupyter-telemetry
# nbformat
jsonschema-specifications==2023.7.1
# via jsonschema
jupyter-client==7.2.0
# via
# ipykernel
# nbclient
# notebook
jupyter-core==5.0.0
# via
# jupyter-client
# nbconvert
# nbformat
# notebook
jupyter-telemetry==0.1.0
# via -r ./oldest-dependencies.txt
jupyterlab-pygments==0.2.2
# via nbconvert
mako==1.2.4
# via alembic
markupsafe==2.0.1
# via
# -r requirements.in
# jinja2
# mako
matplotlib-inline==0.1.6
# via
# ipykernel
# ipython
mistune==0.8.4
# via nbconvert
nbclient==0.5.11
# via nbconvert
nbconvert==6.0.7
# via notebook
nbformat==5.3.0
# via
# nbclient
# nbconvert
# notebook
nest-asyncio==1.5.8
# via
# jupyter-client
# nbclient
notebook==6.1.6
# via -r requirements.in
oauthlib==3.0.0
# via -r ./oldest-dependencies.txt
packaging==23.1
# via pytest
pamela==1.1.0 ; sys_platform != "win32"
# via -r ./oldest-dependencies.txt
pandocfilters==1.5.0
# via nbconvert
parso==0.8.3
# via jedi
pexpect==4.8.0
# via ipython
pickleshare==0.7.5
# via ipython
pkgutil-resolve-name==1.3.10
# via jsonschema
platformdirs==3.10.0
# via
# jupyter-core
# virtualenv
playwright==1.38.0
# via -r requirements.in
pluggy==1.3.0
# via pytest
prometheus-client==0.5.0
# via
# -r ./oldest-dependencies.txt
# notebook
prompt-toolkit==3.0.39
# via ipython
ptyprocess==0.7.0
# via
# pexpect
# terminado
pycparser==2.21
# via cffi
pyee==9.0.4
# via playwright
pygments==2.16.1
# via
# ipython
# nbconvert
pyopenssl==23.2.0
# via certipy
pytest==7.4.2
# via
# -r requirements.in
# pytest-asyncio
# pytest-cov
pytest-asyncio==0.17.2
# via -r requirements.in
pytest-cov==4.1.0
# via -r requirements.in
python-dateutil==2.8.2
# via
# alembic
# jupyter-client
python-editor==1.0.4
# via alembic
python-json-logger==2.0.7
# via jupyter-telemetry
pyzmq==25.1.1
# via
# jupyter-client
# notebook
referencing==0.30.2
# via
# jsonschema
# jsonschema-specifications
requests==2.31.0
# via requests-mock
requests-mock==1.11.0
# via -r requirements.in
rpds-py==0.10.3
# via
# jsonschema
# referencing
ruamel-yaml==0.17.32
# via jupyter-telemetry
ruamel-yaml-clib==0.2.7
# via ruamel-yaml
send2trash==1.8.2
# via notebook
six==1.16.0
# via
# bleach
# python-dateutil
# requests-mock
# traitlets
soupsieve==2.5
# via beautifulsoup4
sqlalchemy==1.4.1
# via
# -r ./oldest-dependencies.txt
# alembic
terminado==0.13.3
# via notebook
testpath==0.6.0
# via nbconvert
tomli==2.0.1
# via
# coverage
# pytest
tornado==5.1
# via
# -r ./oldest-dependencies.txt
# ipykernel
# jupyter-client
# notebook
# terminado
traitlets==4.3.2
# via
# -r ./oldest-dependencies.txt
# ipykernel
# ipython
# jupyter-client
# jupyter-core
# jupyter-telemetry
# matplotlib-inline
# nbclient
# nbconvert
# nbformat
# notebook
typing-extensions==4.8.0
# via
# playwright
# pyee
urllib3==2.0.5
# via requests
virtualenv==20.24.5
# via -r requirements.in
wcwidth==0.2.6
# via prompt-toolkit
webencodings==0.5.1
# via bleach
zipp==3.17.0
# via
# importlib-metadata
# importlib-resources
# The following packages are considered to be unsafe in a requirements file:
# setuptools

16
demo-image/Dockerfile Normal file
View File

@@ -0,0 +1,16 @@
# Demo JupyterHub Docker image
#
# This should only be used for demo or testing and not as a base image to build on.
#
# It includes the notebook package and it uses the DummyAuthenticator and the SimpleLocalProcessSpawner.
ARG BASE_IMAGE=jupyterhub/jupyterhub-onbuild
FROM ${BASE_IMAGE}
# Install the notebook package
RUN python3 -m pip install notebook
# Create a demo user
RUN useradd --create-home demo
RUN chown demo .
USER demo

26
demo-image/README.md Normal file
View File

@@ -0,0 +1,26 @@
## Demo Dockerfile
This is a demo JupyterHub Docker image to help you get a quick overview of what
JupyterHub is and how it works.
It uses the SimpleLocalProcessSpawner to spawn new user servers and
DummyAuthenticator for authentication.
The DummyAuthenticator allows you to log in with any username & password and the
SimpleLocalProcessSpawner allows starting servers without having to create a
local user for each JupyterHub user.
### Important!
This should only be used for demo or testing purposes!
It shouldn't be used as a base image to build on.
### Try it
1. `cd` to the root of your jupyterhub repo.
2. Build the demo image with `docker build -t jupyterhub-demo demo-image`.
3. Run the demo image with `docker run -d -p 8000:8000 jupyterhub-demo`.
4. Visit http://localhost:8000 and login with any username and password
5. Happy demo-ing :tada:!

View File

@@ -0,0 +1,7 @@
# Configuration file for jupyterhub-demo
c = get_config()
# Use DummyAuthenticator and SimpleSpawner
c.JupyterHub.spawner_class = "simple"
c.JupyterHub.authenticator_class = "dummy"

View File

@@ -0,0 +1,14 @@
FROM alpine:3.13
ENV LANG=en_US.UTF-8
RUN apk add --no-cache \
python3 \
py3-pip \
py3-ruamel.yaml \
py3-cryptography \
py3-sqlalchemy
ARG JUPYTERHUB_VERSION=1.3.0
RUN pip3 install --no-cache jupyterhub==${JUPYTERHUB_VERSION}
USER nobody
CMD ["jupyterhub"]

22
dockerfiles/README.md Normal file
View File

@@ -0,0 +1,22 @@
## What is Dockerfile.alpine
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.
## Steps to test it outside a cluster
- start configurable-http-proxy in another container
- specify CONFIGPROXY_AUTH_TOKEN env in both containers
- 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 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"

14
dockerfiles/test.py Normal file
View File

@@ -0,0 +1,14 @@
import os
from jupyterhub._data import DATA_FILES_PATH
print(f"DATA_FILES_PATH={DATA_FILES_PATH}")
for sub_path in (
"templates",
"static/components",
"static/css/style.min.css",
"static/js/admin-react.js",
):
path = os.path.join(DATA_FILES_PATH, sub_path)
assert os.path.exists(path), path

View File

@@ -1,58 +1,212 @@
# Makefile for Sphinx documentation generated by sphinx-quickstart
# ----------------------------------------------------------------------------
# Makefile for Sphinx documentation
#
# 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
# 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
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS)
@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"
.PHONY: help Makefile metrics scopes
clean:
rm -rf $(BUILDDIR)/*
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option.
metrics: source/reference/metrics.rst
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
# If the pre-requisites for the html target is updated, also update the Read The
# Docs section in docs/source/conf.py.
#
# Several sphinx-build commands can be used through this, for example:
#
# - make clean
# - make linkcheck
# - make spelling
#
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS)
# 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
$(SPHINXBUILD) -b html "$(SOURCEDIR)" "$(BUILDDIR)/html" $(SPHINXOPTS)
html: metrics scopes
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
metrics: source/reference/metrics.md
source/reference/metrics.md:
python3 generate-metrics.py
dirhtml:
$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
singlehtml:
$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
@echo
@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
# Manually added targets - related to development
# ----------------------------------------------------------------------------
pickle:
$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
@echo
@echo "Build finished; now you can process the pickle files."
# 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 files
# - starts a livereload enabled webserver and opens up a browser
devenv: html
sphinx-autobuild -b html --open-browser "$(SOURCEDIR)" "$(BUILDDIR)/html"
json:
$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
@echo
@echo "Build finished; now you can process the JSON files."
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."

View File

@@ -1,6 +1,7 @@
import os
from pytablewriter import MarkdownTableWriter
from pytablewriter import RstSimpleTableWriter
from pytablewriter.style import Style
import jupyterhub.metrics
@@ -10,11 +11,12 @@ HERE = os.path.abspath(os.path.dirname(__file__))
class Generator:
@classmethod
def create_writer(cls, table_name, headers, values):
writer = MarkdownTableWriter()
writer = RstSimpleTableWriter()
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):
@@ -31,17 +33,18 @@ class Generator:
if not os.path.exists(generated_directory):
os.makedirs(generated_directory)
filename = f"{generated_directory}/metrics.md"
filename = f"{generated_directory}/metrics.rst"
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("# List of Prometheus Metrics\n\n")
f.write(writer.dumps())
f.write("\n")
print(f"Generated {filename}")
f.write(content)
print(f"Generated {filename}.")
def main():

View File

@@ -1,49 +1,263 @@
@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 SOURCEDIR=source
set BUILDDIR=_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%
)
if "%1" == "" goto help
if "%1" == "devenv" goto devenv
goto default
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
)
:default
%SPHINXBUILD% >NUL 2>NUL
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
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Open and read README.md!
exit /b 1
)
%SPHINXBUILD% -M %1 "%SOURCEDIR%" "%BUILDDIR%" %SPHINXOPTS%
goto end
:help
%SPHINXBUILD% -M help "%SOURCEDIR%" "%BUILDDIR%" %SPHINXOPTS%
goto end
:devenv
sphinx-autobuild >NUL 2>NUL
if errorlevel 9009 (
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.The 'sphinx-autobuild' command was not found. Open and read README.md!
echo.If you don't have Sphinx installed, grab it from
echo.http://sphinx-doc.org/
exit /b 1
)
sphinx-autobuild -b html --open-browser "%SOURCEDIR%" "%BUILDDIR%/html"
goto end
:sphinx_ok
if "%1" == "html" (
%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
if errorlevel 1 exit /b 1
echo.
echo.Build finished. The HTML pages are in %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

View File

@@ -1,11 +1,17 @@
# docs also require jupyterhub itself to be installed
# don't depend on it here, as that often results in a duplicate
# installation of jupyterhub that's already installed
# 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 .
autodoc-traits
intersphinx-registry
jupyterhub-sphinx-theme
myst-parser>=0.19
myst-parser
pre-commit
pydata-sphinx-theme
pytablewriter>=0.56
ruamel.yaml
sphinx>=4

File diff suppressed because it is too large Load Diff

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@@ -1,2 +0,0 @@
{%- set _meta = meta | default({}) %}
{%- extends _meta.page_template | default('!page.html') %}

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@@ -1,32 +0,0 @@
{# djlint: off #}
{%- extends "!layout.html" %}
{# not sure why, but theme CSS prevents scrolling within redoc content
# If this were fixed, we could keep the navbar and footer
#}
{% block css %}
{% endblock css %}
{% block docs_navbar %}
{% endblock docs_navbar %}
{% block footer %}
{% endblock footer %}
{%- block body_tag -%}<body>{%- endblock body_tag %}
{%- block extrahead %}
{{ super() }}
<link href="{{ pathto('_static/redoc-fonts.css', 1) }}" rel="stylesheet" />
<script src="{{ pathto('_static/redoc.js', 1) }}"></script>
{%- endblock extrahead %}
{%- block content %}
<redoc id="redoc-spec"></redoc>
<script>
if (location.protocol === "file:") {
document.body.innerText = "Rendered API specification doesn't work with file: protocol. Use sphinx-autobuild to do local builds of the docs, served over HTTP."
} else {
Redoc.init(
"{{ pathto('_static/rest-api.yml', 1) }}",
{{ meta.redoc_options | default ({}) }},
document.getElementById("redoc-spec"),
);
}
</script>
{%- endblock content %}
{# djlint: on #}

