Compare commits

..

20 Commits

Author SHA1 Message Date
Min RK
a3c93088a8 Bump to 2.3.0 2022-05-06 16:05:34 +02:00
Min RK
834229622d Merge pull request #3887 from minrk/2.3-backports
2.3 backports
2022-05-06 16:05:10 +02:00
Min RK
44a1ea42de One more in the changelog 2022-05-06 15:56:13 +02:00
Simon Li
3879a96b67 Backport PR #3886: Cleanup everything on API shutdown
`app.stop` triggers full cleanup and stopping of the event loop

closes  3881

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-06 15:55:00 +02:00
Min RK
d40627d397 changelog for 2.3 2022-05-05 13:24:00 +02:00
Min RK
057cdbc9e9 pre-commit autoupdate 2022-05-05 13:23:52 +02:00
Min RK
75390d2e46 Backport PR #3882: Use log.exception when logging exceptions
This provides the stack trace in the log file, incredibly
useful when debugging

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:28 +02:00
Min RK
f5e4846cfa Backport PR #3874: Missing f prefix on f-strings fix
Fixes  3873

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:27 +02:00
Georgiana Elena
3dc115a829 Backport PR #3876: don't confuse :// in next_url query params for a redirect hostname
closes  3014

These query params should be url-encoded (https://github.com/jupyterhub/nbgitpuller/issues/118), but we still shouldn't be making the wrong assumptions about when a hostname is specified

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:25 +02:00
Min RK
af4ddbfc58 Backport PR #3867: ci: update black configuration
Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:24 +02:00
Min RK
50a4d1e34d Backport PR #3863: [Bug Fix] Search bar disabled on admin dashboard
I originally had `defaultValue` here and I changed it not realizing this would break/disable the input.

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:23 +02:00
Erik Sundell
86a238334c Backport PR #3862: Fix typo in [rest api] link in README.md
Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:22 +02:00
Simon Li
dacb9d1668 Backport PR #3859: Do not store Spawner.ip/port on spawner.server during get_env
we shouldn't mutate db state when getting the environment.

IIRC, this was part of an attempt to get the url via `self.server.bind_url` that didn't end up getting used in  3381. So this doesn't really have any positive effects, but it _can_ have negative effects if `get_env` is called in unusual circumstances (jupyterhub/batchspawner 236)

closes jupyterhub/batchspawner 236

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:21 +02:00
Min RK
95cc170383 Backport PR #3853: Fix xsrf_cookie_kwargs ValueError
Fixes

`ValueError: too many values to unpack (expected 2)`

Related to code added as a fix for https://github.com/jupyterhub/jupyterhub/issues/3821

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:20 +02:00
Erik Sundell
437a9d150f Backport PR #3849: The word used is duplicated in upgrade.md
This PR is to update doc for that the word `used` is duplicated in this doc.

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:19 +02:00
Erik Sundell
c9616d6f11 Backport PR #3843: Some typos in docs
- fix some references to old 'all' name which was renamed 'inherit'
- fix a heading level in changlog that sphinx warns about

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:18 +02:00
Min RK
61aed70c4d Backport PR #3841: adopt pytest-asyncio asyncio_mode='auto'
removes need for our own implementation of the same behavior in conftest

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:17 +02:00
Erik Sundell
9abb573d47 Backport PR #3839: Document version mismatch log message
Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:16 +02:00
Erik Sundell
b074304834 Backport PR #3835: remove lingering reference to distutils
traitlets, like most Jupyter projects (and Python itself), has a `.version_info` tuple to avoid needing to parse versions

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:15 +02:00
Min RK
201e7ca3d8 Backport PR #3834: Admin Dashboard - Collapsible Details View
I made this PR to see if this feature would be useful for other people. Right now, you can't see all of a user or server's details in the admin page so I added a collapsible view which will let you see the entire server and user objects. I'm open to ideas about how the information is displayed. Will add more tests if this feature is accepted.

![improved-collapse](https://user-images.githubusercontent.com/737367/158468531-1efc28e6-a229-4383-b5f9-b301898d929f.gif)

Signed-off-by: Min RK <benjaminrk@gmail.com>
2022-05-05 13:15:14 +02:00
304 changed files with 8507 additions and 14931 deletions

View File

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

View File

@@ -1,15 +0,0 @@
# 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

@@ -32,18 +32,17 @@ jobs:
build-release:
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
- uses: actions/checkout@v2
- uses: actions/setup-python@v2
with:
python-version: "3.9"
python-version: 3.8
- uses: actions/setup-node@v3
- uses: actions/setup-node@v1
with:
node-version: "14"
- name: install build requirements
- name: install build package
run: |
npm install -g yarn
pip install --upgrade pip
pip install build
pip freeze
@@ -53,21 +52,28 @@ jobs:
python -m build --sdist --wheel .
ls -l dist
- name: verify sdist
- name: verify wheel
run: |
./ci/check_sdist.py dist/jupyterhub-*.tar.gz
- name: verify data-files are installed where they are found
run: |
pip install dist/*.whl
./ci/check_installed_data.py
- name: verify sdist can be installed without npm/yarn
run: |
docker run --rm -v $PWD/dist:/dist:ro docker.io/library/python:3.9-slim-bullseye bash -c 'pip install /dist/jupyterhub-*.tar.gz'
cd dist
pip install ./*.whl
# verify data-files are installed where they are found
cat <<EOF | python
import os
from jupyterhub._data import DATA_FILES_PATH
print(f"DATA_FILES_PATH={DATA_FILES_PATH}")
assert os.path.exists(DATA_FILES_PATH), DATA_FILES_PATH
for subpath in (
"templates/page.html",
"static/css/style.min.css",
"static/components/jquery/dist/jquery.js",
):
path = os.path.join(DATA_FILES_PATH, subpath)
assert os.path.exists(path), path
print("OK")
EOF
# ref: https://github.com/actions/upload-artifact#readme
- uses: actions/upload-artifact@v3
- uses: actions/upload-artifact@v2
with:
name: jupyterhub-${{ github.sha }}
path: "dist/*"
@@ -84,7 +90,6 @@ jobs:
publish-docker:
runs-on: ubuntu-20.04
timeout-minutes: 30
services:
# So that we can test this in PRs/branches
@@ -103,16 +108,16 @@ jobs:
echo "REGISTRY=localhost:5000/" >> $GITHUB_ENV
fi
- uses: actions/checkout@v3
- uses: actions/checkout@v2
# Setup docker to build for multiple platforms, see:
# https://github.com/docker/build-push-action/tree/v2.4.0#usage
# https://github.com/docker/build-push-action/blob/v2.4.0/docs/advanced/multi-platform.md
- name: Set up QEMU (for docker buildx)
uses: docker/setup-qemu-action@v2
uses: docker/setup-qemu-action@25f0500ff22e406f7191a2a8ba8cda16901ca018 # associated tag: v1.0.2
- name: Set up Docker Buildx (for multi-arch builds)
uses: docker/setup-buildx-action@v2
uses: docker/setup-buildx-action@2a4b53665e15ce7d7049afb11ff1f70ff1610609 # associated tag: v1.1.2
with:
# Allows pushing to registry on localhost:5000
driver-opts: network=host
@@ -150,7 +155,7 @@ jobs:
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub
uses: docker/build-push-action@3b5e8027fcad23fda98b2e3ac259d8d67585f671
uses: docker/build-push-action@e1b7f96249f2e4c8e4ac1519b9608c0d48944a1f
with:
context: .
platforms: linux/amd64,linux/arm64
@@ -171,7 +176,7 @@ jobs:
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub-onbuild
uses: docker/build-push-action@3b5e8027fcad23fda98b2e3ac259d8d67585f671
uses: docker/build-push-action@e1b7f96249f2e4c8e4ac1519b9608c0d48944a1f
with:
build-args: |
BASE_IMAGE=${{ fromJson(steps.jupyterhubtags.outputs.tags)[0] }}
@@ -192,7 +197,7 @@ jobs:
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub-demo
uses: docker/build-push-action@3b5e8027fcad23fda98b2e3ac259d8d67585f671
uses: docker/build-push-action@e1b7f96249f2e4c8e4ac1519b9608c0d48944a1f
with:
build-args: |
BASE_IMAGE=${{ fromJson(steps.onbuildtags.outputs.tags)[0] }}
@@ -216,7 +221,7 @@ jobs:
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub/singleuser
uses: docker/build-push-action@3b5e8027fcad23fda98b2e3ac259d8d67585f671
uses: docker/build-push-action@e1b7f96249f2e4c8e4ac1519b9608c0d48944a1f
with:
build-args: |
JUPYTERHUB_VERSION=${{ github.ref_type == 'tag' && github.ref_name || format('git:{0}', github.sha) }}

View File

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

View File

@@ -15,13 +15,15 @@ on:
- "docs/**"
- "jupyterhub/_version.py"
- "jupyterhub/scopes.py"
- ".github/workflows/test-docs.yml"
- ".github/workflows/*"
- "!.github/workflows/test-docs.yml"
push:
paths:
- "docs/**"
- "jupyterhub/_version.py"
- "jupyterhub/scopes.py"
- ".github/workflows/test-docs.yml"
- ".github/workflows/*"
- "!.github/workflows/test-docs.yml"
branches-ignore:
- "dependabot/**"
- "pre-commit-ci-update-config"
@@ -38,37 +40,25 @@ jobs:
validate-rest-api-definition:
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v2
- name: Validate REST API definition
uses: char0n/swagger-editor-validate@v1.3.2
uses: char0n/swagger-editor-validate@182d1a5d26ff5c2f4f452c43bd55e2c7d8064003
with:
definition-file: docs/source/_static/rest-api.yml
test-docs:
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
- uses: actions/checkout@v2
- uses: actions/setup-python@v2
with:
python-version: "3.9"
- name: Install requirements
run: |
pip install -r docs/requirements.txt pytest
pip install -r docs/requirements.txt pytest -e .
- name: pytest docs/
run: |
pytest docs/
# readthedocs doesn't halt on warnings,
# so raise any warnings here
- name: build docs
run: |
cd docs
make html
- name: check links
run: |
cd docs
make linkcheck

View File

@@ -19,9 +19,6 @@ on:
- "**"
workflow_dispatch:
permissions:
contents: read
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
@@ -32,8 +29,8 @@ jobs:
timeout-minutes: 5
steps:
- uses: actions/checkout@v3
- uses: actions/setup-node@v3
- uses: actions/checkout@v2
- uses: actions/setup-node@v1
with:
node-version: "14"
@@ -50,3 +47,62 @@ jobs:
run: |
cd jsx
yarn test
# The ./jsx folder contains React based source files that are to compile to
# share/jupyterhub/static/js/admin-react.js. This job makes sure that whatever
# we have in jsx/src matches the compiled asset that we package and
# distribute.
#
# This job's purpose is to make sure we don't forget to compile changes and to
# verify nobody sneaks in a change in the hard to review compiled asset.
#
# NOTE: In the future we may want to stop version controlling the compiled
# artifact and instead generate it whenever we package JupyterHub. If we
# do this, we are required to setup node and compile the source code
# more often, at the same time we could avoid having this check be made.
#
compile-jsx-admin-react:
runs-on: ubuntu-20.04
timeout-minutes: 5
steps:
- uses: actions/checkout@v2
- uses: actions/setup-node@v1
with:
node-version: "14"
- name: Install yarn
run: |
npm install -g yarn
- name: yarn
run: |
cd jsx
yarn
- name: yarn build
run: |
cd jsx
yarn build
- name: yarn place
run: |
cd jsx
yarn place
- name: Verify compiled jsx/src matches version controlled artifact
run: |
if [[ `git status --porcelain=v1` ]]; then
echo "The source code in ./jsx compiles to something different than found in ./share/jupyterhub/static/js/admin-react.js!"
echo
echo "Please re-compile the source code in ./jsx with the following commands:"
echo
echo "yarn"
echo "yarn build"
echo "yarn place"
echo
echo "See ./jsx/README.md for more details."
exit 1
else
echo "Compilation of jsx/src to share/jupyterhub/static/js/admin-react.js didn't lead to changes."
fi