View File

@@ -1,5 +1,3 @@
(explanation:capacity-planning)=
# Capacity planning
General capacity planning advice for JupyterHub is hard to give,
@@ -42,7 +40,7 @@ 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
```[figure} ../images/mybinder-hub-components-cpu-memory.png
JupyterHub component resource usage for mybinder.org.
```
@@ -70,7 +68,7 @@ but which are **less predictable**.
[the-littlest-jupyterhub]: https://the-littlest-jupyterhub.readthedocs.io
[zero-to-jupyterhub]: https://z2jh.jupyter.org
[zero-to-jupyterhub]: https://zero-to-jupyterhub.readthedocs.io
(limits-requests)=
@@ -202,13 +200,13 @@ 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
```[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**,
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.
@@ -279,7 +277,7 @@ 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)
![Snapshot from JupyterHub's Grafana dashboards on mybinder.org](../images/mybinder-user-resources.png)
[prometheus]: https://prometheus.io
[grafana]: https://grafana.com
@@ -301,10 +299,10 @@ There are lots of other resources for cost and capacity planning that may be spe
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)
- [Zero to JupyterHub](https://zero-to-jupyterhub.readthedocs.io) documentation on
- [projecting costs](https://zero-to-jupyterhub.readthedocs.io/en/latest/administrator/cost.html)
- [configuring user resources](https://zero-to-jupyterhub.readthedocs.io/en/latest/jupyterhub/customizing/user-resources.html)
- Cloud platform cost calculators:
- [Google Cloud](https://cloud.google.com/products/calculator/)
- [Amazon AWS](https://calculator.aws)
- [Amazon AWS](https://calculator.s3.amazonaws.com)
- [Microsoft Azure](https://azure.microsoft.com/en-us/pricing/calculator/)

View File

@@ -1,5 +1,3 @@
(howto:log-messages)=
# Interpreting common log messages
When debugging errors and outages, looking at the logs emitted by
@@ -71,4 +69,4 @@ aligned, rather than as an indicator of an existing problem.
Upgrade the version of the `jupyterhub` package in your user environment or image
so that it matches the version of JupyterHub running your JupyterHub server! If you
are using the [zero-to-jupyterhub](https://z2jh.jupyter.org) helm chart, you can find the appropriate
version of the `jupyterhub` package to install in your user image [here](https://hub.jupyter.org/helm-chart/)
version of the `jupyterhub` package to install in your user image [here](https://jupyterhub.github.io/helm-chart/)

View File

@@ -0,0 +1,155 @@
====================
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:
#. 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!

15
docs/source/api/app.rst Normal file
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@@ -0,0 +1,15 @@
=========================
Application configuration
=========================
Module: :mod:`jupyterhub.app`
=============================
.. automodule:: jupyterhub.app
.. currentmodule:: jupyterhub.app
:class:`JupyterHub`
-------------------
.. autoconfigurable:: JupyterHub

32
docs/source/api/auth.rst Normal file
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@@ -0,0 +1,32 @@
==============
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

33
docs/source/api/index.rst Normal file
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@@ -0,0 +1,33 @@
.. _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/

22
docs/source/api/proxy.rst Normal file
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@@ -0,0 +1,22 @@
=======
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

View File

@@ -0,0 +1,16 @@
========
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

View File

@@ -0,0 +1,40 @@
=======================
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

View File

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

36
docs/source/api/user.rst Normal file
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@@ -0,0 +1,36 @@
=====
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.

File diff suppressed because one or more lines are too long

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@@ -6,27 +6,22 @@ import contextlib
import datetime
import io
import os
import re
import subprocess
from pathlib import Path
from urllib.request import urlretrieve
from docutils import nodes
from intersphinx_registry import get_intersphinx_mapping
from ruamel.yaml import YAML
from sphinx.directives.other import SphinxDirective
from sphinx.util import logging
import jupyterhub
from jupyterhub.app import JupyterHub
logger = logging.getLogger(__name__)
# -- Project information -----------------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
#
project = "JupyterHub"
author = "Project Jupyter Contributors"
copyright = f"{datetime.date.today().year}, {author}"
version = "%i.%i" % jupyterhub.version_info[:2]
release = jupyterhub.__version__
# -- General Sphinx configuration --------------------------------------------
@@ -41,41 +36,23 @@ extensions = [
"sphinx-jsonschema",
"sphinxext.opengraph",
"sphinxext.rediraffe",
"jupyterhub_sphinx_theme",
"myst_parser",
]
root_doc = "index"
source_suffix = [".md"]
source_suffix = [".md", ".rst"]
# default_role let's use use `foo` instead of ``foo`` in rST
default_role = "literal"
docs = Path(__file__).parent.parent.absolute()
docs_source = docs / "source"
rest_api_yaml = docs_source / "_static" / "rest-api.yml"
# -- 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
"attrs_inline",
"colon_fence",
"deflist",
"fieldlist",
"substitution",
]
myst_substitutions = {
# date example: Dev 07, 2022
"date": datetime.date.today().strftime("%b %d, %Y").title(),
"node_min": "12",
"python_min": "3.8",
"version": jupyterhub.__version__,
}
# -- Custom directives to generate documentation -----------------------------
# ref: https://myst-parser.readthedocs.io/en/latest/syntax/roles-and-directives.html
@@ -132,103 +109,10 @@ class HelpAllDirective(SphinxDirective):
return [par]
class RestAPILinksDirective(SphinxDirective):
"""Directive to populate link targets for the REST API
The resulting nodes resolve xref targets,
but are not actually rendered in the final result
which is handled by a custom template.
"""
has_content = False
required_arguments = 0
optional_arguments = 0
final_argument_whitespace = False
option_spec = {}
def run(self):
targets = []
yaml = YAML(typ="safe")
with rest_api_yaml.open() as f:
api = yaml.load(f)
for path, path_spec in api["paths"].items():
for method, operation in path_spec.items():
operation_id = operation.get("operationId")
if not operation_id:
logger.warning(f"No operation id for {method} {path}")
continue
# 'id' is the id on the page (must match redoc anchor)
# 'name' is the name of the ref for use in our documents
target = nodes.target(
ids=[f"operation/{operation_id}"],
names=[f"rest-api-{operation_id}"],
)
targets.append(target)
self.state.document.note_explicit_target(target, target)
return targets
templates_path = ["_templates"]
def stage_redoc_js(app, exception):
"""Download redoc.js to our static files"""
if app.builder.name != "html":
logger.info(f"Skipping redoc download for builder: {app.builder.name}")
return
out_static = Path(app.builder.outdir) / "_static"
redoc_version = "2.1.3"
redoc_url = (
f"https://cdn.redoc.ly/redoc/v{redoc_version}/bundles/redoc.standalone.js"
)
dest = out_static / "redoc.js"
if not dest.exists():
logger.info(f"Downloading {redoc_url} -> {dest}")
urlretrieve(redoc_url, dest)
# stage fonts for redoc from google fonts
fonts_css_url = "https://fonts.googleapis.com/css?family=Montserrat:300,400,700|Roboto:300,400,700"
fonts_css_file = out_static / "redoc-fonts.css"
fonts_dir = out_static / "fonts"
fonts_dir.mkdir(exist_ok=True)
if not fonts_css_file.exists():
logger.info(f"Downloading {fonts_css_url} -> {fonts_css_file}")
urlretrieve(fonts_css_url, fonts_css_file)
# For each font external font URL,
# download the font and rewrite to a local URL
# The downloaded TTF fonts have license info in their metadata
with open(fonts_css_file) as f:
fonts_css = f.read()
fonts_css_changed = False
for font_url in re.findall(r'url\((https?[^\)]+)\)', fonts_css):
fonts_css_changed = True
filename = font_url.rpartition("/")[-1]
dest = fonts_dir / filename
local_url = str(dest.relative_to(fonts_css_file.parent))
fonts_css = fonts_css.replace(font_url, local_url)
if not dest.exists():
logger.info(f"Downloading {font_url} -> {dest}")
urlretrieve(font_url, dest)
if fonts_css_changed:
# rewrite font css with local URLs
with open(fonts_css_file, "w") as f:
logger.info(f"Rewriting URLs in {fonts_css_file}")
f.write(fonts_css)
def setup(app):
app.connect("build-finished", stage_redoc_js)
app.add_css_file("custom.css")
app.add_directive("jupyterhub-generate-config", ConfigDirective)
app.add_directive("jupyterhub-help-all", HelpAllDirective)
app.add_directive("jupyterhub-rest-api-links", RestAPILinksDirective)
app.add_css_file("https://docs.jupyter.org/en/latest/_static/jupyter.css")
# -- Read The Docs -----------------------------------------------------------
@@ -237,7 +121,8 @@ def setup(app):
# pre-requisite steps for "make html" from here if needed.
#
if os.environ.get("READTHEDOCS"):
subprocess.check_call(["make", "metrics", "scopes"], cwd=str(docs))
docs = os.path.dirname(os.path.dirname(__file__))
subprocess.check_call(["make", "metrics", "scopes"], cwd=docs)
# -- Spell checking ----------------------------------------------------------
@@ -261,15 +146,18 @@ html_logo = "_static/images/logo/logo.png"
html_favicon = "_static/images/logo/favicon.ico"
html_static_path = ["_static"]
html_theme = "jupyterhub_sphinx_theme"
html_theme = "pydata_sphinx_theme"
html_theme_options = {
"announcement": "🚀 Join us in San Diego · JupyterCon 2025 · Nov 4-5 · <a href=\"https://events.linuxfoundation.org/jupytercon/program/schedule/?ajs_aid=53afb00d-be65-4a99-9112-28cdaac99463\">SCHEDULE</a> · <a href=\"https://events.linuxfoundation.org/jupytercon/register/?ajs_aid=53afb00d-be65-4a99-9112-28cdaac99463\">REGISTER NOW</a>",
"header_links_before_dropdown": 6,
"icon_links": [
{
"name": "GitHub",
"url": "https://github.com/jupyterhub/jupyterhub",
"icon": "fa-brands fa-github",
"icon": "fab fa-github-square",
},
{
"name": "Discourse",
"url": "https://discourse.jupyter.org/c/jupyterhub/10",
"icon": "fab fa-discourse",
},
],
"use_edit_page_button": True,
@@ -289,40 +177,23 @@ html_context = {
linkcheck_ignore = [
r"(.*)github\.com(.*)#", # javascript based anchors
r"(.*)/#%21(.*)/(.*)", # /#!forum/jupyter - encoded anchor edge case
r"https?://(.*\.)?example\.(org|com)(/.*)?", # example links
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
"https://schema.jupyter.org/jupyterhub/.*", # schemas are not published yet
r"https?://(localhost|127.0.0.1).*", # ignore localhost references in auto-links
r"https://linux.die.net/.*", # linux.die.net seems to block requests from CI with 403 sometimes
# don't check links to unpublished advisories
r"https://github.com/jupyterhub/jupyterhub/security/advisories/.*",
# Occasionally blocks CI checks with 403
r"https://www\.mysql\.com",
r"https://www\.npmjs\.com",
# Occasionally blocks CI checks with SSL error
r"https://mediaspace\.msu\.edu/.*",
]
linkcheck_anchors_ignore = [
"/#!",
"/#%21",
]
# -- Intersphinx -------------------------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/extensions/intersphinx.html#configuration
#
intersphinx_mapping = get_intersphinx_mapping(
packages={
"python",
"tornado",
"jupyter-server",
"nbgitpuller",
}
)
intersphinx_mapping = {
"python": ("https://docs.python.org/3/", None),
"tornado": ("https://www.tornadoweb.org/en/stable/", None),
}
# -- Options for the opengraph extension -------------------------------------
# ref: https://github.com/wpilibsuite/sphinxext-opengraph#options
#
@@ -334,33 +205,11 @@ ogp_use_first_image = True
# -- Options for the rediraffe extension -------------------------------------
# ref: https://github.com/wpilibsuite/sphinxext-rediraffe#readme
#
# This extension helps us relocate content without breaking links. If a
# document is moved internally, a redirect link should be configured as below to
# 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.
#
# The workflow for adding redirects can be as follows:
# 1. Change "rediraffe_branch" below to point to the commit/ branch you
# want to base off the changes.
# 2. Option 1: run "make rediraffecheckdiff"
# a. Analyze the output of this command.
# b. Manually add the redirect entries to the "redirects.txt" file.
# Option 2: run "make rediraffewritediff"
# a. rediraffe will then automatically add the obvious redirects to redirects.txt.
# b. Analyze the output of the command for broken links.
# c. Check the "redirects.txt" file for any files that were moved/ renamed but are not listed.
# d. Manually add the redirects that have been mised by the automatic builder to "redirects.txt".
# Option 3: Do not use the commands above and, instead, do everything manually - by taking
# note of the files you have moved or renamed and adding them to the "redirects.txt" file.
#
# If you are basing changes off another branch/ commit, always change back
# rediraffe_branch to main before pushing your changes upstream.
#
rediraffe_branch = os.environ.get("REDIRAFFE_BRANCH", "main")
rediraffe_redirects = "redirects.txt"
# allow 80% match for autogenerated redirects
rediraffe_auto_redirect_perc = 80
# rediraffe_redirects = {
# "old-file": "new-folder/new-file-name",
# }
rediraffe_branch = "main"
rediraffe_redirects = {
# "old-file": "new-folder/new-file-name",
}

View File

@@ -1,42 +1,27 @@
(contributing:community)=
# Community communication channels
```{note}
Our community is distributed across the world in various timezones, so please be patient if you do not get a response immediately!
```
We use different channels of communication for different purposes. Whichever one you use will depend on what kind of communication you want to engage in.
## Discourse (recommended)
```{note}
[Discourse] is open source.
```
We use [Discourse](https://discourse.jupyter.org) for online discussions and support questions.
You can ask questions here if you are a first-time contributor to the JupyterHub project.
Everyone in the Jupyter community is welcome to bring ideas and questions there.
We use [Jupyter instance of Discourse] for online discussions and support questions.
You can ask questions at [Jupyter instance of Discourse] if you are a first-time contributor to the JupyterHub project.
Everyone is welcome to bring ideas and questions at [Jupyter instance of Discourse].
We recommend that you first use our Discourse as all past and current discussions on it are archived and searchable. Thus, all discussions remain useful and accessible to the whole community.
We recommend that you first use [Jupyter instance of Discourse] as all past and current discussions on it are archived and searchable. Thus, all discussions remain useful and accessible to the whole community.
## Gitter
## Zulip
```{note}
[Zulip] is open source.
```
We use [Jupyter instance of Zulip] for online, real-time text chat; a place for more ephemeral discussions. When you're not on [Jupyter instance of Discourse], you can stop at [Jupyter instance of Zulip] to have other discussions on the fly.
We use [our Gitter channel](https://gitter.im/jupyterhub/jupyterhub) for online, real-time text chat; a place for more ephemeral discussions. When you're not on Discourse, you can stop here to have other discussions on the fly.
## Github Issues
[Github issues](https://docs.github.com/en/issues/tracking-your-work-with-issues/about-issues) are used for most long-form project discussions, bug reports and feature requests.
- Issues related to a specific authenticator or spawner should be opened in the appropriate repository for the authenticator or spawner.
- If you are using a specific JupyterHub distribution (such as [Zero to JupyterHub on Kubernetes](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 are using a specific JupyterHub distribution (such as [Zero to JupyterHub on Kubernetes](http://github.com/jupyterhub/zero-to-jupyterhub-k8s) or [The Littlest JupyterHub](http://github.com/jupyterhub/the-littlest-jupyterhub/)), you should open issues directly in their repository.
- If you cannot find a repository to open your issue in, do not worry! Open the issue in the [main JupyterHub repository](https://github.com/jupyterhub/jupyterhub/) and our community will help you figure it out.
[Discourse]: https://www.discourse.org/
[Jupyter instance of Discourse]: https://discourse.jupyter.org
[Jupyter instance of Zulip]: https://jupyter.zulipchat.com/
[Zulip]: https://zulip.com/
```{note}
Our community is distributed across the world in various timezones, so please be patient if you do not get a response immediately!
```

View File

@@ -1,69 +0,0 @@
(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.
We use [Sphinx](https://www.sphinx-doc.org) to build our documentation. It takes
our documentation source files (written in [Markedly Structured Text (MyST)](https://mystmd.org/) and
stored under the `docs/source` directory) and converts it into various
formats for people to read.
## Building documentation locally
To make sure the documentation you write or
change renders correctly, it is good practice to test it locally.
```{note}
You will need Python and Git installed. Installation details are avaiable at {ref}`contributing:setup`.
```
1. Install the packages required to build the docs.
```bash
python3 -m pip install -r docs/requirements.txt
python3 -m pip install sphinx-autobuild
```
2. 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
sphinx-autobuild docs/source/ docs/_build/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 the HTML will be re-render automatically.
3. View the rendered documentation by opening <http://127.0.0.1:8000> in
a web browser.
(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/).

View File

@@ -0,0 +1,84 @@
.. _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 <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 correctly 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>``.
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:
.. 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/>`_.

View File

@@ -1,24 +0,0 @@
(contributing)=
# Contributing
We want you to contribute to JupyterHub in ways that are most exciting
and useful to you. We value documentation, testing, bug reporting and code equally,
and are glad to have your contributions in whatever form you wish.
Be sure to first check 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)), which help keep our community welcoming to as many people as possible.
This section covers information about our community, as well as ways that you can connect and get involved.
```{toctree}
:maxdepth: 2
contributor-list
community
setup
docs
tests
roadmap
security
```

View File

@@ -0,0 +1,21 @@
============
Contributing
============
We want you to contribute to JupyterHub in ways that are most exciting
& useful to you. We value documentation, testing, bug reporting & code equally,
and are glad to have your contributions in whatever form you wish :)
Our `Code of Conduct <https://github.com/jupyter/governance/blob/HEAD/conduct/code_of_conduct.md>`_
(`reporting guidelines <https://github.com/jupyter/governance/blob/HEAD/conduct/reporting_online.md>`_)
helps keep our community welcoming to as many people as possible.
.. toctree::
:maxdepth: 2
community
setup
docs
tests
roadmap
security

View File

@@ -1,5 +1,3 @@
(contributing:roadmap)=
# The JupyterHub roadmap
This roadmap collects "next steps" for JupyterHub. It is about creating a

View File

@@ -1,15 +0,0 @@
(contributing:security)=
# 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 [Security Overview](explanation:security)
or a failure in implementation,
please report it!
Please use GitHub's "Report a Vulnerability" button under Security > Advisories on the appropriate repo,
e.g. [report here for JupyterHub](https://github.com/jupyterhub/jupyterhub/security/advisories).
You may also send an email to <mailto:security@ipython.org>, but the GitHub reporting system is preferred.
If you prefer to encrypt your security reports,
you can use {download}`this PGP public key </ipython_security.asc>`.

View File

@@ -0,0 +1,10 @@
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>`.

View File

@@ -1,261 +0,0 @@
(contributing:setup)=
# Setting up a development install
JupyterHub's continuous integration runs on [Ubuntu LTS](https://ubuntu.com/).
While JupyterHub is only tested on one [Linux distribution](https://en.wikipedia.org/wiki/Linux_distribution),
it should be fairly insensitive to variations between common [POXIS](https://en.wikipedia.org/wiki/POSIX) implementation,
though we don't have the bandwidth to verify this automatically and continuously.
Feel free to try it on your platform, and be sure to {ref}`let us know <contributing:community>` about any issues you encounter.
## System requirements
Your system **must** be able to run
- Python
- NodeJS
- Git
Our small team knows JupyterHub to work perfectly on macOS or Linux operating systems.
```{admonition} What about Windows?
Some users have reported that JupyterHub runs successfully on [Windows Subsystem for Linux (WSL)](https://learn.microsoft.com/en-us/windows/wsl/). We have no plans to support Windows outside of the WSL.
```
```{admonition} What about virtualization?
Using any form of virtualization (for example, [VirtualBox](https://www.virtualbox.org/), [Docker](https://www.docker.com/), [Podman](https://podman.io/), [WSL](https://learn.microsoft.com/en-us/windows/wsl/)) is a good way to get up and running quickly, though properly configuring the networking settings can be a bit tricky.
```
### Install Python
JupyterHub is written in the [Python](https://www.python.org) programming language and
requires you have at least version {{python_min}} installed locally. If you havent
installed Python before, the recommended way to install it is to use
[Miniforge](https://github.com/conda-forge/miniforge#download).
### Install NodeJS
Some JavaScript components require you have at least version {{node_min}} of [NodeJS](https://nodejs.org/en/) installed locally.
`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) and [GitHub](https://github.com)
for development and 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.
## Install JupyterHub for development
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 {{python_min}}.
```bash
npm -v
```
This should return a version number greater than or equal to {{node_min}}.
3. Install `configurable-http-proxy` (required to run and test the default JupyterHub configuration):
```bash
npm install -g configurable-http-proxy
```
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
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
```
4. Install an editable version of JupyterHub and its requirements for
development and testing. This lets you edit JupyterHub code in a text editor
and restart the JupyterHub process to see your code changes immediately.
```bash
python3 -m pip install --editable ".[test]"
```
5. You are now ready to start JupyterHub!
```bash
jupyterhub
```
6. You can access JupyterHub from your browser at
`http://localhost:8000` now.
Happy developing!
## Using DummyAuthenticator and 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 test configuration enables a few things to make testing easier:
- use 'dummy' authentication and 'simple' spawner
- named servers are enabled
- listen only on localhost
- 'admin' is an admin user, if you want to test the admin page
- disable caching of static files
The default JupyterHub [authenticator](PAMAuthenticator)
and [spawner](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 and 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 and spawners, we recommend using both DummyAuthenticator and
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.
## Building frontend components
The testing configuration file also disables caching of static files,
which allows you to edit and rebuild these files without restarting JupyterHub.
If you are working on the admin react page, which is in the `jsx` directory, you can run:
```bash
cd jsx
npm install
npm run build:watch
```
to continuously rebuild the admin page, requiring only a refresh of the page.
If you are working on the frontend SCSS files, you can run the same `build:watch` command
in the _top level_ directory of the repo:
```bash
npm install
npm run build:watch
```
## 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
```
### Failed to bind XXX to `http://127.0.0.1:<port>/<path>`
This error can happen when there's already an application or a service using this
port.
Use the following command to find out which service is using this port.
```bash
lsof -P -i TCP:<port> -sTCP:LISTEN
```
If nothing shows up, it likely means there's a system service that uses it but
your current user cannot list it. Reuse the same command with sudo.
```bash
sudo lsof -P -i TCP:<port> -sTCP:LISTEN
```
Depending on the result of the above commands, the most simple solution is to
configure JupyterHub to use a different port for the service that is failing.
As an example, the following is a frequently seen issue:
`Failed to bind hub to http://127.0.0.1:8081/hub/`
Using the procedure described above, start with:
```bash
lsof -P -i TCP:8081 -sTCP:LISTEN
```
and if nothing shows up:
```bash
sudo lsof -P -i TCP:8081 -sTCP:LISTEN
```
Finally, depending on your findings, you can apply the following change and start JupyterHub again:
```python
c.JupyterHub.hub_port = 9081 # Or any other free port
```

View File

@@ -0,0 +1,187 @@
.. _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 havent
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``.
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.
.. 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``.
``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:
.. 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 an editable version of JupyterHub and its requirements for
development and testing. This lets you edit JupyterHub code in a text editor
& restart the JupyterHub process to see your code changes immediately.
.. code:: bash
python3 -m pip install --editable ".[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!
.. code:: 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:
.. 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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@@ -1,165 +0,0 @@
(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.
```{note}
Before run any test, 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 development environment is properly set up for tests to run.
```
```{note}
For details of `pytest`, refer to the [`pytest` usage documentation](https://pytest.readthedocs.io/en/latest/usage.html).
```
## Running all the tests
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
```
## Running tests from a specific file
You can also run tests in just a specific file:
```bash
pytest -v jupyterhub/tests/<test-file-name>
```
## Running a single test
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
```
## 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.
### The Pytest-Asyncio Plugin
When testing the various JupyterHub components and their various implementations, it sometimes becomes necessary to have a running instance of JupyterHub to test against.
The [`app`](https://github.com/jupyterhub/jupyterhub/blob/270b61992143b29af8c2fab90c4ed32f2f6fe209/jupyterhub/tests/conftest.py#L60) fixture mocks a JupyterHub application for use in testing by:
- enabling ssl if internal certificates are available
- creating an instance of [MockHub](https://github.com/jupyterhub/jupyterhub/blob/270b61992143b29af8c2fab90c4ed32f2f6fe209/jupyterhub/tests/mocking.py#L221) using any provided configurations as arguments
- initializing the mocked instance
- starting the mocked instance
- finally, a registered finalizer function performs a cleanup and stops the mocked instance
The JupyterHub test suite uses the [pytest-asyncio plugin](https://pytest-asyncio.readthedocs.io/en/latest/) that handles [event-loop](https://docs.python.org/3/library/asyncio-eventloop.html) integration in [Tornado](https://www.tornadoweb.org/en/stable/) applications. This allows for the use of top-level awaits when calling async functions or [fixtures](https://docs.pytest.org/en/6.2.x/fixture.html#what-fixtures-are) during testing. All test functions and fixtures labelled as `async` will run on the same event loop.
```{note}
With the introduction of [top-level awaits](https://piccolo-orm.com/blog/top-level-await-in-python/), the use of the `io_loop` fixture of the [pytest-tornado plugin](https://www.tornadoweb.org/en/stable/ioloop.html) is no longer necessary. It was initially used to call coroutines. With the upgrades made to `pytest-asyncio`, this usage is now deprecated. It is now, only utilized within the JupyterHub test suite to ensure complete cleanup of resources used during testing such as open file descriptors. This is demonstrated in this [pull request](https://github.com/jupyterhub/jupyterhub/pull/4332).
More information is provided below.
```
One of the general goals of the [JupyterHub Pytest Plugin project](https://github.com/jupyterhub/pytest-jupyterhub) is to ensure the MockHub cleanup fully closes and stops all utilized resources during testing so the use of the `io_loop` fixture for teardown is not necessary. This was highlighted in this [issue](https://github.com/jupyterhub/pytest-jupyterhub/issues/30)
For more information on asyncio and event-loops, here are some resources:
- **Read**: [Introduction to the Python event loop](https://www.pythontutorial.net/python-concurrency/python-event-loop)
- **Read**: [Overview of Async IO in Python 3.7](https://stackabuse.com/overview-of-async-io-in-python-3-7)
- **Watch**: [Asyncio: Understanding Async / Await in Python](https://www.youtube.com/watch?v=bs9tlDFWWdQ)
- **Watch**: [Learn Python's AsyncIO #2 - The Event Loop](https://www.youtube.com/watch?v=E7Yn5biBZ58)
## 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.

View File

@@ -0,0 +1,138 @@
.. _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
==================
#. 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.
#. 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 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:
#. ``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)
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.
.. code:: bash
pip install pre-commit
pre-commit install --install-hooks
To run pre-commit manually you would do:
.. code:: bash
# check for changes to code not yet committed
pre-commit run
# check for changes also in already committed code
pre-commit run --all-files
You may also install `black integration <https://github.com/psf/black#editor-integration>`_
into your text editor to format code automatically.

View File

@@ -1,5 +1,3 @@
(contributing:contributors)=
# Contributors
Project Jupyter thanks the following people for their help and

View File

@@ -0,0 +1,46 @@
Event logging and telemetry
===========================
JupyterHub can be configured to record structured events from a running server using Jupyter's `Telemetry System`_. The types of events that JupyterHub emits are defined by `JSON schemas`_ listed at the bottom of this page_.
.. _logging: https://docs.python.org/3/library/logging.html
.. _`Telemetry System`: https://github.com/jupyter/telemetry
.. _`JSON schemas`: https://json-schema.org/
How to emit events
------------------
Event logging is handled by its ``Eventlog`` object. This leverages Python's standing logging_ library to emit, filter, and collect event data.
To begin recording events, you'll need to set two configurations:
1. ``handlers``: tells the EventLog *where* to route your events. This trait is a list of Python logging handlers that route events to the event log file.
2. ``allows_schemas``: tells the EventLog *which* events should be recorded. No events are emitted by default; all recorded events must be listed here.
Here's a basic example:
.. code-block::
import logging
c.EventLog.handlers = [
logging.FileHandler('event.log'),
]
c.EventLog.allowed_schemas = [
'hub.jupyter.org/server-action'
]
The output is a file, ``"event.log"``, with events recorded as JSON data.
.. _page:
Event schemas
-------------
.. toctree::
:maxdepth: 2
server-actions.rst

View File

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

View File

@@ -1,430 +0,0 @@
(explanation:concepts)=
# JupyterHub: A conceptual overview
```{warning}
This page could be missing cross-links to other parts of
the documentation. You can help by adding them!
```
JupyterHub is not what you think it is. Most things you think are
part of JupyterHub are actually handled by some other component, for
example the spawner or notebook server itself, and it's not always
obvious how the parts relate. The knowledge contained here hasn't
been assembled in one place before, and is essential to understand
when setting up a sufficiently complex Jupyter(Hub) setup.
This document was originally written to assist in debugging: very
often, the actual problem is not where one thinks it is and thus
people can't easily debug. In order to tell this story, we start at
JupyterHub and go all the way down to the fundamental components of
Jupyter.
In this document, we occasionally leave things out or bend the truth
where it helps in explanation, and give our explanations in terms of
Python even though Jupyter itself is language-neutral. The "(&)"
symbol highlights important points where this page leaves out or bends
the truth for simplification of explanation, but there is more if you
dig deeper.
This guide is long, but after reading it you will be know of all major
components in the Jupyter ecosystem and everything else you read
should make sense.
## What is Jupyter?
Before we get too far, let's remember what our end goal is. A
**Jupyter Notebook** is nothing more than a Python(&) process
which is getting commands from a web browser and displaying the output
via that browser. What the process actually sees is roughly like
getting commands on standard input(&) and writing to standard
output(&). There is nothing intrinsically special about this process
- it can do anything a normal Python process can do, and nothing more.
The **Jupyter kernel** handles capturing output and converting things
such as graphics to a form usable by the browser.
Everything we explain below is building up to this, going through many
different layers which give you many ways of customizing how this
process runs.
## JupyterHub
**JupyterHub** is the central piece that provides multi-user
login capabilities. Despite this, the end user only briefly interacts with
JupyterHub and most of the actual Jupyter session does not relate to
the hub at all: the hub mainly handles authentication and creating (JupyterHub calls it "spawning") the
single-user server. In short, anything which is related to _starting_
the user's workspace/environment is about JupyterHub, anything about
_running_ usually isn't.
If you have problems connecting the authentication, spawning, and the
proxy (explained below), the issue is usually with JupyterHub. To
debug, JupyterHub has extensive logs which get printed to its console
and can be used to discover most problems.
The main pieces of JupyterHub are:
### Authenticator
JupyterHub itself doesn't actually manage your users. It has a
database of users, but it is usually connected with some other system
that manages the usernames and passwords. When someone tries to log
in to JupyteHub, it asks the
**authenticator**([basics](authenticators),
[reference](../reference/authenticators)) if the
username/password is valid(&). The authenticator returns a username(&),
which is passed on to the spawner, which has to use it to start that
user's environment. The authenticator can also return user
groups and admin status of users, so that JupyterHub can do some
higher-level management.
The following authenticators are included with JupyterHub:
- **PAMAuthenticator** uses the standard Unix/Linux operating system
functions to check users. Roughly, if someone already has access to
the machine (they can log in by ssh), they will be able to log in to
JupyterHub without any other setup. Thus, JupyterHub fills the role
of a ssh server, but providing a web-browser based way to access the
machine.
There are [plenty of others to choose from](authenticators-reference).
You can connect to almost any other existing service to manage your
users. You either use all users from this other service (e.g. your
company), or enable only the allowed users (e.g. your group's
Github usernames). Some other popular authenticators include:
- **OAuthenticator** uses the standard OAuth protocol to verify users.
For example, you can easily use Github to authenticate your users -
people have a "click to login with Github" button. This is often
done with a allowlist to only allow certain users.
- **NativeAuthenticator** actually stores and validates its own
usernames and passwords, unlike most other authenticators. Thus,
you can manage all your users within JupyterHub only.
- There are authenticators for LTI (learning management systems),
Shibboleth, Kerberos - and so on.
The authenticator is configured with the
`c.JupyterHub.authenticator_class` configuration option in the
`jupyterhub_config.py` file.
The authenticator runs internally to the Hub process but communicates
with outside services.
If you have trouble logging in, this is usually a problem of the
authenticator. The authenticator logs are part of the the JupyterHub
logs, but there may also be relevant information in whatever external
services you are using.
### Spawner
The **spawner** ([basics](spawners),
[reference](../reference/spawners)) is the real core of
JupyterHub: when someone wants a notebook server, the spawner allocates
resources and starts the server. The notebook server could run on the
same machine as JupyterHub, on another machine, on some cloud service,
or more. Administrators can limit resources (CPU, memory) or isolate users
from each other - if the spawner supports it. They can also do no
limiting and allow any user to access any other user's files if they
are not configured properly.
Some basic spawners included in JupyterHub are:
- **LocalProcessSpawner** is built into JupyterHub. Upon launch it tries
to switch users to the given username (`su` (&)) and start the
notebook server. It requires that the hub be run as root (because
only root has permission to start processes as other user IDs).
LocalProcessSpawner is no different than a user logging in with
something like `ssh` and running `jupyter notebook`. PAMAuthenticator and
LocalProcessSpawner is the most basic way of using JupyterHub (and
what it does out of the box) and makes the hub not too dissimilar to
an advanced ssh server.
There are [many more advanced spawners](/reference/spawners), and to
show the diversity of spawning strategys some are listed below:
- **SudoSpawner** is like LocalProcessSpawner but lets you run
JupyterHub without root. `sudo` has to be configured to allow the
hub's user to run processes under other user IDs.
- **SystemdSpawner** uses Systemd to start other processes. It can
isolate users from each other and provide resource limiting.
- **DockerSpawner** runs stuff in Docker, a containerization system.
This lets you fully isolate users, limit CPU, memory, and provide
other container images to fully customize the environment.
- **KubeSpawner** runs on the Kubernetes, a cloud orchestration
system. The spawner can easily limit users and provide cloud
scaling - but the spawner doesn't actually do that, Kubernetes
does. The spawner just tells Kubernetes what to do. If you want to
get KubeSpawner to do something, first you would figure out how to
do it in Kubernetes, then figure out how to tell KubeSpawner to tell
Kubernetes that. Actually... this is true for most spawners.
- **BatchSpawner** runs on computer clusters with batch job scheduling
systems (e.g Slurm, HTCondor, PBS, etc). The user processes are run
as batch jobs, having access to all the data and software that the
users normally will.
In short, spawners are the interface to the rest of the operating
system, and to configure them right you need to know a bit about how
the corresponding operating system service works.
The spawner is responsible for the environment of the single-user
notebook servers (described in the next section). In the end, it just
makes a choice about how to start these processes: for example, the
Docker spawner starts a normal Docker container and runs the right
command inside of it. Thus, the spawner is responsible for setting
what kind of software and data is available to the user.
The spawner runs internally to the Hub process but communicates with
outside services. It is configured by `c.JupyterHub.spawner_class` in
`jupyterhub_config.py`.
If a user tries to launch a notebook server and it doesn't work, the
error is usually with the spawner or the notebook server (as described
in the next section). Each spawner outputs some logs to the main
JupyterHub logs, but may also have logs in other places depending on
what services it interacts with (for example, the Docker spawner
somehow puts logs in the Docker system services, Kubernetes through
the `kubectl` API).
### Proxy
The JupyterHub **proxy** relays connections between the users
and their single-user notebook servers. What this basically means is
that the hub itself can shut down and the proxy can continue to
allow users to communicate with their notebook servers. (This
further emphasizes that the hub is responsible for starting, not
running, the notebooks). By default, the hub starts the proxy