View File

@@ -28,10 +28,7 @@ on:
env:
# UTF-8 content may be interpreted as ascii and causes errors without this.
LANG: C.UTF-8
SQLALCHEMY_WARN_20: "1"
permissions:
contents: read
PYTEST_ADDOPTS: "--verbose --color=yes"
jobs:
# Run "pytest jupyterhub/tests" in various configurations
@@ -56,9 +53,9 @@ jobs:
# Tests everything when JupyterHub works against a dedicated mysql or
# postgresql server.
#
# legacy_notebook:
# nbclassic:
# Tests everything when the user instances are started with
# the legacy notebook server instead of jupyter_server.
# notebook instead of jupyter_server.
#
# ssl:
# Tests everything using internal SSL connections instead of
@@ -71,36 +68,21 @@ jobs:
# NOTE: Since only the value of these parameters are presented in the
# GitHub UI when the workflow run, we avoid using true/false as
# values by instead duplicating the name to signal true.
# Python versions available at:
# https://github.com/actions/python-versions/blob/HEAD/versions-manifest.json
include:
- python: "3.7"
- python: "3.6"
oldest_dependencies: oldest_dependencies
legacy_notebook: legacy_notebook
- python: "3.8"
jupyter_server: "1.*"
subset: singleuser
- python: "3.9"
nbclassic: nbclassic
- python: "3.6"
subdomain: subdomain
- python: "3.7"
db: mysql
- python: "3.10"
- python: "3.7"
ssl: ssl
- python: "3.8"
db: postgres
- python: "3.11"
subdomain: subdomain
serverextension: serverextension
- python: "3.11"
ssl: ssl
serverextension: serverextension
- python: "3.11"
subdomain: subdomain
noextension: noextension
subset: singleuser
- python: "3.11"
ssl: ssl
noextension: noextension
subset: singleuser
- python: "3.11"
selenium: selenium
- python: "3.11"
- python: "3.8"
nbclassic: nbclassic
- python: "3.9"
main_dependencies: main_dependencies
steps:
@@ -125,35 +107,32 @@ 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
- uses: actions/checkout@v3
# NOTE: actions/setup-node@v3 make use of a cache within the GitHub base
- uses: actions/checkout@v2
# NOTE: actions/setup-node@v1 make use of a cache within the GitHub base
# environment and setup in a fraction of a second.
- name: Install Node v14
uses: actions/setup-node@v3
uses: actions/setup-node@v1
with:
node-version: "14"
- name: Install Javascript dependencies
- name: Install Node dependencies
run: |
npm install
npm install -g configurable-http-proxy yarn
npm install -g configurable-http-proxy
npm list
# NOTE: actions/setup-python@v4 make use of a cache within the GitHub base
# NOTE: actions/setup-python@v2 make use of a cache within the GitHub base
# environment and setup in a fraction of a second.
- name: Install Python ${{ matrix.python }}
uses: actions/setup-python@v4
uses: actions/setup-python@v2
with:
python-version: "${{ matrix.python }}"
python-version: ${{ matrix.python }}
- name: Install Python dependencies
run: |
pip install --upgrade pip
pip install -e ".[test]"
pip install --upgrade . -r dev-requirements.txt
if [ "${{ matrix.oldest_dependencies }}" != "" ]; then
# take any dependencies in requirements.txt such as tornado>=5.0
@@ -165,14 +144,10 @@ jobs:
if [ "${{ matrix.main_dependencies }}" != "" ]; then
pip install git+https://github.com/ipython/traitlets#egg=traitlets --force
pip install --upgrade --pre sqlalchemy
fi
if [ "${{ matrix.legacy_notebook }}" != "" ]; then
if [ "${{ matrix.nbclassic }}" != "" ]; then
pip uninstall jupyter_server --yes
pip install 'notebook<7'
fi
if [ "${{ matrix.jupyter_server }}" != "" ]; then
pip install "jupyter_server==${{ matrix.jupyter_server }}"
pip install notebook
fi
if [ "${{ matrix.db }}" == "mysql" ]; then
pip install mysql-connector-python
@@ -180,9 +155,6 @@ jobs:
if [ "${{ matrix.db }}" == "postgres" ]; then
pip install psycopg2-binary
fi
if [ "${{ matrix.serverextension }}" != "" ]; then
pip install 'jupyter-server>=2'
fi
pip freeze
@@ -227,22 +199,19 @@ jobs:
DB=postgres bash ci/init-db.sh
fi
- 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
- uses: codecov/codecov-action@v3
pytest --maxfail=2 --cov=jupyterhub jupyterhub/tests
- name: Submit codecov report
run: |
codecov
docker-build:
runs-on: ubuntu-20.04
timeout-minutes: 20
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v2
- name: build images
run: |

5
.gitignore vendored
View File

@@ -9,21 +9,16 @@ docs/_build
docs/build
docs/source/_static/rest-api
docs/source/rbac/scope-table.md
docs/source/reference/metrics.md
.ipynb_checkpoints
jsx/build/
# ignore config file at the top-level of the repo
# 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.min.css
share/jupyterhub/static/css/style.min.css.map
share/jupyterhub/static/js/admin-react.js*
*.egg-info
MANIFEST
.coverage

View File

@@ -8,50 +8,36 @@
# - Run on all files: pre-commit run --all-files
# - Register git hooks: pre-commit install --install-hooks
#
ci:
# pre-commit.ci will open PRs updating our hooks once a month
autoupdate_schedule: monthly
repos:
# Autoformat: Python code, syntax patterns are modernized
- repo: https://github.com/asottile/pyupgrade
rev: v3.3.1
rev: v2.32.1
hooks:
- id: pyupgrade
args:
- --py37-plus
- --py36-plus
# Autoformat: Python code
- repo: https://github.com/PyCQA/autoflake
rev: v2.0.1
- repo: https://github.com/asottile/reorder_python_imports
rev: v3.1.0
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: 5.12.0
hooks:
- id: isort
- id: reorder-python-imports
# Autoformat: Python code
- repo: https://github.com/psf/black
rev: 23.1.0
rev: 22.3.0
hooks:
- id: black
# Autoformat: markdown, yaml, javascript (see the file .prettierignore)
- repo: https://github.com/pre-commit/mirrors-prettier
rev: v3.0.0-alpha.4
rev: v2.6.2
hooks:
- id: prettier
# Autoformat and linting, misc. details
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.4.0
rev: v4.2.0
hooks:
- id: end-of-file-fixer
exclude: share/jupyterhub/static/js/admin-react.js
@@ -61,6 +47,6 @@ repos:
# Linting: Python code (see the file .flake8)
- repo: https://github.com/PyCQA/flake8
rev: "6.0.0"
rev: "4.0.1"
hooks:
- id: flake8