automatically
and stops the proxy when the hub stops (so that connections get
interrupted). But when you [configure the proxy to run
separately](howto:separate-proxy),
user's connections will continue to work even without the hub.
The default proxy is **ConfigurableHttpProxy** which is simple but
effective. A more advanced option is the [**Traefik Proxy**](https://blog.jupyter.org/introducing-traefikproxy-a-new-jupyterhub-proxy-based-on-traefik-4839e972faf6),
which gives you redundancy and high-availability.
When users "connect to JupyterHub", they _always_ first connect to the
proxy and the proxy relays the connection to the hub. Thus, the proxy
is responsible for SSL and accepting connections from the rest of the
internet. The user uses the hub to authenticate and start the server,
and then the hub connects back to the proxy to adjust the proxy routes
for the user's server (e.g. the web path `/user/someone` redirects to
the server of someone at a certain internal address). The proxy has
to be able to internally connect to both the hub and all the
single-user servers.
The proxy always runs as a separate process to JupyterHub (even though
JupyterHub can start it for you). JupyterHub has one set of
configuration options for the proxy addresses (`bind_url`) and one for
the hub (`hub_bind_url`). If `bind_url` is given, it is just passed to
the automatic proxy to tell it what to do.
If you have problems after users are redirected to their single-user
notebook servers, or making the first connection to the hub, it is
usually caused by the proxy. The ConfigurableHttpProxy's logs are
mixed with JupyterHub's logs if it's started through the hub (the
default case), otherwise from whatever system runs the proxy (if you
do configure it, you'll know).
### Services
JupyterHub has the concept of **services** ([basics](tutorial:services),
[reference](services-reference)), which are other web services
started by the hub, but otherwise are not necessarily related to the
hub itself. They are often used to do things related to Jupyter
(things that user interacts with, usually not the hub), but could
always be run some other way. Running from the hub provides an easy
way to get Hub API tokens and authenticate users against the hub. It
can also automatically add a proxy route to forward web requests to
that service.
A common example of a service is the [cull idle
servers](https://github.com/jupyterhub/jupyterhub-idle-culler)
service. When started by the hub, it automatically gets admin API
tokens. It uses the API to list all running servers, compare against
activity timeouts, and shut down servers exceeding the limits. Even
though this is an intrinsic part of JupyterHub, it is only loosely
coupled and running as a service provides convenience of
authentication - it could be just as well run some other way, with a
manually provided API token.
The configuration option `c.JupyterHub.services` is used to start
services from the hub.
When a service is started from JupyterHub automatically, its logs are
included in the JupyterHub logs.
## Single-user notebook server
The **single-user notebook server** is the same thing you get by
running `jupyter notebook` or `jupyter lab` from the command line -
the actual Jupyter user interface for a single person.
The role of the spawner is to start this server - basically, running
the command `jupyter notebook`. Actually it doesn't run that, it runs
`jupyterhub-singleuser` which first communicates with the hub to say
"I'm alive" before running a completely normal Jupyter server. The
single-user server can be JupyterLab or classic notebooks. By this
point, the hub is almost completely out of the picture (the web
traffic is going through proxy unchanged). Also by this time, the
spawner has already decided the environment which this single-user
server will have and the single-user server has to deal with that.
The spawner starts the server using `jupyterhub-singleuser` with some
environment variables like `JUPYTERHUB_API_TOKEN` and
`JUPYTERHUB_BASE_URL` which tell the single-user server how to connect
back to the hub in order to say that it's ready.
The single-user server options are **JupyterLab** and **classic
Jupyter Notebook**. They both run through the same backend server process--the web
frontend is an option when it is starting. The spawner can choose the
command line when it starts the single-user server. Extensions are a
property of the single-user server (in two parts: there can be a part
that runs in the Python server process, and parts that run in
javascript in lab or notebook).
If one wants to install software for users, it is not a matter of
"installing it for JupyerHub" - it's a matter of installing it for the
single-user server, which might be the same environment as the hub,
but not necessarily. (see below - it's a matter of the kernels!)
After the single-user notebook server is started, any errors are only
an issue of the single-user notebook server. Sometimes, it seems like
the spawner is failing, but really the spawner is working but the
single-user notebook server dies right away (in this case, you need to
find the problem with the single-user server and adjust the spawner to
start it correctly or fix the environment). This can happen, for
example, if the spawner doesn't set an environment variable or doesn't
provide storage.
The single-user server's logs are printed to stdout/stderr, and the
spawer decides where those streams are directed, so if you
notice problems at this phase you need to check your spawner for
instructions for accessing the single-user logs. For example, the
LocalProcessSpawner logs are just outputted to the same JupyterHub
output logs, the SystemdSpawner logs are
written to the Systemd journal, Docker and Kubernetes logs are written
to Docker and Kubernetes respectively, and batchspawner output goes to
the normal output places of batch jobs and is an explicit
configuration option of the spawner.
**(Jupyter) Notebook** is the classic interface, where each notebook
opens in a separate tab. It is traditionally started by `jupyter
notebook`. Does anything need to be said here?
**JupyterLab** is the new interface, where multiple notebooks are
openable in the same tab in an IDE-like environment. It is
traditionally started with `jupyter lab`. Both Notebook and Lab use
the same `.ipynb` file format.
JupyterLab is run thorugh the same server file, but at a path `/lab`
instead of `/tree`. Thus, they can be active at the same time in the
backend and you can switch between them at runtime by changing your
URL path.
Extensions need to be re-written for JupyterLab (if moving from
classic notebooks). But, the server-side of the extensions can be
shared by both.
## Kernel
The commands you run in the notebook session are not executed in the same process as
the notebook itself, but in a separate **Jupyter kernel**. There are [many
kernels
available](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels).
As a basic approximation, a **Jupyter kernel** is a process which
accepts commands (cells that are run) and returns the output to
Jupyter to display. One example is the **IPython Jupyter kernel**,
which runs Python. There is nothing special about it, it can be
considered a \*normal Python process. The kernel process can be
approximated in UNIX terms as a process that takes commands on stdin
and returns stuff on stdout(&). Obviously, it's more because it has
to be able to disentangle all the possible outputs, such as figures,
and present it to the user in a web browser.
Kernel communication is via the the ZeroMQ protocol on the local
computer. Kernels are separate processes from the main single-user
notebook server (and thus obviously, different from the JupyterHub
process and everything else). By default (and unless you do something
special), kernels share the same environment as the notebook server
(data, resource limits, permissions, user id, etc.). But they _can_
run in a separate Python environment from the single-user server
(search `--prefix` in the [ipykernel installation
instructions](https://ipython.readthedocs.io/en/stable/install/kernel_install.html))
There are also more fancy techniques such as the [Jupyter Kernel
Gateway](https://jupyter-kernel-gateway.readthedocs.io/) and [Enterprise
Gateway](https://jupyter-enterprise-gateway.readthedocs.io/), which
allow you to run the kernels on a different machine and possibly with
a different environment.
A kernel doesn't just execute it's language - cell magics such as `%`,
`%%`, and `!` are a property of the kernel - in particular, these are
IPython kernel commands and don't necessarily work in any other
kernel unless they specifically support them.
Kernels are yet _another_ layer of configurability.
Each kernel can run a different programming language, with different
software, and so on. By default, they would run in the same
environment as the single-user notebook server, and the most common
other way they are configured is by
running in different Python virtual environments or conda
environments. They can be started and killed independently (there is
normally one per notebook you have open). The kernel uses
most of your memory and CPU when running Jupyter - the rest of the web
interface has a small footprint.
You can list your installed kernels with `jupyter kernelspec list`.
If you look at one of `kernel.json` files in those directories, you
will see exactly what command is run. These are normally
automatically made by the kernels, but can be edited as needed. [The
spec](https://jupyter-client.readthedocs.io/en/stable/kernels.html)
tells you even more.
The kernel normally has to be reachable by the single-user notebook server
but the gateways mentioned above can get around that limitation.
If you get problems with "Kernel died" or some other error in a single
notebook but the single-user notebook server stays working, it is
usually a problem with the kernel. It could be that you are trying to
use more resources than you are allowed and the symptom is the kernel
getting killed. It could be that it crashes for some other reason.
In these cases, you need to find the kernel logs and investigate.
The debug logs for the kernel are normally mixed in with the
single-user notebook server logs.
## JupyterHub distributions
There are several "distributions" which automatically install all of
the things above and configure them for a certain purpose. They are
good ways to get started, but if you have custom needs, eventually it
may become hard to adapt them to your requirements.
- [**Zero to JupyterHub with
Kubernetes**](https://zero-to-jupyterhub.readthedocs.io/) installs
an entire scaleable system using Kubernetes. Uses KubeSpawner,
....Authenticator, ....
- [**The Littlest JupyterHub**](https://tljh.jupyter.org/) installs JupyterHub on a single system
using SystemdSpawner and NativeAuthenticator (which manages users
itself).
- [**JupyterHub the hard way**](https://github.com/jupyterhub/jupyterhub-the-hard-way/blob/master/docs/installation-guide-hard.md)
takes you through everything yourself. It is a natural companion to
this guide, since you get to experience every little bit.
## What's next?
Now you know everything. Well, you know how everything relates, but
there are still plenty of details, implementations, and exceptions.
When setting up JupyterHub, the first step is to consider the above
layers, decide the right option for each of them, then begin putting
everything together.

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(explanation:hub-database)=
# The Hub's Database
JupyterHub uses a database to store information about users, services, and other data needed for operating the Hub.
This is the **state** of the Hub.
## Why does JupyterHub have a database?
JupyterHub is a **stateful** application (more on that 'state' later).
Updating JupyterHub's configuration or upgrading the version of JupyterHub requires restarting the JupyterHub process to apply the changes.
We want to minimize the disruption caused by restarting the Hub process, so it can be a mundane, frequent, routine activity.
Storing state information outside the process for later retrieval is necessary for this, and one of the main thing databases are for.
A lot of the operations in JupyterHub are also **relationships**, which is exactly what SQL databases are great at.
For example:
- Given an API token, what user is making the request?
- Which users don't have running servers?
- Which servers belong to user X?
- Which users have not been active in the last 24 hours?
Finally, a database allows us to have more information stored without needing it all loaded in memory,
e.g. supporting a large number (several thousands) of inactive users.
## What's in the database?
The short answer of what's in the JupyterHub database is "everything."
JupyterHub's **state** lives in the database.
That is, everything JupyterHub needs to be aware of to function that _doesn't_ come from the configuration files, such as
- users, roles, role assignments
- state, urls of running servers
- Hashed API tokens
- Short-lived state related to OAuth flow
- Timestamps for when users, tokens, and servers were last used
### What's _not_ in the database
Not _quite_ all of JupyterHub's state is in the database.
This mostly involves transient state, such as the 'pending' transitions of Spawners (starting, stopping, etc.).
Anything not in the database must be reconstructed on Hub restart, and the only sources of information to do that are the database and JupyterHub configuration file(s).
## How does JupyterHub use the database?
JupyterHub makes some _unusual_ choices in how it connects to the database.
These choices represent trade-offs favoring single-process simplicity and performance at the expense of horizontal scalability (multiple Hub instances).
We often say that the Hub 'owns' the database.
This ownership means that we assume the Hub is the only process that will talk to the database.
This assumption enables us to make several caching optimizations that dramatically improve JupyterHub's performance (i.e. data written recently to the database can be read from memory instead of fetched again from the database) that would not work if multiple processes could be interacting with the database at the same time.
Database operations are also synchronous, so while JupyterHub is waiting on a database operation, it cannot respond to other requests.
This allows us to avoid complex locking mechanisms, because transaction races can only occur during an `await`, so we only need to make sure we've completed any given transaction before the next `await` in a given request.
:::{note}
We are slowly working to remove these assumptions, and moving to a more traditional db session per-request pattern.
This will enable multiple Hub instances and enable scaling JupyterHub, but will significantly reduce the number of active users a single Hub instance can serve.
:::
### Database performance in a typical request
Most authenticated requests to JupyterHub involve a few database transactions:
1. look up the authenticated user (e.g. look up token by hash, then resolve owner and permissions)
2. record activity
3. perform any relevant changes involved in processing the request (e.g. create the records for a running server when starting one)
This means that the database is involved in almost every request, but only in quite small, simple queries, e.g.:
- lookup one token by hash
- lookup one user by name
- list tokens or servers for one user (typically 1-10)
- etc.
### The database as a limiting factor
As a result of the above transactions in most requests, database performance is the _leading_ factor in JupyterHub's baseline requests-per-second performance, but that cost does not scale significantly with the number of users, active or otherwise.
However, the database is _rarely_ a limiting factor in JupyterHub performance in a practical sense, because the main thing JupyterHub does is start, stop, and monitor whole servers, which take far more time than any small database transaction, no matter how many records you have or how slow your database is (within reason).
Additionally, there is usually _very_ little load on the database itself.
By far the most taxing activity on the database is the 'list all users' endpoint, primarily used by the [idle-culling service](https://github.com/jupyterhub/jupyterhub-idle-culler).
Database-based optimizations have been added to make even these operations feasible for large numbers of users:
1. State filtering on [GET /hub/api/users?state=active](rest-api-get-users),
which limits the number of results in the query to only the relevant subset (added in JupyterHub 1.3), rather than all users.
2. [Pagination](api-pagination) of all list endpoints, allowing the request of a large number of resources to be more fairly balanced with other Hub activities across multiple requests (added in 2.0).
:::{note}
It's important to note when discussing performance and limiting factors and that all of this only applies to requests to `/hub/...`.
The Hub and its database are not involved in most requests to single-user servers (`/user/...`), which is by design, and largely motivated by the fact that the Hub itself doesn't _need_ to be fast because its operations are infrequent and large.
:::
## Database backends
JupyterHub supports a variety of database backends via [SQLAlchemy][].
The default is sqlite, which works great for many cases, but you should be able to use many backends supported by SQLAlchemy.
Usually, this will mean PostgreSQL or MySQL, both of which are officially supported and well tested with JupyterHub, but others may work as well.
See [SQLAlchemy's docs][sqlalchemy-dialect] for how to connect to different database backends.
Doing so generally involves:
1. installing a Python package that provides a client implementation, and
2. setting [](JupyterHub.db_url) to connect to your database with the specified implementation
[sqlalchemy-dialect]: https://docs.sqlalchemy.org/en/20/dialects/
[sqlalchemy]: https://www.sqlalchemy.org
### Default backend: SQLite
The default database backend for JupyterHub is [SQLite](https://sqlite.org).
We have chosen SQLite as JupyterHub's default because it's simple (the 'database' is a single file), ubiquitous (it is in the Python standard library), and it does not require maintaining a separate database server.
The main disadvantage of SQLite is it does not support remote backup tools or replication.
You should backup your database by taking snapshots of the file (`jupyterhub.sqlite`).
SQLite is ideal for testing, small deployments, workshops, and production servers where you do not require remote backup or replication.
### Picking your database backend (PostgreSQL, MySQL)
The sqlite documentation provides a helpful page about [when to use SQLite and
where traditional RDBMS may be a better choice](https://sqlite.org/whentouse.html).
In general, you select your database backend with [](JupyterHub.db_url), and can further configure it (usually not necessary) with [](JupyterHub.db_kwargs).
## Notes and Tips
### Upgrading the JupyterHub database
[Upgrading JupyterHub to a new major release](howto:upgrading-jupyterhub) often requires an upgrade to the database schema.
- `jupyterhub upgrade-db` will execute a schema upgrade. You should backup your database before running this.
- `jupyterhub downgrade-db` may be able to revert a schema upgrade on PostgreSQL and MySQL, but this is not guaranteed to work, and is not supported.
### SQLite
The SQLite database should not be used on NFS. SQLite uses reader/writer locks
to control access to the database. This locking mechanism might not work
correctly if the database file is kept on an NFS filesystem. This is because
`fcntl()` file locking is broken on many NFS implementations. Therefore, you
should avoid putting SQLite database files on NFS since it will not handle well
multiple processes which might try to access the file at the same time.
### PostgreSQL
We recommend using PostgreSQL for production if you are unsure whether to use
MySQL or PostgreSQL or if you do not have a strong preference.
There is additional configuration required for MySQL that is not needed for PostgreSQL.
For example, to connect to a PostgreSQL database with psycopg2:
1. install psycopg2: `pip install psycopg2` (or `psycopg2-binary` to avoid compilation, which is [not recommended for production][psycopg2-binary])
2. set authentication via environment variables `PGUSER` and `PGPASSWORD`
3. configure [](JupyterHub.db_url):
```python
c.JupyterHub.db_url = "postgresql+psycopg2://my-postgres-server:5432/my-db-name"
```
[psycopg2-binary]: https://www.psycopg.org/docs/install.html#psycopg-vs-psycopg-binary
### MySQL / MariaDB
- You should probably use the `pymysql` or `mysqlclient` sqlalchemy provider, or another backend [recommended by sqlalchemy](https://docs.sqlalchemy.org/en/20/dialects/mysql.html#dialect-mysql)
- You also need to set `pool_recycle` to some value (typically 60 - 300, JupyterHub will default to 60)
which depends on your MySQL setup. This is necessary since MySQL kills
connections serverside if they've been idle for a while, and the connection
from the hub will be idle for longer than most connections. This behavior
will lead to frustrating 'the connection has gone away' errors from
sqlalchemy if `pool_recycle` is not set.
- If you use `utf8mb4` collation with MySQL earlier than 5.7.7 or MariaDB
earlier than 10.2.1 you may get an `1709, Index column size too large` error.
To fix this you need to set `innodb_large_prefix` to enabled and
`innodb_file_format` to `Barracuda` to allow for the index sizes jupyterhub
uses. `row_format` will be set to `DYNAMIC` as long as those options are set
correctly. Later versions of MariaDB and MySQL should set these values by
default, as well as have a default `DYNAMIC` `row_format` and pose no trouble
to users.
For example, to connect to a mysql database with mysqlclient:
1. install mysqlclient: `pip install mysqlclient`
2. configure [](JupyterHub.db_url):
```python
c.JupyterHub.db_url = "mysql+mysqldb://myuser:mypassword@my-sql-server:3306/my-db-name"
```

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@@ -1,17 +0,0 @@
(explanation)=
# Explanation
_Explanation_ documentation provide big-picture descriptions of how JupyterHub works. This section is meant to build your understanding of particular topics.
```{toctree}
:maxdepth: 1
concepts
capacity-planning
database
websecurity
oauth
singleuser
../rbac/index
```

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@@ -1,109 +0,0 @@
(explanation:singleuser)=
# The JupyterHub single-user server
When a user logs into JupyterHub, they get a 'server', which we usually call the **single-user server**, because it's a server that's meant for a single JupyterHub user.
Each JupyterHub user gets a different one (or more than one!).
A single-user server is a process running somewhere that is:
1. accessible over http[s],
2. authenticated via JupyterHub using OAuth 2.0,
3. started by a [Spawner](spawners), and
4. 'owned' by a single JupyterHub user
## The single-user server command
The Spawner's default single-user server startup command, `jupyterhub-singleuser`, launches `jupyter-server`, the same program used when you run `jupyter lab` on your laptop.
(_It can also launch the legacy `jupyter-notebook` server_).
That's why JupyterHub looks familiar to folks who are already using Jupyter at home or elsewhere.
It's the same!
`jupyterhub-singleuser` _customizes_ that program to change (approximately) one thing: **authenticate requests with JupyterHub**.
(singleuser-auth)=
## Single-user server authentication
Implementation-wise, JupyterHub single-user servers are a special-case of {ref}`services-reference`
and as such use the same (OAuth) authentication mechanism (more on OAuth in JupyterHub at [](oauth)).
This is primarily implemented in the {class}`~.HubOAuth` class.
This code resides in `jupyterhub.singleuser` subpackage of JupyterHub.
The main task of this code is to:
1. resolve a JupyterHub token to a JupyterHub user (authenticate)
2. check permissions (`access:servers`) for the token to make sure the request should be allowed (authorize)
3. if not authorized, begin the OAuth process with a redirect to the Hub
4. after login, store OAuth tokens in a cookie only used by this single-user server
5. implement logout to clear the cookie
Most of this is implemented in the {class}`~.HubOAuth` class. `jupyterhub.singleuser` is responsible for _adapting_ the base Jupyter Server to use HubOAuth for these tasks.
### JupyterHub authentication extension
By default, `jupyter-server` uses its own cookie to authenticate.
If that cookie is not present, the server redirects you a login page and asks you to enter a password or token.
Jupyter Server 2.0 introduces two new _APIs_ for customizing authentication: the [IdentityProvider](inv:jupyter-server#jupyter_server.auth.IdentityProvider) and the [Authorizer](inv:jupyter-server#jupyter_server.auth.Authorizer).
More information can be found in the [Jupyter Server documentation](https://jupyter-server.readthedocs.io).
JupyterHub implements these APIs in `jupyterhub.singleuser.extension`.
The IdentityProvider is responsible for _authenticating_ requests.
In JupyterHub, that means extracting OAuth tokens from the request and resolving them to a JupyterHub user.
The Authorizer is a _separate_ API for _authorizing_ actions on particular resources.
Because the JupyterHub IdentityProvider only allows _authenticating_ users who already have the necessary `access:servers` permission to access the server, the default Authorizer only contains a redundant check for this same permission, and ignores the resource inputs.
However, specifying a _custom_ Authorizer allows for granular permissions, such as read-only access to subsets of a shared server.
### JupyterHub authentication via subclass
Prior to Jupyter Server 2 (i.e. Jupyter Server 1.x or the legacy `jupyter-notebook` server), JupyterHub authentication is applied via _subclass_.
Originally a subclass of `NotebookApp`,
this approach works with both `jupyter-server` and `jupyter-notebook`.
Instead of using the extension mechanisms above,
the server application is _subclassed_. This worked well in the `jupyter-notebook` days,
but doesn't fit well with Jupyter Server's extension-based architecture.
### Selecting jupyterhub-singleuser implementation
Using the JupyterHub singleuser-server extension is the default behavior of JupyterHub 4 and Jupyter Server 2, otherwise the subclass approach is taken.
You can opt-out of the extension by setting the environment variable `JUPYTERHUB_SINGLEUSER_EXTENSION=0`:
```python
c.Spawner.environment.update(
{
"JUPYTERHUB_SINGLEUSER_EXTENSION": "0",
}
)
```
The subclass approach will also be taken if you've opted to use the classic notebook server with:
```
JUPYTERHUB_SINGLEUSER_APP=notebook
```
which was introduced in JupyterHub 2.
## Other customizations
`jupyterhub-singleuser` makes other small customizations to how the single-user server behaves:
1. logs activity on the single-user server, used in [idle-culling](https://github.com/jupyterhub/jupyterhub-idle-culler).
2. disables some features that don't make sense in JupyterHub (trash, retrying ports)
3. loading options such as URLs and SSL configuration from the environment
4. customize logging for consistency with JupyterHub logs
## Running a single-user server that's not `jupyterhub-singleuser`
By default, `jupyterhub-singleuser` is the same `jupyter-server` used by JupyterLab, Jupyter notebook (>= 7), etc.
But technically, all JupyterHub cares about is that it is:
1. an http server at the prescribed URL, accessible from the Hub and proxy, and
2. authenticated via [OAuth](oauth) with the Hub (it doesn't even have to do this, if you want to do your own authentication, as is done in BinderHub)
which means that you can customize JupyterHub to launch _any_ web application that meets these criteria, by following the specifications in {ref}`services-reference`.
Most of the time, though, it's easier to use [jupyter-server-proxy](https://jupyter-server-proxy.readthedocs.io) if you want to launch additional web applications in JupyterHub.

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(explanation:security)=
# Security Overview
The **Security Overview** section helps you learn about:
- the design of JupyterHub with respect to web security
- the semi-trusted user
- the available mitigations to protect untrusted users from each other
- the value of periodic security audits
This overview also helps you obtain a deeper understanding of how JupyterHub
works.
## Semi-trusted and untrusted users
JupyterHub is designed to be a _simple multi-user server for modestly sized
groups_ of **semi-trusted** users. While the design reflects serving
semi-trusted users, JupyterHub can also be suitable for serving **untrusted** users,
but **is not suitable for untrusted users** in its default configuration.
As a result, using JupyterHub with **untrusted** users means more work by the
administrator, since much care is required to secure a Hub, with extra caution on
protecting users from each other.
One aspect of JupyterHub's _design simplicity_ for **semi-trusted** users is that
the Hub and single-user servers are placed in a _single domain_, behind a
[_proxy_][configurable-http-proxy]. If the Hub is serving untrusted
users, many of the web's cross-site protections are not applied between
single-user servers and the Hub, or between single-user servers and each
other, since browsers see the whole thing (proxy, Hub, and single user
servers) as a single website (i.e. single domain).
## Protect users from each other
To protect users from each other, a user must **never** be able to write arbitrary
HTML and serve it to another user on the Hub's domain. This is prevented by JupyterHub's
authentication setup because only the owner of a given single-user notebook server is
allowed to view user-authored pages served by the given single-user notebook
server.
To protect all users from each other, JupyterHub administrators must
ensure that:
- A user **does not have permission** to modify their single-user notebook server,
including:
- the installation of new packages in the Python environment that runs
their single-user server;
- the creation of new files in any `PATH` directory that precedes the
directory containing `jupyterhub-singleuser` (if the `PATH` is used
to resolve the single-user executable instead of using an absolute path);
- the modification of environment variables (e.g. PATH, PYTHONPATH) for
their single-user server;
- the modification of the configuration of the notebook server
(the `~/.jupyter` or `JUPYTER_CONFIG_DIR` directory).
- unrestricted selection of the base environment (e.g. the image used in container-based Spawners)
If any additional services are run on the same domain as the Hub, the services
**must never** display user-authored HTML that is neither _sanitized_ nor _sandboxed_
to any user that lacks authentication as the author of a file.
### Sharing access to servers
Because sharing access to servers (via `access:servers` scopes or the sharing feature in JupyterHub 5) by definition means users can serve each other files, enabling sharing is not suitable for untrusted users without also enabling per-user domains.
JupyterHub does not enable any sharing by default.
## Mitigate security issues
The several approaches to mitigating security issues with configuration
options provided by JupyterHub include:
(subdomains)=
### Enable user subdomains
JupyterHub provides the ability to run single-user servers on their own
domains. This means the cross-origin protections between servers has the
desired effect, and user servers and the Hub are protected from each other.
**Subdomains are the only way to reliably isolate user servers from each other.**
To enable subdomains, set:
```python
c.JupyterHub.subdomain_host = "https://jupyter.example.org"
```
When subdomains are enabled, each user's single-user server will be at e.g. `https://username.jupyter.example.org`.
This also requires all user subdomains to point to the same address,
which is most easily accomplished with wildcard DNS, where a single A record points to your server and a wildcard CNAME record points to your A record:
```
A jupyter.example.org 192.168.1.123
CNAME *.jupyter.example.org jupyter.example.org
```
Since this spreads the service across multiple domains, you will likely need wildcard SSL as well,
matching `*.jupyter.example.org`.
Unfortunately, for many institutional domains, wildcard DNS and SSL may not be available.
We also **strongly encourage** serving JupyterHub and user content on a domain that is _not_ a subdomain of any sensitive content.
For reasoning, see [GitHub's discussion of moving user content to github.io from \*.github.com](https://github.blog/engineering/yummy-cookies-across-domains/).
**If you do plan to serve untrusted users, enabling subdomains is highly encouraged**,
as it resolves many security issues, which are difficult to unavoidable when JupyterHub is on a single-domain.
:::{important}
JupyterHub makes no guarantees about protecting users from each other unless subdomains are enabled.
If you want to protect users from each other, you **_must_** enable per-user domains.
:::
### Disable user config
If subdomains are unavailable or undesirable, JupyterHub provides a
configuration option `Spawner.disable_user_config = True`, which can be set to prevent
the user-owned configuration files from being loaded. After implementing this
option, `PATH`s and package installation are the other things that the
admin must enforce.
### Prevent spawners from evaluating shell configuration files
For most Spawners, `PATH` is not something users can influence, but it's important that
the Spawner should _not_ evaluate shell configuration files prior to launching the server.
### Isolate packages in a read-only environment
The user must not have permission to install packages into the environment where the singleuser-server runs.
On a shared system, package isolation is most easily handled by running the single-user server in
a root-owned virtualenv with disabled system-site-packages.
The user must not have permission to install packages into this environment.
The same principle extends to the images used by container-based deployments.
If users can select the images in which their servers run, they can disable all security for their own servers.
It is important to note that the control over the environment is only required for the
single-user server, and not the environment(s) in which the users' kernel(s)
may run. Installing additional packages in the kernel environment does not
pose additional risk to the web application's security.
### Encrypt internal connections with SSL/TLS
By default, all communications within JupyterHub—between the proxy, hub, and single
-user notebooks—are performed unencrypted. Setting the `internal_ssl` flag in
`jupyterhub_config.py` secures the aforementioned routes. Turning this
feature on does require that the enabled `Spawner` can use the certificates
generated by the `Hub` (the default `LocalProcessSpawner` can, for instance).
It is also important to note that this encryption **does not** cover the
`zmq tcp` sockets between the Notebook client and kernel yet. While users cannot
submit arbitrary commands to another user's kernel, they can bind to these
sockets and listen. When serving untrusted users, this eavesdropping can be
mitigated by setting `KernelManager.transport` to `ipc`. This applies standard
Unix permissions to the communication sockets thereby restricting
communication to the socket owner. The `internal_ssl` option will eventually
extend to securing the `tcp` sockets as well.
### Mitigating same-origin deployments
While per-user domains are **required** for robust protection of users from each other,
you can mitigate many (but not all) cross-user issues.
First, it is critical that users cannot modify their server environments, as described above.
Second, it is important that users do not have `access:servers` permission to any server other than their own.
If users can access each others' servers, additional security measures must be enabled, some of which come with distinct user-experience costs.
Without the [Same-Origin Policy] (SOP) protecting user servers from each other,
each user server is considered a trusted origin for requests to each other user server (and the Hub itself).
Servers _cannot_ meaningfully distinguish requests originating from other user servers,
because SOP implies a great deal of trust, losing many restrictions applied to cross-origin requests.
That means pages served from each user server can:
1. arbitrarily modify the path in the Referer
2. make fully authorized requests with cookies
3. access full page contents served from the hub or other servers via popups
JupyterHub uses distinct xsrf tokens stored in cookies on each server path to attempt to limit requests across.
This has limitations because not all requests are protected by these XSRF tokens,
and unless additional measures are taken, the XSRF tokens from other user prefixes may be retrieved.
[Same-Origin Policy]: https://developer.mozilla.org/en-US/docs/Web/Security/Same-origin_policy
For example:
- `Content-Security-Policy` header must prohibit popups and iframes from the same origin.
The following Content-Security-Policy rules are _insecure_ and readily enable users to access each others' servers:
- `frame-ancestors: 'self'`
- `frame-ancestors: '*'`
- `sandbox allow-popups`
- Ideally, pages should use the strictest `Content-Security-Policy: sandbox` available,
but this is not feasible in general for JupyterLab pages, which need at least `sandbox allow-same-origin allow-scripts` to work.
The default Content-Security-Policy for single-user servers is
```
frame-ancestors: 'none'
```
which prohibits iframe embedding, but not pop-ups.
A more secure Content-Security-Policy that has some costs to user experience is:
```
frame-ancestors: 'none'; sandbox allow-same-origin allow-scripts
```
`allow-popups` is not disabled by default because disabling it breaks legitimate functionality, like "Open this in a new tab", and the "JupyterHub Control Panel" menu item.
To reiterate, the right way to avoid these issues is to enable per-user domains, where none of these concerns come up.
Note: even this level of protection requires administrators maintaining full control over the user server environment.
If users can modify their server environment, these methods are ineffective, as users can readily disable them.
### Cookie tossing
Cookie tossing is a technique where another server on a subdomain or peer subdomain can set a cookie
which will be read on another domain.
This is not relevant unless there are other user-controlled servers on a peer domain.
"Domain-locked" cookies avoid this issue, but have their own restrictions:
- JupyterHub must be served over HTTPS
- All secure cookies must be set on `/`, not on sub-paths, which means they are shared by all JupyterHub components in a single-domain deployment.
As a result, this option is only recommended when per-user subdomains are enabled,
to prevent sending all jupyterhub cookies to all user servers.
To enable domain-locked cookies, set:
```python
c.JupyterHub.cookie_host_prefix_enabled = True
```
```{versionadded} 4.1
```
### Forced-login
Jupyter servers can share links with `?token=...`.
JupyterHub prior to 5.0 will accept this request and persist the token for future requests.
This is useful for enabling admins to create 'fully authenticated' links bypassing login.
However, it also means users can share their own links that will log other users into their own servers,
enabling them to serve each other notebooks and other arbitrary HTML, depending on server configuration.
```{versionadded} 4.1
Setting environment variable `JUPYTERHUB_ALLOW_TOKEN_IN_URL=0` in the single-user environment can opt out of accepting token auth in URL parameters.
```
```{versionadded} 5.0
Accepting tokens in URLs is disabled by default, and `JUPYTERHUB_ALLOW_TOKEN_IN_URL=1` environment variable must be set to _allow_ token auth in URL parameters.
```
## Security audits
We recommend that you do periodic reviews of your deployment's security. It's
good practice to keep [JupyterHub](https://readthedocs.org/projects/jupyterhub/), [configurable-http-proxy][], and [nodejs
versions](https://github.com/nodejs/Release) up to date.
A handy website for testing your deployment is
[Qualsys' SSL analyzer tool](https://www.ssllabs.com/ssltest/analyze.html).
[configurable-http-proxy]: https://github.com/jupyterhub/configurable-http-proxy
## Vulnerability reporting
If you believe you have found a security vulnerability in JupyterHub, or any
Jupyter project, please report it to
[security@ipython.org](mailto:security@ipython.org). If you prefer to encrypt
your security reports, you can use [this PGP public
key](https://jupyter.org/assets/ipython_security.asc).

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@@ -1,78 +0,0 @@
(faq)=
# Frequently asked questions
## How do I share links to notebooks?
Sharing links to notebooks is a common activity,
and can look different depending on what you mean by 'share.'
Your first instinct might be to copy the URL you see in the browser,
e.g. `jupyterhub.example/user/yourname/notebooks/coolthing.ipynb`,
but this usually won't work, depending on the permissions of the person you share the link with.
Unfortunately, 'share' means at least a few things to people in a JupyterHub context.
We'll cover 3 common cases here, when they are applicable, and what assumptions they make:
1. sharing links that will open the same file on the visitor's own server