View File

@@ -1,3 +1,2 @@
share/jupyterhub/templates/
share/jupyterhub/static/js/admin-react.js
jupyterhub/singleuser/templates/

View File

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

View File

@@ -6,9 +6,134 @@ you can follow the [Jupyter contributor guide](https://jupyter.readthedocs.io/en
Make sure to also follow [Project Jupyter's Code of Conduct](https://github.com/jupyter/governance/blob/HEAD/conduct/code_of_conduct.md)
for a friendly and welcoming collaborative environment.
Please see our documentation on
## Setting up a development environment
- [Setting up a development install](https://jupyterhub.readthedocs.io/en/latest/contributing/setup.html)
- [Testing JupyterHub and linting code](https://jupyterhub.readthedocs.io/en/latest/contributing/tests.html)
<!--
https://jupyterhub.readthedocs.io/en/stable/contributing/setup.html
contains a lot of the same information. Should we merge the docs and
just have this page link to that one?
-->
If you need some help, feel free to ask on [Gitter](https://gitter.im/jupyterhub/jupyterhub) or [Discourse](https://discourse.jupyter.org/).
JupyterHub requires Python >= 3.5 and nodejs.
As a Python project, a development install of JupyterHub follows standard practices for the basics (steps 1-2).
1. clone the repo
```bash
git clone https://github.com/jupyterhub/jupyterhub
```
2. do a development install with pip
```bash
cd jupyterhub
python3 -m pip install --editable .
```
3. install the development requirements,
which include things like testing tools
```bash
python3 -m pip install -r dev-requirements.txt
```
4. install configurable-http-proxy with npm:
```bash
npm install -g configurable-http-proxy
```
5. set up pre-commit hooks for automatic code formatting, etc.
```bash
pre-commit install
```
You can also invoke the pre-commit hook manually at any time with
```bash
pre-commit run
```
## Contributing
JupyterHub has adopted automatic code formatting so you shouldn't
need to worry too much about your code style.
As long as your code is valid,
the pre-commit hook should take care of how it should look.
You can invoke the pre-commit hook by hand at any time with:
```bash
pre-commit run
```
which should run any autoformatting on your code
and tell you about any errors it couldn't fix automatically.
You may also install [black integration](https://github.com/psf/black#editor-integration)
into your text editor to format code automatically.
If you have already committed files before setting up the pre-commit
hook with `pre-commit install`, you can fix everything up using
`pre-commit run --all-files`. You need to make the fixing commit
yourself after that.
## Testing
It's a good idea to write tests to exercise any new features,
or that trigger any bugs that you have fixed to catch regressions.
You can run the tests with:
```bash
pytest -v
```
in the repo directory. If you want to just run certain tests,
check out the [pytest docs](https://pytest.readthedocs.io/en/latest/usage.html)
for how pytest can be called.
For instance, to test only spawner-related things in the REST API:
```bash
pytest -v -k spawn jupyterhub/tests/test_api.py
```
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)
To read more about fixtures check out the
[pytest docs](https://docs.pytest.org/en/latest/fixture.html)
for how to use the existing fixtures, and how to create new ones.
When in doubt, feel free to [ask](https://gitter.im/jupyterhub/jupyterhub).

View File

@@ -21,7 +21,7 @@
# your jupyterhub_config.py will be added automatically
# from your docker directory.
ARG BASE_IMAGE=ubuntu:22.04
ARG BASE_IMAGE=ubuntu:focal-20200729
FROM $BASE_IMAGE AS builder
USER root
@@ -35,14 +35,12 @@ RUN apt-get update \
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
RUN python3 -m pip install --upgrade setuptools pip wheel
# copy everything except whats in .dockerignore, its a
# compromise between needing to rebuild and maintaining
@@ -52,7 +50,7 @@ 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 setup.py bdist_wheel
RUN python3 -m pip wheel --wheel-dir wheelhouse dist/*.whl

View File

@@ -8,7 +8,6 @@ include *requirements.txt
include Dockerfile
graft onbuild
graft jsx
graft jupyterhub
graft scripts
graft share
@@ -19,10 +18,6 @@ graft ci
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

View File

@@ -8,6 +8,15 @@
---
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)
@@ -118,7 +127,7 @@ 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)
@@ -181,7 +190,7 @@ this a good choice for **testing JupyterHub on your desktop or laptop**.
If you want to run docker on a computer that has a public IP then you should
(as in MUST) **secure it with ssl** by adding ssl options to your docker
configuration or by using an ssl enabled proxy.
configuration or by using a ssl enabled proxy.
[Mounting volumes](https://docs.docker.com/engine/admin/volumes/volumes/) will
allow you to **store data outside the docker image (host system) so it will be persistent**, even when you start
@@ -230,9 +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

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

View File

@@ -1,35 +0,0 @@
#!/usr/bin/env python
# Check that installed package contains everything we expect
from pathlib import Path
import jupyterhub
from jupyterhub._data import DATA_FILES_PATH
print("Checking jupyterhub._data", end=" ")
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/page.html",
"static/css/style.min.css",
"static/components/jquery/dist/jquery.js",
"static/js/admin-react.js",
):
path = DATA_FILES_PATH / subpath
assert path.is_file(), path
print("OK")
print("Checking package_data", end=" ")
jupyterhub_path = Path(jupyterhub.__file__).parent.resolve()
for subpath in (
"alembic.ini",
"alembic/versions/833da8570507_rbac.py",
"event-schemas/server-actions/v1.yaml",
):
path = jupyterhub_path / subpath
assert path.is_file(), path
print("OK")

View File

@@ -1,27 +0,0 @@
#!/usr/bin/env python
# Check that sdist contains everything we expect
import sys
import tarfile
expected_files = [
"docs/requirements.txt",
"jsx/package.json",
"package.json",
"README.md",
]
assert len(sys.argv) == 2, "Expected one file"
print(f"Checking {sys.argv[1]}")
tar = tarfile.open(name=sys.argv[1], mode="r:gz")
try:
# Remove leading jupyterhub-VERSION/
filelist = {f.partition('/')[2] for f in tar.getnames()}
finally:
tar.close()
for e in expected_files:
assert e in filelist, f"{e} not found"
print("OK")

View File

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

View File

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

22
dev-requirements.txt Normal file
View File

@@ -0,0 +1,22 @@
-r requirements.txt
# temporary pin of attrs for jsonschema 0.3.0a1
# seems to be a pip bug
attrs>=17.4.0
beautifulsoup4
codecov
coverage
cryptography
html5lib # needed for beautifulsoup
jupyterlab >=3
mock
pre-commit
pytest>=3.3
pytest-asyncio; python_version < "3.7"
pytest-asyncio>=0.17; python_version >= "3.7"
pytest-cov
requests-mock
tbump
# blacklist urllib3 releases affected by https://github.com/urllib3/urllib3/issues/1683
# I *think* this should only affect testing, not production
urllib3!=1.25.4,!=1.25.5
virtualenv

View File

@@ -1,11 +1,9 @@
## 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
Dockerfile.alpine contains 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.
@@ -17,6 +15,6 @@ You will need:
- 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
- Use dummy authenticator for ease of testing. Update following in jupyterhub_config file
- c.JupyterHub.authenticator_class = 'dummyauthenticator.DummyAuthenticator'
- c.DummyAuthenticator.password = "your strong password"

View File

@@ -4,11 +4,6 @@ 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",
):
for sub_path in ("templates", "static/components", "static/css/style.min.css"):
path = os.path.join(DATA_FILES_PATH, sub_path)
assert os.path.exists(path), path

View File

@@ -1,62 +1,209 @@
# 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.
#
# Several sphinx-build commands can be used through this, for example:
#
# - make clean
# - make linkcheck
# - make spelling
#
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS)
metrics: source/reference/metrics.rst
# Manually added targets - related to code generation
# ----------------------------------------------------------------------------
# For local development:
# - builds the html
# - NOTE: If the pre-requisites for the html target is updated, also update the
# Read The Docs section in docs/source/conf.py.
#
html: metrics scopes
$(SPHINXBUILD) -b html "$(SOURCEDIR)" "$(BUILDDIR)/html" $(SPHINXOPTS)
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
metrics: source/reference/metrics.md
source/reference/metrics.md:
source/reference/metrics.rst: generate-metrics.py
python3 generate-metrics.py
scopes: source/rbac/scope-table.md
source/rbac/scope-table.md:
source/rbac/scope-table.md: source/rbac/generate-scope-table.py
python3 source/rbac/generate-scope-table.py
html: metrics scopes
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
# Manually added targets - related to development
# ----------------------------------------------------------------------------
dirhtml:
$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
# For local development:
# - requires sphinx-autobuild, see
# https://sphinxcontrib-spelling.readthedocs.io/en/latest/
# - builds and rebuilds html on changes to source, but does not re-generate
# metrics/scopes files
# - starts a livereload enabled webserver and opens up a browser
devenv: html
sphinx-autobuild -b html --open-browser "$(SOURCEDIR)" "$(BUILDDIR)/html"
singlehtml:
$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
@echo
@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
pickle:
$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
@echo
@echo "Build finished; now you can process the pickle files."
json:
$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
@echo
@echo "Build finished; now you can process the JSON files."
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 +1,8 @@
import os
from os.path import join
from pytablewriter import MarkdownTableWriter
from pytablewriter import RstSimpleTableWriter
from pytablewriter.style import Style
import jupyterhub.metrics
@@ -10,11 +12,12 @@ 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 +34,18 @@ 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

@@ -1,21 +1,12 @@
# 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 .
-r ../requirements.txt
alabaster_jupyterhub
autodoc-traits
jupyterhub-sphinx-theme
myst-parser>=0.19
myst-parser
pre-commit
pydata-sphinx-theme
pytablewriter>=0.56
ruamel.yaml
sphinx>=4
sphinx>=1.7
sphinx-copybutton
sphinx-jsonschema
sphinxext-opengraph
sphinxext-rediraffe