2. sharing links that will bring the visitor to _your_ server (e.g. for real-time collaboration, or RTC)
3. publishing notebooks and sharing links that will download the notebook into the user's server
### link to the same file on the visitor's server
This is for the case where you have JupyterHub on a shared (or sufficiently similar) filesystem, where you want to share a link that will cause users to login and start their _own_ server, to view or edit the file.
**Assumption:** the same path on someone else's server is valid and points to the same file
This is useful in e.g. classes where you know students have certain files in certain locations, or collaborations where you know you have a shared filesystem where everyone has access to the same files.
A link should look like `https://jupyterhub.example/hub/user-redirect/lab/tree/foo.ipynb`.
You can hand-craft these URLs from the URL you are looking at, where you see `/user/name/lab/tree/foo.ipynb` use `/hub/user-redirect/lab/tree/foo.ipynb` (replace `/user/name/` with `/hub/user-redirect/`).
Or you can use JupyterLab's "copy shareable link" in the context menu in the file browser:
![copy shareable link in JupyterLab](../images/shareable_link.webp)
which will produce a correct URL with `/hub/user-redirect/` in it.
### link to the file on your server
This is for the case where you want to both be using _your_ server, e.g. for real-time collaboration (RTC).
**Assumption:** the user has (or should have) access to your server.
**Assumption:** your server is running _or_ the user has permission to start it.
By default, JupyterHub users don't have access to each other's servers, but JupyterHub 2.0 administrators can grant users limited access permissions to each other's servers.
If the visitor doesn't have access to the server, these links will result in a 403 Permission Denied error.
In many cases, for this situation you can copy the link in your URL bar (`/user/yourname/lab`), or you can add `/tree/path/to/specific/notebook.ipynb` to open a specific file.
The [jupyterlab-link-share] JupyterLab extension generates these links, and even can _grant_ other users access to your server.
[jupyterlab-link-share]: https://github.com/jupyterlab-contrib/jupyterlab-link-share
:::{warning}
Note that the way the extension _grants_ access is handing over credentials to allow the other user to **_BECOME YOU_**.
This is usually not appropriate in JupyterHub.
:::
### link to a published copy
Another way to 'share' notebooks is to publish copies, e.g. pushing the notebook to a git repository and sharing a download link.
This way is especially useful for course materials,
where no assumptions are necessary about the user's environment,
except for having one package installed.
**Assumption:** The [nbgitpuller](inv:nbgitpuller#index) server extension is installed
Unlike the other two methods, nbgitpuller doesn't provide an extension to copy a shareable link for the document you're currently looking at,
but it does provide a [link generator](inv:nbgitpuller#link),
which uses the `user-redirect` approach above.
When visiting an nbgitpuller link:
- The visitor will be directed to their own server
- Your repo will be cloned (or updated if it's already been cloned)
- and then the file opened when it's ready
[nbgitpuller]: https://nbgitpuller.readthedocs.io
[nbgitpuller-link]: https://nbgitpuller.readthedocs.io/en/latest/link.html

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@@ -1,11 +0,0 @@
# FAQs
Find answers to some of the most frequently-asked questions around JupyterHub and how it works.
```{toctree}
:maxdepth: 2
faq
institutional-faq
troubleshooting
```

View File

@@ -1,5 +1,3 @@
(gallery-of-deployments)=
# A Gallery of JupyterHub Deployments
**A JupyterHub Community Resource**
@@ -16,13 +14,19 @@ Please submit pull requests to update information or to add new institutions or
- [BIDS - Berkeley Institute for Data Science](https://bids.berkeley.edu/)
- [Data 8](https://www.data8.org/)
- [Teaching with Jupyter notebooks and JupyterHub](https://bids.berkeley.edu/resources/videos/teaching-ipythonjupyter-notebooks-and-jupyterhub)
- [Data 8](http://data8.org/)
- [GitHub organization](https://github.com/data-8)
- [NERSC](https://www.nersc.gov/)
- [NERSC](http://www.nersc.gov/)
- [Research IT](https://research-it.berkeley.edu)
- [JupyterHub server supports campus research computation](https://research-it.berkeley.edu/news/free-fully-loaded-jupyterhub-server-supports-campus-research-computation)
- [Press release on Jupyter and Cori](http://www.nersc.gov/news-publications/nersc-news/nersc-center-news/2016/jupyter-notebooks-will-open-up-new-possibilities-on-nerscs-cori-supercomputer/)
- [Moving and sharing data](https://www.nersc.gov/assets/Uploads/03-MovingAndSharingData-Cholia.pdf)
- [Research IT](http://research-it.berkeley.edu)
- [JupyterHub server supports campus research computation](http://research-it.berkeley.edu/blog/17/01/24/free-fully-loaded-jupyterhub-server-supports-campus-research-computation)
### University of California Davis
@@ -57,15 +61,6 @@ easy to do with RStudio too.
- [jupyterhub-deploy-teaching](https://github.com/jupyterhub/jupyterhub-deploy-teaching) based on work by Brian Granger for Cal Poly's Data Science 301 Course
### CERN
[CERN](https://home.cern/), also known as the European Organization for Nuclear Research, is a world-renowned scientific research centre and the home of the Large Hadron Collider (LHC).
Within CERN, there are two noteworthy JupyterHub deployments in operation:
- [SWAN](https://swan.web.cern.ch/swan/), which stands for Service for Web based Analysis, serves as an interactive data analysis platform primarily utilized at CERN.
- [VRE](https://vre-hub.github.io/), which stands for Virtual Research Environment, is an analysis platform developed within the [EOSC Project](https://eoscfuture.eu/) to cater to the needs of scientific communities involved in European projects.
### Chameleon
[Chameleon](https://www.chameleoncloud.org) is a NSF-funded configurable experimental environment for large-scale computer science systems research with [bare metal reconfigurability](https://chameleoncloud.readthedocs.io/en/latest/technical/baremetal.html). Chameleon users utilize JupyterHub to document and reproduce their complex CISE and networking experiments.
@@ -76,19 +71,25 @@ Within CERN, there are two noteworthy JupyterHub deployments in operation:
### Clemson University
- Advanced Computing
- [Palmetto cluster and JupyterHub](https://citi.sites.clemson.edu/2016/08/18/JupyterHub-for-Palmetto-Cluster.html)
- [Palmetto cluster and JupyterHub](http://citi.sites.clemson.edu/2016/08/18/JupyterHub-for-Palmetto-Cluster.html)
### ETH Zurich
### University of Colorado Boulder
[ETH Zurich](https://ethz.ch/en.html), (Federal Institute of Technology Zurich), is a public research university in Zürich, Switzerland, with focus on science, technology, engineering, and mathematics, although its 16 departments span a variety of disciplines and subjects.
- (CU Research Computing) CURC
The [Educational Development and Technology](https://ethz.ch/en/the-eth-zurich/organisation/departments/teaching-and-learning.html) unit provides JupyterHub exclusively for teaching and learning, integrated in the learning management system [Moodle](https://ethz.ch/staffnet/en/teaching/academic-support/it-services-teaching/teaching-applications/moodle-service.html). Each course gets its individually configured JupyterHub environment deployed on a on-premise Kubernetes cluster.
- [JupyterHub User Guide](https://www.rc.colorado.edu/support/user-guide/jupyterhub.html)
- Slurm job dispatched on Crestone compute cluster
- log troubleshooting
- Profiles in IPython Clusters tab
- [Parallel Processing with JupyterHub tutorial](https://www.rc.colorado.edu/support/examples-and-tutorials/parallel-processing-with-jupyterhub.html)
- [Parallel Programming with JupyterHub document](https://www.rc.colorado.edu/book/export/html/833)
- [ETH JupyterHub](https://ethz.ch/staffnet/en/teaching/academic-support/it-services-teaching/teaching-applications/jupyterhub.html) for teaching and learning
- Earth Lab at CU
- [Tutorial on Parallel R on JupyterHub](https://earthdatascience.org/tutorials/parallel-r-on-jupyterhub/)
### George Washington University
- [JupyterHub](https://go.gwu.edu/jupyter) with university single-sign-on. Deployed early 2017.
- [Jupyter Hub](http://go.gwu.edu/jupyter) with university single-sign-on. Deployed early 2017.
### HTCondor
@@ -100,7 +101,7 @@ The [Educational Development and Technology](https://ethz.ch/en/the-eth-zurich/o
### IllustrisTNG Simulation Project
- [JupyterHub/Lab-based analysis platform, part of the TNG public data release](https://www.tng-project.org/data/)
- [JupyterHub/Lab-based analysis platform, part of the TNG public data release](http://www.tng-project.org/data/)
### MIT and Lincoln Labs
@@ -120,16 +121,21 @@ The [Educational Development and Technology](https://ethz.ch/en/the-eth-zurich/o
### Paderborn University
- [Data Science (DICE) group](https://dice-research.org)
- [JavaOnlineExercises](https://github.com/dice-group/JavaOnlineExercises): Use JupyterHub + nbgrader + iJava kernel for online Java exercises. Used in lecture Statistical Natural Language Processing.
- [Data Science (DICE) group](https://dice.cs.uni-paderborn.de/)
- [nbgraderutils](https://github.com/dice-group/nbgraderutils): Use JupyterHub + nbgrader + iJava kernel for online Java exercises. Used in lecture Statistical Natural Language Processing.
### Penn State University
- [Press release](https://www.psu.edu/news/academics/story/new-open-source-web-apps-available-students-and-faculty): "New open-source web apps available for students and faculty"
- [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
- [Deploy JupyterHub on a Supercomputer with SSH](https://zonca.github.io/2017/05/jupyterhub-hpc-batchspawner-ssh.html)
- [Run Jupyterhub on a Supercomputer](https://zonca.github.io/2015/04/jupyterhub-hpc.html)
- [Deploy JupyterHub on a VM for a Workshop](https://zonca.github.io/2016/04/jupyterhub-sdsc-cloud.html)
@@ -138,7 +144,7 @@ The [Educational Development and Technology](https://ethz.ch/en/the-eth-zurich/o
- [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
- [datahub.ucsd.edu](https://datahub.ucsd.edu)
- [jupyterhub.ucsd.edu](https://jupyterhub.ucsd.edu)
### TACC University of Texas
@@ -149,7 +155,7 @@ The [Educational Development and Technology](https://ethz.ch/en/the-eth-zurich/o
### Elucidata
- What's new in Jupyter Notebooks @[Elucidata](https://www.elucidata.io/):
- What's new in Jupyter Notebooks @[Elucidata](https://elucidata.io/):
- [Using Jupyter Notebooks with Jupyterhub on GCP, managed by GKE](https://medium.com/elucidata/why-you-should-be-using-a-jupyter-notebook-8385a4ccd93d)
## Service Providers
@@ -169,33 +175,28 @@ The [Educational Development and Technology](https://ethz.ch/en/the-eth-zurich/o
### Microsoft Azure
- [Azure Data Science Virtual Machine release notes](https://learn.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-linux-dsvm-intro)
- [Azure Data Science Virtual Machine release notes](https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-linux-dsvm-intro)
### Rackspace Carina
- https://getcarina.com/blog/learning-how-to-whale/
- https://carolynvanslyck.com/talk/carina/jupyterhub/#/ (but carolynvanslyck is currently down; checked 10/26/22)
- http://carolynvanslyck.com/talk/carina/jupyterhub/#/ (but carolynvanslyck is currently down; checked 10/26/22)
### Hadoop
- [Deploying JupyterHub on Hadoop](https://jupyterhub-on-hadoop.readthedocs.io)
### Sirepo
- Sirepo is an online Computer-Aided Engineering gateway that contains a JupyterHub instance. Sirepo is provided at no cost for community use, but users must request login access.
- [Sirepo.com](https://www.sirepo.com)
- [Sirepo Jupyter](https://www.sirepo.com/jupyter)
## Miscellaneous
- https://medium.com/@ybarraud/setting-up-jupyterhub-with-sudospawner-and-anaconda-844628c0dbee#.rm3yt87e1
- [Mailing list UT deployment](https://groups.google.com/g/jupyter/c/nkPSEeMr8c0)
- [Mailing list UT deployment](https://groups.google.com/forum/#!topic/jupyter/nkPSEeMr8c0)
- [JupyterHub setup on Centos](https://gist.github.com/johnrc/604971f7d41ebf12370bf5729bf3e0a4)
- [Deploy JupyterHub to Docker Swarm](https://jupyterhub.surge.sh)
- https://www.laketide.com/building-your-lab-part-3/
- https://estrellita.hatenablog.com/entry/2015/07/31/083202
- https://www.walkingrandomly.com/?p=5734
- [Deploy JupyterHub to Docker Swarm](https://jupyterhub.surge.sh/#/welcome)
- http://www.laketide.com/building-your-lab-part-3/
- http://estrellita.hatenablog.com/entry/2015/07/31/083202
- http://www.walkingrandomly.com/?p=5734
- https://wrdrd.com/docs/consulting/education-technology
- https://bitbucket.org/jackhale/fenics-jupyter
- [LinuxCluster blog](https://thelinuxcluster.com/category/application/jupyterhub/)
- [LinuxCluster blog](https://linuxcluster.wordpress.com/category/application/jupyterhub/)
- [Network Technology](https://arnesund.com/tag/jupyterhub/)
- [Spark Cluster on OpenStack with Multi-User Jupyter Notebook](https://arnesund.com/2015/09/21/spark-cluster-on-openstack-with-multi-user-jupyter-notebook/)

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@@ -0,0 +1,131 @@
# Authentication and User Basics
The default Authenticator uses [PAM][] (Pluggable Authentication Module) to authenticate system users with
their username and password. With the default Authenticator, any user
with an account and password on the system will be allowed to login.
## Create a set of allowed users (`allowed_users`)
You can restrict which users are allowed to login with a set,
`Authenticator.allowed_users`:
```python
c.Authenticator.allowed_users = {'mal', 'zoe', 'inara', 'kaylee'}
```
Users in the `allowed_users` set are added to the Hub database when the Hub is
started.
```{warning}
If this configuration value is not set, then **all authenticated users will be allowed into your hub**.
```
## Configure admins (`admin_users`)
```{note}
As of JupyterHub 2.0, the full permissions of `admin_users`
should not be required.
Instead, you can assign [roles](define-role-target) to users or groups
with only the scopes they require.
```
Admin users of JupyterHub, `admin_users`, can add and remove users from
the user `allowed_users` set. `admin_users` can take actions on other users'
behalf, such as stopping and restarting their servers.
A set of initial admin users, `admin_users` can be configured as follows:
```python
c.Authenticator.admin_users = {'mal', 'zoe'}
```
Users in the admin set are automatically added to the user `allowed_users` set,
if they are not already present.
Each Authenticator may have different ways of determining whether a user is an
administrator. By default, JupyterHub uses the PAMAuthenticator which provides the
`admin_groups` option and can set administrator status based on a user
group. For example, we can let any user in the `wheel` group be an admin:
```python
c.PAMAuthenticator.admin_groups = {'wheel'}
```
## Give admin access to other users' notebook servers (`admin_access`)
Since the default `JupyterHub.admin_access` setting is `False`, the admins
do not have permission to log in to the single user notebook servers
owned by _other users_. If `JupyterHub.admin_access` is set to `True`,
then admins have permission to log in _as other users_ on their
respective machines for debugging. **As a courtesy, you should make
sure your users know if admin_access is enabled.**
## Add or remove users from the Hub
Users can be added to and removed from the Hub via the admin
panel or the REST API. When a user is **added**, the user will be
automatically added to the `allowed_users` set and database. Restarting the Hub
will not require manually updating the `allowed_users` set in your config file,
as the users will be loaded from the database.
After starting the Hub once, it is not sufficient to **remove** a user
from the allowed users set in your config file. You must also remove the user
from the Hub's database, either by deleting the user from JupyterHub's
admin page, or you can clear the `jupyterhub.sqlite` database and start
fresh.
## Use LocalAuthenticator to create system users
The `LocalAuthenticator` is a special kind of Authenticator that has
the ability to manage users on the local system. When you try to add a
new user to the Hub, a `LocalAuthenticator` will check if the user
already exists. If you set the configuration value, `create_system_users`,
to `True` in the configuration file, the `LocalAuthenticator` has
the ability to add users to the system. The setting in the config
file is:
```python
c.LocalAuthenticator.create_system_users = True
```
Adding a user to the Hub that doesn't already exist on the system will
result in the Hub creating that user via the system `adduser` command
line tool. This option is typically used on hosted deployments of
JupyterHub to avoid the need to manually create all your users before
launching the service. This approach is not recommended when running
JupyterHub in situations where JupyterHub users map directly onto the
system's UNIX users.
## Use OAuthenticator to support OAuth with popular service providers
JupyterHub's [OAuthenticator][] currently supports the following
popular services:
- [Auth0](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.auth0.html#module-oauthenticator.auth0)
- [Azure AD](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.azuread.html#module-oauthenticator.azuread)
- [Bitbucket](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.bitbucket.html#module-oauthenticator.bitbucket)
- [CILogon](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.cilogon.html#module-oauthenticator.cilogon)
- [GitHub](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.github.html#module-oauthenticator.github)
- [GitLab](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.gitlab.html#module-oauthenticator.gitlab)
- [Globus](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.globus.html#module-oauthenticator.globus)
- [Google](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.google.html#module-oauthenticator.google)
- [MediaWiki](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.mediawiki.html#module-oauthenticator.mediawiki)
- [Okpy](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.okpy.html#module-oauthenticator.okpy)
- [OpenShift](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.openshift.html#module-oauthenticator.openshift)
A [generic implementation](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.generic.html#module-oauthenticator.generic), which you can use for OAuth authentication
with any provider, is also available.
## Use DummyAuthenticator for testing
The `DummyAuthenticator` is a simple Authenticator that
allows for any username or password unless a global password has been set. If
set, it will allow for any username as long as the correct password is provided.
To set a global password, add this to the config file:
```python
c.DummyAuthenticator.password = "some_password"
```
[pam]: https://en.wikipedia.org/wiki/Pluggable_authentication_module
[oauthenticator]: https://github.com/jupyterhub/oauthenticator

View File

@@ -1,7 +1,7 @@
# Configuration Basics
This section contains basic information about configuring settings for a JupyterHub
deployment. The [Technical Reference](reference-index)
deployment. The [Technical Reference](../reference/index)
documentation provides additional details.
This section will help you learn how to:
@@ -11,8 +11,6 @@ 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,
@@ -46,7 +44,7 @@ jupyterhub -f /etc/jupyterhub/jupyterhub_config.py
```
The IPython documentation provides additional information on the
[config system](https://ipython.readthedocs.io/en/stable/development/config.html)
[config system](http://ipython.readthedocs.io/en/stable/development/config.html)
that Jupyter uses.
## Configure using command line options
@@ -82,7 +80,7 @@ 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.
This enables JupyterHub to be used with a variety of authentication methods or
process control and deployment environments. [Some examples](config-examples),
process control and deployment environments. [Some examples](../reference/config-examples),
meant as illustrations, are:
- Using GitHub OAuth instead of PAM with [OAuthenticator](https://github.com/jupyterhub/oauthenticator)
@@ -99,4 +97,4 @@ maintenance, re-configuration, etc.), then user connections are not
interrupted. For simplicity, by default the hub starts the proxy
automatically, so if the hub restarts, the proxy restarts, and user
connections are interrupted. It is easy to run the proxy separately,
for information see [the separate proxy page](howto:separate-proxy).
for information see [the separate proxy page](../reference/separate-proxy).

View File

@@ -0,0 +1,36 @@
# Frequently asked questions
## How do I share links to notebooks?
In short, where you see `/user/name/notebooks/foo.ipynb` use `/hub/user-redirect/notebooks/foo.ipynb` (replace `/user/name` with `/hub/user-redirect`).
Sharing links to notebooks is a common activity,
and can look different based on what you mean.
Your first instinct might be to copy the URL you see in the browser,
e.g. `hub.jupyter.org/user/yourname/notebooks/coolthing.ipynb`.
However, let's break down what this URL means:
`hub.jupyter.org/user/yourname/` is the URL prefix handled by _your server_,
which means that sharing this URL is asking the person you share the link with
to come to _your server_ and look at the exact same file.
In most circumstances, this is forbidden by permissions because the person you share with does not have access to your server.
What actually happens when someone visits this URL will depend on whether your server is running and other factors.
**But what is our actual goal?**
A typical situation is that you have some shared or common filesystem,
such that the same path corresponds to the same document
(either the exact same document or another copy of it).
Typically, what folks want when they do sharing like this
is for each visitor to open the same file _on their own server_,
so Breq would open `/user/breq/notebooks/foo.ipynb` and
Seivarden would open `/user/seivarden/notebooks/foo.ipynb`, etc.
JupyterHub has a special URL that does exactly this!
It's called `/hub/user-redirect/...`.
So if you replace `/user/yourname` in your URL bar
with `/hub/user-redirect` any visitor should get the same
URL on their own server, rather than visiting yours.
In JupyterLab 2.0, this should also be the result of the "Copy Shareable Link"
action in the file browser.

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@@ -0,0 +1,19 @@
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
config-basics
networking-basics
security-basics
authenticators-users-basics
spawners-basics
services-basics
faq
institutional-faq

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@@ -1,5 +1,3 @@
(faq:institutional)=
# Institutional FAQ
This page contains common questions from users of JupyterHub,
@@ -66,14 +64,14 @@ industry, and government research labs. It is most-commonly used by two kinds of
Here is a sample of organizations that use JupyterHub:
- **Universities and colleges**: UC Berkeley, UC San Diego, Cal Poly SLO, Harvard University, University of Chicago,
University of Oslo, University of Sheffield, Université Paris Sud, University of Versailles, University of Portland
University of Oslo, University of Sheffield, Université Paris Sud, University of Versailles
- **Research laboratories**: NASA, NCAR, NOAA, the Large Synoptic Survey Telescope, Brookhaven National Lab,
Minnesota Supercomputing Institute, ALCF, CERN, Lawrence Livermore National Laboratory, HUNT
Minnesota Supercomputing Institute, ALCF, CERN, Lawrence Livermore National Laboratory
- **Online communities**: Pangeo, Quantopian, mybinder.org, MathHub, Open Humans
- **Computing infrastructure providers**: NERSC, San Diego Supercomputing Center, Compute Canada
- **Companies**: Capital One, SANDVIK code, Globus
See the [Gallery of JupyterHub deployments](gallery-of-deployments) for
See the [Gallery of JupyterHub deployments](../gallery-jhub-deployments.md) for
a more complete list of JupyterHub deployments at institutions.
### How does JupyterHub compare with hosted products, like Google Colaboratory, RStudio.cloud, or Anaconda Enterprise?
@@ -126,15 +124,15 @@ as more resources are needed - allowing you to utilize the benefits of a flexibl
### Is JupyterHub secure?
The short answer: yes.
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](explanation:security).
[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://z2jh.jupyter.org/en/latest/security.html).
[JupyterHub on Kubernetes security page](https://zero-to-jupyterhub.readthedocs.io/en/latest/security.html).
The longer answer: it depends on your deployment. Because JupyterHub is very flexible, it can be used
in a variety of deployment setups. This often entails connecting your JupyterHub to **other** infrastructure
@@ -142,7 +140,7 @@ in a variety of deployment setups. This often entails connecting your JupyterHub
in these cases, and the security of your JupyterHub deployment will often depend on these decisions.
If you are worried about security, don't hesitate to reach out to the JupyterHub community in the
[Jupyter Community Forum](https://discourse.jupyter.org/c/jupyterhub/10). This community of practice has many
[Jupyter Community Forum](https://discourse.jupyter.org/c/jupyterhub). This community of practice has many
individuals with experience running secure JupyterHub deployments and will be very glad to help you out.
### Does JupyterHub provide computing or data infrastructure?

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@@ -0,0 +1,254 @@
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, remove ``c.JupyterHub.ssl_key`` and ``c.JupyterHub.ssl_cert``
from your configuration (setting them to ``None`` or an empty string does not
have the same effect, and will result in an error).
.. _authentication-token:
Proxy authentication token
--------------------------
The Hub authenticates its requests to the Proxy using a secret token that
the Hub and Proxy agree upon. Note that this applies to the default
``ConfigurableHTTPProxy`` implementation. Not all proxy implementations
use an auth token.
The value of this token should be a random string (for example, generated by
``openssl rand -hex 32``). You can store it in the configuration file or an
environment variable.
Generating and storing token in the configuration file
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You can set the value in the configuration file, ``jupyterhub_config.py``:
.. code-block:: python
c.ConfigurableHTTPProxy.api_token = 'abc123...' # any random string
Generating and storing as an environment variable
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You can pass this value of the proxy authentication token to the Hub and Proxy
using the ``CONFIGPROXY_AUTH_TOKEN`` environment variable:
.. code-block:: bash
export CONFIGPROXY_AUTH_TOKEN=$(openssl rand -hex 32)
This environment variable needs to be visible to the Hub and Proxy.
Default if token is not set
~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you do not set the Proxy authentication token, the Hub will generate a random
key itself. This means that any time you restart the Hub, you **must also
restart the Proxy**. If the proxy is a subprocess of the Hub, this should happen
automatically (this is the default configuration).
.. _cookie-secret:
Cookie secret
-------------
The cookie secret is an encryption key, used to encrypt the browser cookies,
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 users 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 users server receives it.