View File

@@ -6,7 +6,7 @@ info:
description: The REST API for JupyterHub
license:
name: BSD-3-Clause
version: 4.0.0b1
version: 2.3.0
servers:
- url: /hub/api
security:
@@ -139,16 +139,6 @@ paths:
If unspecified, use api_page_default_limit.
schema:
type: number
- name: include_stopped_servers
in: query
description: |
Include stopped servers in user model(s).
Added in JupyterHub 3.0.
Allows retrieval of information about stopped servers,
such as activity and state fields.
schema:
type: boolean
allowEmptyValue: true
responses:
200:
description: The Hub's user list
@@ -570,19 +560,7 @@ paths:
description: A note attached to the token for future bookkeeping
roles:
type: array
description: |
A list of role names from which to derive scopes.
This is a shortcut for assigning collections of scopes;
Tokens do not retain role assignment.
(Changed in 3.0: roles are immediately resolved to scopes
instead of stored on roles.)
items:
type: string
scopes:
type: array
description: |
A list of scopes that the token should have.
(new in JupyterHub 3.0).
description: A list of role names that the token should have
items:
type: string
required: false
@@ -815,39 +793,6 @@ paths:
- oauth2:
- groups
x-codegen-request-body-name: body
/groups/{name}/properties:
put:
summary: |
Set the group properties.
Added in JupyterHub 3.2.
parameters:
- name: name
in: path
description: group name
required: true
schema:
type: string
requestBody:
description: The new group properties, as a JSON dict.
content:
application/json:
schema:
type: object
required: true
responses:
200:
description: |
The properties have been updated.
The updated group model is returned.
content:
application/json:
schema:
$ref: "#/components/schemas/Group"
security:
- oauth2:
- groups
x-codegen-request-body-name: body
/services:
get:
summary: List services
@@ -1203,11 +1148,7 @@ components:
format: date-time
servers:
type: array
description: |
The servers for this user.
By default: only includes _active_ servers.
Changed in 3.0: if `?include_stopped_servers` parameter is specified,
stopped servers will be included as well.
description: The active servers for this user.
items:
$ref: "#/components/schemas/Server"
auth_state:
@@ -1229,15 +1170,6 @@ components:
description: |
Whether the server is ready for traffic.
Will always be false when any transition is pending.
stopped:
type: boolean
description: |
Whether the server is stopped. Added in JupyterHub 3.0,
and only useful when using the `?include_stopped_servers`
request parameter.
Now that stopped servers may be included (since JupyterHub 3.0),
this is the simplest way to select stopped servers.
Always equivalent to `not (ready or pending)`.
pending:
type: string
description: |
@@ -1322,15 +1254,6 @@ components:
description: The names of users who are members of this group
items:
type: string
properties:
type: object
description: |
Group properties (a dictionary).
Unused by JupyterHub itself,
but an extension point to store information about groups.
Added in JupyterHub 3.2.
roles:
type: array
description: The names of roles this group has
@@ -1391,16 +1314,7 @@ components:
description: The service that owns the token (undefined of owned by a user)
roles:
type: array
description:
Deprecated in JupyterHub 3, always an empty list. Tokens have
'scopes' starting from JupyterHub 3.
items:
type: string
scopes:
type: array
description:
List of scopes this token has been assigned. New in JupyterHub
3. In JupyterHub 2.x, tokens were assigned 'roles' insead of scopes.
description: The names of roles this token has
items:
type: string
note:
@@ -1456,9 +1370,6 @@ components:
inherit:
Everything that the token-owning entity can access _(metascope
for tokens)_
admin-ui:
Access the admin page. Permission to take actions via the admin
page granted separately.
admin:users:
Read, write, create and delete users and their authentication
state, not including their servers or tokens.

View File

@@ -1,33 +1,33 @@
# Interpreting common log messages
When debugging errors and outages, looking at the logs emitted by
JupyterHub is very helpful. This document intends to describe some common
log messages, what they mean and what are the most common causes that generated them, as well as some possible ways to fix them.
JupyterHub is very helpful. This document tries to document some common
log messages, and what they mean.
## Failing suspected API request to not-running server
### Example
Your logs might be littered with lines that look scary
Your logs might be littered with lines that might look slightly scary
```
[W 2022-03-10 17:25:19.774 JupyterHub base:1349] Failing suspected API request to not-running server: /hub/user/<user-name>/api/metrics/v1
```
### Cause
This likely means that the user's server has stopped running but they
still have a browser tab open. For example, you might have 3 tabs open and you shut
the server down via one.
Another possible reason could be that you closed your laptop and the server was culled for inactivity, then reopened the laptop!
However, the client-side code (JupyterLab, Classic Notebook, etc) doesn't interpret the shut-down server and continues to make some API requests.
### Most likely cause
This likely means is that the user's server has stopped running but they
still have a browser tab open. For example, you might have 3 tabs open, and shut
your server down via one. Or you closed your laptop, your server was
culled for inactivity, and then you reopen your laptop again! The
client side code (JupyterLab, Classic Notebook, etc) does not know
yet that the server is dead, and continues to make some API requests.
JupyterHub's architecture means that the proxy routes all requests that
don't go to a running user server to the hub process itself. The hub
process then explicitly returns a failure response, so the client knows
that the server is not running anymore. This is used by JupyterLab to
inform the user that the server is not running anymore, and provide an option
to restart it.
tell you your server is not running anymore, and offer you the option
to let you restart it.
Most commonly, you'll see this in reference to the `/api/metrics/v1`
URL, used by [jupyter-resource-usage](https://github.com/jupyter-server/jupyter-resource-usage).
@@ -47,9 +47,9 @@ This log message is benign, and there is usually no action for you to take.
### Cause
JupyterHub requires the `jupyterhub` python package installed inside the image or
environment, the user server starts in. This message indicates that the version of
environment the user server starts in. This message indicates that the version of
the `jupyterhub` package installed inside the user image or environment is not
the same as the JupyterHub server's version itself. This is not necessarily always a
the same version as the JupyterHub server itself. This is not necessarily always a
problem - some version drift is mostly acceptable, and the only two known cases of
breakage are across the 0.7 and 2.0 version releases. In those cases, issues pop
up immediately after upgrading your version of JupyterHub, so **always check the JupyterHub
@@ -67,6 +67,6 @@ aligned, rather than as an indicator of an existing problem.
### Actions you can take
Upgrade the version of the `jupyterhub` package in your user environment or image
so that it matches the version of JupyterHub running your JupyterHub server! If you
so it matches the version of JupyterHub running your JupyterHub server! If you
are using the [zero-to-jupyterhub](https://z2jh.jupyter.org) helm chart, you can find the appropriate
version of the `jupyterhub` package to install in your user image [here](https://jupyterhub.github.io/helm-chart/)