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@@ -1,5 +1,3 @@
(tutorial:services)=
# External services
When working with JupyterHub, a **Service** is defined as a process
@@ -16,7 +14,7 @@ document will:
- explain some basic information about API tokens
- clarify that API tokens can be used to authenticate to
single-user servers as of [version 0.8.0](changelog)
single-user servers as of [version 0.8.0](../changelog)
- show how the [jupyterhub_idle_culler][] script can be:
- used in a Hub-managed service
- run as a standalone script
@@ -31,19 +29,19 @@ Hub via the REST API.
To run such an external service, an API token must be created and
provided to the service.
As of [version 0.6.0](changelog), the preferred way of doing
As of [version 0.6.0](../changelog), the preferred way of doing
this is to first generate an API token:
```bash
openssl rand -hex 32
```
In [version 0.8.0](changelog), a TOKEN request page for
In [version 0.8.0](../changelog), a TOKEN request page for
generating an API token is available from the JupyterHub user interface:
![Request API TOKEN page](/images/token-page.png)
![Request API TOKEN page](../images/token-request.png)
![API TOKEN success page](/images/token-request-success.png)
![API TOKEN success page](../images/token-request-success.png)
### Step 2: Pass environment variable with token to the Hub

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@@ -1,5 +1,3 @@
(spawners)=
# Spawners and single-user notebook servers
A Spawner starts each single-user notebook server. Since the single-user server is an instance of `jupyter notebook`, an entire separate

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@@ -1,130 +0,0 @@
# Logging users in via URL
Sometimes, JupyterHub is integrated into an existing application that has already handled user login, etc..
It is often preferable in these applications to be able to link users to their running JupyterHub server without _prompting_ the user to login again with the Hub when the Hub should really be an implementation detail,
and not part of the user experience.
One way to do this has been to use [API only mode](#howto:api-only), issue tokens for users, and redirect users to a URL like `/users/name/?token=abc123`.
This is [disabled by default](#HubAuth.allow_token_in_url) in JupyterHub 5, because it presents a vulnerability for users to craft links that let _other_ users login as them, which can lead to inter-user attacks.
But that leaves the question: how do I as an _application developer_ embedding JupyterHub link users to their own running server without triggering another login prompt?
The problem with `?token=...` in the URL is specifically that _users_ can get and create these tokens, and share URLs.
This wouldn't be an issue if only authorized applications could issue tokens that behave this way.
The single-user server doesn't exactly have the hooks to manage this easily, but the [Authenticator](#Authenticator) API does.
## Problem statement
We want our external application to be able to:
1. authenticate users
2. (maybe) create JupyterHub users
3. start JupyterHub servers
4. redirect users into running servers _without_ any login prompts/loading pages from JupyterHub, and without any prior JupyterHub credentials
Step 1 is up to the application and not JupyterHub's problem.
Step 2 and 3 use the JupyterHub [REST API](#jupyterhub-rest-API).
The service would need the scopes:
```
admin:users # creating users
servers # start/stop servers
```
That leaves the last step: sending users to their running server with credentials, without prompting login.
This is where things can get tricky!
### Ideal case: oauth
_Ideally_, the best way to set this up is with the external service as an OAuth provider,
though in some cases it works best to use proxy-based authentication like Shibboleth / [REMOTE_USER](https://github.com/cwaldbieser/jhub_remote_user_authenticator).
The main things to know are:
- Links to `/hub/user-redirect/some/path` will ultimately land users at `/users/theirserver/some/path` after completing login, ensuring the server is running, etc.
- Setting `Authenticator.auto_login = True` allows beginning the login process without JupyterHub's "Login with..." prompt
_If_ your OAuth provider allows logging in to external services via your oauth provider without prompting, this is enough.
Not all do, though.
If you've already ensured the server is running, this will _appear_ to the user as if they are being sent directly to their running server.
But what _actually_ happens is quite a series of redirects, state checks, and cookie-setting:
1. visiting `/hub/user-redirect/some/path` checks if the user is logged in
1. if not, begin the login process (`/hub/login?next=/hub/user-redirect/...`)
2. redirects to your oauth provider to authenticate the user
3. redirects back to `/hub/oauth_callback` to complete login
4. redirects back to `/hub/user-redirect/...`
2. once authenticated, checks that the user's server is running
1. if not running, begins launch of the server
2. redirects to `/hub/spawn-pending/?next=...`
3. once the server is running, redirects to the actual user server `/users/username/some/path`
Now we're done, right? Actually, no, because the browser doesn't have credentials for their user server!
This sequence of redirects happens all the time in JupyterHub launch, and is usually totally transparent.
4. at the user server, check for a token in cookie
1. if not present or not valid, begin oauth with the Hub (redirect to `/hub/api/oauth2/authorize/...`)
2. hub redirects back to `/users/user/oauth_callback` to complete oauth
3. redirect again to the URL that started this internal oauth
5. finally, arrive at `/users/username/some/path`, the ultimate destination, with valid JupyterHub credentials
The steps that will show users something other than the page you want them to are:
- Step 1.1 will be a prompt e.g. with "Login with..." unless you set `c.Authenticator.auto_login = True`
- Step 1.2 _may_ be a prompt from your oauth provider. This isn't controlled by JupyterHub, and may not be avoidable.
- Step 2.2 will show the spawn pending page only if the server is not already running
Otherwise, this is all transparent redirects to the final destination.
#### Using an authentication proxy (REMOTE_USER)
If you use an Authentication proxy like Shibboleth that sets e.g. the REMOTE_USER header,
you can use an Authenticator like [RemoteUserAuthenticator](https://github.com/cwaldbieser/jhub_remote_user_authenticator) to automatically login users based on headers in the request.
The same process will work, but instead of step 1.1 redirecting to the oauth provider, it logs in immediately.
If you do support an auth proxy, you also need to be extremely sure that requests only come from the auth proxy, and don't accept any requests setting the REMOTE_USER header coming from other sources.
### Custom case
But let's say you can't use OAuth or REMOTE_USER, and you still want to hide JupyterHub implementation details.
All you really want is a way to write a URL that will take users to their servers without any login prompts.
You can do this if you create an Authenticator with `auto_login=True` that logs users in based on something in the _request_, e.g. a query parameter.
We have an _example_ in the JupyterHub repo in `examples/forced-login` that does this.
It is a sample 'external service' where you type in a username and a destination path.
When you 'login' with this username:
1. a token is issued
2. the token is stored and associated with the username
3. redirect to `/hub/login?login_token=...&next=/hub/user-redirect/destination/path`
Then on the JupyterHub side, there is the `ForcedLoginAuthenticator`.
This class implements `authenticate`, which:
1. has `auto_login = True` so visiting `/hub/login` calls `authenticate()` directly instead of serving a page
2. gets the token from the `login_token` URL parameter
3. makes a POST request to the external application with the token, requesting a username
4. the external application returns the username and deletes the token, so it cannot be re-used
5. Authenticator returns the username
This doesn't _bypass_ JupyterHub authentication, as some deployments have done, but it does _hide_ it.
If your service launches servers via the API, you could run this in [API only mode](#howto:api-only) by adding `/hub/login` as well:
```python
c.JupyterHub.hub_routespec = "/hub/api/"
c.Proxy.additional_routes = {"/hub/login": "http://hub:8080"}
```
```{literalinclude} ../../../examples/forced-login/jupyterhub_config.py
:language: python
:start-at: class ForcedLoginAuthenticator
:end-before: c = get_config()
```
**Why does this work?**
This is still logging in with a token in the URL, right?
Yes, but the key difference is that users cannot issue these tokens.
The sample application is still technically vulnerable, because the token link should really be non-transferrable, even if it can only be used once.
The only defense the sample application has against this is rapidly expiring tokens (they expire after 30 seconds).
You can use state cookies, etc. to manage that more rigorously, as done in OAuth (at which point, maybe implement OAuth itself, why not?).

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@@ -1,34 +0,0 @@
# How-to
The _How-to_ guides provide practical step-by-step details to help you achieve a particular goal. They are useful when you are trying to get something done but require you to understand and adapt the steps to your specific usecase.
Use the following guides when:
```{toctree}
:maxdepth: 1
api-only
proxy
rest
separate-proxy
templates
upgrading
log-messages
forced-login
```
(config-examples)=
## Configuration
The following guides provide examples, including configuration files and tips, for the
following:
```{toctree}
:maxdepth: 1
configuration/config-user-env
configuration/config-ghoauth
configuration/config-proxy
configuration/config-sudo
```

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@@ -1,144 +0,0 @@
(howto:upgrading-v5)=
# Upgrading to JupyterHub 5
This document describes the specific considerations.
For general upgrading tips, see the [docs on upgrading jupyterhub](upgrading).
You can see the [changelog](changelog) for more detailed information.
## Python version
JupyterHub 5 requires Python 3.8.
Make sure you have at least Python 3.8 in your user and hub environments before upgrading.
## Database upgrades
JupyterHub 5 does have a database schema upgrade,
so you should backup your database and run `jupyterhub upgrade-db` after upgrading and before starting JupyterHub.
The updated schema only adds some columns, so is one that should be not too disruptive to roll back if you need to.
## User subdomains
All JupyterHub deployments which care about protecting users from each other are encouraged to enable per-user domains, if possible,
as this provides the best isolation between user servers.
To enable subdomains, set:
```python
c.JupyterHub.subdomain_host = "https://myjupyterhub.example.org"
```
If you were using subdomains before, some user servers and all services will be on different hosts in the default configuration.
JupyterHub 5 allows complete customization of the subdomain scheme via the new {attr}`.JupyterHub.subdomain_hook`,
and changes the default subdomain scheme.
.
You can provide a completely custom subdomain scheme, or select one of two default implementations by name: `idna` or `legacy`. `idna` is the default.
The new default behavior can be selected explicitly via:
```python
c.JupyterHub.subdomain_hook = "idna"
```
Or to delay any changes to URLs for your users, you can opt-in to the pre-5.0 behavior with:
```python
c.JupyterHub.subdomain_hook = "legacy"
```
The key differences of the new `idna` scheme:
- It should always produce valid domains, regardless of username (not true for the legacy scheme when using characters that might need escaping or usernames that are long)
- each Service gets its own subdomain on `service--` rather than sharing `services.`
Below is a table of examples of users and services with their domains with the old and new scheme, assuming the configuration:
```python
c.JupyterHub.subdomain_host = "https://jupyter.example.org"
```
| kind | name | legacy | idna |
| ------- | ------------------ | ---------------------------------------------------------- | ----------------------------------------------------------------------------------------------------- |
| user | laudna | `laudna.jupyter.example.org` | `laudna.jupyter.example.org` |
| service | bells | `services.jupyter.example.org` | `bells--service.jupyter.example.org` |
| user | jester@mighty.nein | `jester_40mighty.nein.jupyter.example.org` (may not work!) | `u-jestermi--8037680.jupyter.example.org` (not as pretty, but guaranteed to be valid and not collide) |
## Tokens in URLs
JupyterHub 5 does not accept `?token=...` URLs by default in single-user servers.
These URLs allow one user to force another to login as them,
which can be the start of an inter-user attack.
There is a valid use case for producing links which allow starting a fully authenticated session,
so you may still opt in to this behavior by setting:
```python
c.Spawner.environment.update({"JUPYTERHUB_ALLOW_TOKEN_IN_URL": "1"})
```
if you are not concerned about protecting your users from each other.
If you have subdomains enabled, the threat is substantially reduced.
## Sharing
The big new feature in JupyterHub 5.0 is sharing.
Check it out in [the sharing docs](sharing-tutorial).
## Authenticator.allow_all and allow_existing_users
Prior to JupyterHub 5, JupyterHub Authenticators had the _implicit_ default behavior to allow any user who successfully authenticates to login **if no users are explicitly allowed** (i.e. `allowed_users` is empty on the base class).
This behavior was considered a too-permissive default in Authenticators that source large user pools like OAuthenticator, which would accept e.g. all users with a Google account by default.
As a result, OAuthenticator 16 introduced two configuration options: `allow_all` and `allow_existing_users`.
JupyterHub 5 adopts these options for all Authenticators:
1. `Authenticator.allow_all` (default: False)
2. `Authenticator.allow_existing_users` (default: True if allowed_users is non-empty, False otherwise)
having the effect that _some_ allow configuration is required for anyone to be able to login.
If you want to preserve the pre-5.0 behavior with no explicit `allow` configuration, set:
```python
c.Authenticator.allow_all = True
```
`allow_existing_users` defaults are meant to be backward-compatible, but you can now _explicitly_ allow or not based on presence in the database by setting `Authenticator.allow_existing_users` to True or False.
:::{seealso}
[Authenticator config docs](authenticators) for details on these and other Authenticator options.
:::
## Bootstrap 5
JupyterHub uses the CSS framework [bootstrap](https://getbootstrap.com), which is upgraded from 3.4 to 5.3.
If you don't have any custom HTML templates, you are likely to only see relatively minor aesthetic changes.
If you have custom HTML templates or spawner options forms, they may need some updating to look right.
See the bootstrap documentation. Since we upgraded two major versions, you might need to look at both v4 and v5 documentation for what has changed since 3.x:
- [migrating to v4](https://getbootstrap.com/docs/4.6/migration/)
- [migrating to v5](https://getbootstrap.com/docs/5.3/migration/)
If you customized the JupyterHub CSS by recompiling from LESS files, bootstrap migrated to SCSS.
You can start by autoconverting your LESS to SCSS (it's not that different) with [less2sass](https://github.com/ekryski/less2sass):
```bash
npm install --global less2scss
# converts less/foo.less to scss/foo.scss
less2scss --src ./less --dst ./scss
```
Bootstrap also allows configuring things with [CSS variables](https://getbootstrap.com/docs/5.3/customize/css-variables/), so depending on what you have customized, you may be able to get away with just adding a CSS file defining variables without rebuilding the whole SCSS.
## groups required with Authenticator.manage_groups
Setting `Authenticator.manage_groups = True` allows the Authenticator to manage group membership by returning `groups` from the authentication model.
However, this option is available even on Authenticators that do not support it, which led to confusion.
Starting with JupyterHub 5, if `manage_groups` is True `authenticate` _must_ return a groups field, otherwise an error is raised.
This prevents confusion when users enable managed groups that is not implemented.
If an Authenticator _does_ support managing groups but was not providing a `groups` field in order to leave membership unmodified, it must specify `"groups": None` to make this explicit instead of implicit (this is backward-compatible).

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@@ -1,138 +0,0 @@
(howto:upgrading-jupyterhub)=
# 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.
For specific version migrations:
```{toctree}
:maxdepth: 1
./upgrading-v5
```
## Read the Changelog
The [changelog](changelog) 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 and 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 and 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 and 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.
### 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!

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=====
About
=====
JupyterHub is an open source project and community. It is a part of the
`Jupyter Project <https://jupyter.org>`_. JupyterHub is an open and inclusive
community, and invites contributions from anyone. This section covers information
about our community, as well as ways that you can connect and get involved.
.. toctree::
:maxdepth: 1
contributor-list
changelog
gallery-jhub-deployments

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@@ -0,0 +1,15 @@
=====================
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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@@ -1,137 +0,0 @@
# 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.
(index/distributions)=
## Distributions
JupyterHub can be used in a collaborative environment by 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 two main distributions which are developed to serve the needs of each of these teams respectively.
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.
This distribution runs JupyterHub on top of [Kubernetes](https://kubernetes.io/).
```{note}
It is important to evaluate these distributions before you can continue with the
configuration of JupyterHub.
```
## Subsystems
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).
---
## Documentation structure
### Tutorials
This section of the documentation contains step-by-step tutorials that help outline the capabilities of JupyterHub and how you can achieve specific aims, such as installing it. The tutorials are recommended if you do not have much experience with JupyterHub.
```{toctree}
:maxdepth: 2
tutorial/index.md
```
### How-to guides
The _How-to_ guides provide more in-depth details than the tutorials. They are recommended for those already familiar with JupyterHub and have a specific goal. The guides help answer the question _"How do I ...?"_ based on a particular topic.
```{toctree}
:maxdepth: 2
howto/index.md
```
### Explanation
The _Explanation_ section provides further details that can be used to better understand JupyterHub, such as how it can be used and configured. They are intended for those seeking to expand their knowledge of JupyterHub.
```{toctree}
:maxdepth: 2
explanation/index.md
```
### Reference
The _Reference_ section provides technical information about JupyterHub, such as monitoring the state of your installation and working with JupyterHub's API modules and classes.
```{toctree}
:maxdepth: 2
reference/index.md
```
### Frequently asked questions
Find answers to the most frequently asked questions about JupyterHub such as how to troubleshoot an issue.
```{toctree}
:maxdepth: 2
faq/index.md
```
### Contributing
JupyterHub welcomes all contributors, whether you are new to the project or know your way around. The _Contributing_ section provides information on how you can make your contributions.
```{toctree}
:maxdepth: 2
contributing/index
```
---
## 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

161
docs/source/index.rst Normal file
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@@ -0,0 +1,161 @@
==========
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
: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
-------------
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!
.. _JupyterHub: https://github.com/jupyterhub/jupyterhub
.. _Jupyter notebook: https://jupyter-notebook.readthedocs.io/en/latest/

View File

@@ -6,7 +6,7 @@ JupyterHub is supported on Linux/Unix based systems. To use JupyterHub, you need
a Unix server (typically Linux) running somewhere that is accessible to your
team on the network. The JupyterHub server can be on an internal network at your
organization, or it can run on the public internet (in which case, take care
with the Hub's [security](security-basics)).
with the Hub's [security](./getting-started/security-basics)).
JupyterHub officially **does not** support Windows. You may be able to use
JupyterHub on Windows if you use a Spawner and Authenticator that work on
@@ -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:](https://www.tornadoweb.org/en/stable/#installation)
[Additional Reference:](http://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](https://www.sqlalchemy.org))
MySQL, or other databases supported by [SQLAlchemy](http://www.sqlalchemy.org))
## Folders and File Locations

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@@ -1,7 +0,0 @@
---
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>

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