View File

@@ -0,0 +1,157 @@
====================
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
document is if you have set up your own JupyterHub without using a
distribution.
It is long because is pretty detailed! Most likely, upgrading
JupyterHub is painless, quick and with minimal user interruption.
Read the Changelog
==================
The `changelog <../changelog.html>`_ contains information on what has
changed with the new JupyterHub release, and any deprecation warnings.
Read these notes to familiarize yourself with the coming changes. There
might be new releases of authenticators & spawners you are using, so
read the changelogs for those too!
Notify your users
=================
If you are using the default configuration where ``configurable-http-proxy``
is managed by JupyterHub, your users will see service disruption during
the upgrade process. You should notify them, and pick a time to do the
upgrade where they will be least disrupted.
If you are using a different proxy, or running ``configurable-http-proxy``
independent of JupyterHub, your users will be able to continue using notebook
servers they had already launched, but will not be able to launch new servers
nor sign in.
Backup database & config
========================
Before doing an upgrade, it is critical to back up:
#. Your JupyterHub database (sqlite by default, or MySQL / Postgres
if you used those). If you are using sqlite (the default), you
should backup the ``jupyterhub.sqlite`` file.
#. Your ``jupyterhub_config.py`` file.
#. Your user's home directories. This is unlikely to be affected directly by
a JupyterHub upgrade, but we recommend a backup since user data is very
critical.
Shutdown JupyterHub
===================
Shutdown the JupyterHub process. This would vary depending on how you
have set up JupyterHub to run. Most likely, it is using a process
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*, which is where the JupyterHub server process
runs. This is started with the ``jupyterhub`` command, and is what
people generally think of as JupyterHub.
#. The *notebook user environments*. This is where the user notebook
servers are launched from, and is probably custom to your own
installation. This could be just one environment (different from the
hub environment) that is shared by all users, one environment
per user, or same environment as the hub environment. The hub
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>
Where ``<version>`` is the version of JupyterHub you are upgrading to.
You should also check for new releases of the authenticator & spawner you
are using. You might wish to upgrade those packages too along with JupyterHub,
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 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 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 config file, or login using an external
authentication provider (Google, GitHub, LDAP, etc)
- user servers are stopped during upgrade
- don't mind causing users to login again after upgrade
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
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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
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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
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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
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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

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@@ -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

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@@ -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
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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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@@ -1,79 +1,70 @@
# Configuration file for Sphinx to build our documentation to HTML.
#
# Configuration reference: https://www.sphinx-doc.org/en/master/usage/configuration.html
#
import contextlib
import datetime
import io
import os
import subprocess
import sys
from docutils import nodes
from sphinx.directives.other import SphinxDirective
# Set paths
sys.path.insert(0, os.path.abspath('.'))
# -- General configuration ------------------------------------------------
# Minimal Sphinx version
needs_sphinx = '1.4'
# Sphinx extension modules
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.intersphinx',
'sphinx.ext.napoleon',
'autodoc_traits',
'sphinx_copybutton',
'sphinx-jsonschema',
'myst_parser',
]
myst_heading_anchors = 2
myst_enable_extensions = [
'colon_fence',
'deflist',
]
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = 'JupyterHub'
copyright = '2016, Project Jupyter team'
author = 'Project Jupyter team'
# Autopopulate version
from os.path import dirname
docs = dirname(dirname(__file__))
root = dirname(docs)
sys.path.insert(0, root)
import jupyterhub
# The short X.Y version.
version = '%i.%i' % jupyterhub.version_info[:2]
# The full version, including alpha/beta/rc tags.
release = jupyterhub.__version__
language = None
exclude_patterns = []
pygments_style = 'sphinx'
todo_include_todos = False
# Set the default role so we can use `foo` instead of ``foo``
default_role = 'literal'
# -- Config -------------------------------------------------------------
from jupyterhub.app import JupyterHub
from docutils import nodes
from sphinx.directives.other import SphinxDirective
from contextlib import redirect_stdout
from io import StringIO
# -- Project information -----------------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
#
project = "JupyterHub"
author = "Project Jupyter Contributors"
copyright = f"{datetime.date.today().year}, {author}"
# -- General Sphinx configuration --------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
#
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.intersphinx",
"sphinx.ext.napoleon",
"autodoc_traits",
"sphinx_copybutton",
"sphinx-jsonschema",
"sphinxext.opengraph",
"sphinxext.rediraffe",
"jupyterhub_sphinx_theme",
"myst_parser",
]
root_doc = "index"
source_suffix = [".md"]
# default_role let's use use `foo` instead of ``foo`` in rST
default_role = "literal"
# -- MyST configuration ------------------------------------------------------
# ref: https://myst-parser.readthedocs.io/en/latest/configuration.html
#
myst_heading_anchors = 2
myst_enable_extensions = [
# available extensions: https://myst-parser.readthedocs.io/en/latest/syntax/optional.html
"colon_fence",
"deflist",
"fieldlist",
"substitution",
]
myst_substitutions = {
# date example: Dev 07, 2022
"date": datetime.date.today().strftime("%b %d, %Y").title(),
"version": jupyterhub.__version__,
}
# -- Custom directives to generate documentation -----------------------------
# ref: https://myst-parser.readthedocs.io/en/latest/syntax/roles-and-directives.html
#
# We define custom directives to help us generate documentation using Python on
# demand when referenced from our documentation files.
#
# Create a temp instance of JupyterHub for use by two separate directive classes
# to get the output from using the "--generate-config" and "--help-all" CLI
# flags respectively.
#
# create a temp instance of JupyterHub just to get the output of the generate-config
# and help --all commands.
jupyterhub_app = JupyterHub()
@@ -90,8 +81,8 @@ class ConfigDirective(SphinxDirective):
# The generated configuration file for this version
generated_config = jupyterhub_app.generate_config_file()
# post-process output
home_dir = os.environ["HOME"]
generated_config = generated_config.replace(home_dir, "$HOME", 1)
home_dir = os.environ['HOME']
generated_config = generated_config.replace(home_dir, '$HOME', 1)
par = nodes.literal_block(text=generated_config)
return [par]
@@ -107,134 +98,160 @@ class HelpAllDirective(SphinxDirective):
def run(self):
# The output of the help command for this version
buffer = io.StringIO()
with contextlib.redirect_stdout(buffer):
jupyterhub_app.print_help("--help-all")
buffer = StringIO()
with redirect_stdout(buffer):
jupyterhub_app.print_help('--help-all')
all_help = buffer.getvalue()
# post-process output
home_dir = os.environ["HOME"]
all_help = all_help.replace(home_dir, "$HOME", 1)
home_dir = os.environ['HOME']
all_help = all_help.replace(home_dir, '$HOME', 1)
par = nodes.literal_block(text=all_help)
return [par]
def setup(app):
app.add_css_file("custom.css")
app.add_directive("jupyterhub-generate-config", ConfigDirective)
app.add_directive("jupyterhub-help-all", HelpAllDirective)
app.add_css_file('custom.css')
app.add_directive('jupyterhub-generate-config', ConfigDirective)
app.add_directive('jupyterhub-help-all', HelpAllDirective)
# -- Read The Docs -----------------------------------------------------------
#
# Since RTD runs sphinx-build directly without running "make html", we run the
# pre-requisite steps for "make html" from here if needed.
#
if os.environ.get("READTHEDOCS"):
docs = os.path.dirname(os.path.dirname(__file__))
subprocess.check_call(["make", "metrics", "scopes"], cwd=docs)
source_suffix = ['.rst', '.md']
# source_encoding = 'utf-8-sig'
# -- Options for HTML output ----------------------------------------------
# -- Spell checking ----------------------------------------------------------
# ref: https://sphinxcontrib-spelling.readthedocs.io/en/latest/customize.html#configuration-options
#
# The "sphinxcontrib.spelling" extension is optionally enabled if its available.
#
try:
import sphinxcontrib.spelling # noqa
except ImportError:
pass
else:
extensions.append("sphinxcontrib.spelling")
spelling_word_list_filename = "spelling_wordlist.txt"
# The theme to use for HTML and HTML Help pages.
html_theme = 'pydata_sphinx_theme'
html_logo = '_static/images/logo/logo.png'
html_favicon = '_static/images/logo/favicon.ico'
# -- Options for HTML output -------------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
#
html_logo = "_static/images/logo/logo.png"
html_favicon = "_static/images/logo/favicon.ico"
html_static_path = ["_static"]
# Paths that contain custom static files (such as style sheets)
html_static_path = ['_static']
htmlhelp_basename = 'JupyterHubdoc'
html_theme = "jupyterhub_sphinx_theme"
html_theme_options = {
"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,
"navbar_align": "left",
}
html_context = {
"github_user": "jupyterhub",
"github_repo": "jupyterhub",
"github_version": "main",
"doc_path": "docs/source",
"doc_path": "docs",
}
# -- Options for LaTeX output ---------------------------------------------
# -- Options for linkcheck builder -------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-the-linkcheck-builder
#
linkcheck_ignore = [
r"(.*)github\.com(.*)#", # javascript based anchors
r"(.*)/#%21(.*)/(.*)", # /#!forum/jupyter - encoded anchor edge case
r"https://github.com/[^/]*$", # too many github usernames / searches in changelog
"https://github.com/jupyterhub/jupyterhub/pull/", # too many PRs in changelog
"https://github.com/jupyterhub/jupyterhub/compare/", # too many comparisons in changelog
r"https?://(localhost|127.0.0.1).*", # ignore localhost references in auto-links
r"https://jupyter.chameleoncloud.org", # FIXME: ignore (presumably) short-term SSL issue
]
linkcheck_anchors_ignore = [
"/#!",
"/#%21",
latex_elements = {
# 'papersize': 'letterpaper',
# 'pointsize': '10pt',
# 'preamble': '',
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(
master_doc,
'JupyterHub.tex',
'JupyterHub Documentation',
'Project Jupyter team',
'manual',
)
]
# -- Intersphinx -------------------------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/extensions/intersphinx.html#configuration
#
# latex_logo = None
# latex_use_parts = False
# latex_show_pagerefs = False
# latex_show_urls = False
# latex_appendices = []
# latex_domain_indices = True
# -- manual page output -------------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [(master_doc, 'jupyterhub', 'JupyterHub Documentation', [author], 1)]
# man_show_urls = False
# -- Texinfo output -----------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(
master_doc,
'JupyterHub',
'JupyterHub Documentation',
author,
'JupyterHub',
'One line description of project.',
'Miscellaneous',
)
]
# texinfo_appendices = []
# texinfo_domain_indices = True
# texinfo_show_urls = 'footnote'
# texinfo_no_detailmenu = False
# -- Epub output --------------------------------------------------------
# Bibliographic Dublin Core info.
epub_title = project
epub_author = author
epub_publisher = author
epub_copyright = copyright
# A list of files that should not be packed into the epub file.
epub_exclude_files = ['search.html']
# -- Intersphinx ----------------------------------------------------------
intersphinx_mapping = {
"python": ("https://docs.python.org/3/", None),
"tornado": ("https://www.tornadoweb.org/en/stable/", None),
"jupyter-server": ("https://jupyter-server.readthedocs.io/en/stable/", None),
'python': ('https://docs.python.org/3/', None),
'tornado': ('https://www.tornadoweb.org/en/stable/', None),
}
# -- Options for the opengraph extension -------------------------------------
# ref: https://github.com/wpilibsuite/sphinxext-opengraph#options
#
# ogp_site_url is set automatically by RTD
ogp_image = "_static/logo.png"
ogp_use_first_image = True
# -- Read The Docs --------------------------------------------------------
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
if on_rtd:
# readthedocs.org uses their theme by default, so no need to specify it
# build both metrics and rest-api, since RTD doesn't run make
from subprocess import check_call as sh
# -- 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
# 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 = "main"
rediraffe_redirects = "redirects.txt"
# rediraffe_redirects = {
# "old-file": "new-folder/new-file-name",
# }
sh(['make', 'metrics', 'scopes'], cwd=docs)
# -- Spell checking -------------------------------------------------------
try:
import sphinxcontrib.spelling
except ImportError:
pass
else:
extensions.append("sphinxcontrib.spelling")
spelling_word_list_filename = 'spelling_wordlist.txt'

View File

@@ -1,27 +0,0 @@
# Community communication channels
We use different channels of communication for different purposes. Whichever one you use will depend on what kind of communication you want to engage in.
## Discourse (recommended)
We use [Discourse](https://discourse.jupyter.org) for online discussions and support questions.
You can ask questions here if you are a first-time contributor to the JupyterHub project.
Everyone in the Jupyter community is welcome to bring ideas and questions there.
We recommend that you first use our Discourse as all past and current discussions on it are archived and searchable. Thus, all discussions remain useful and accessible to the whole community.
## Gitter
We use [our Gitter channel](https://gitter.im/jupyterhub/jupyterhub) for online, real-time text chat; a place for more ephemeral discussions. When you're not on Discourse, you can stop here to have other discussions on the fly.
## Github Issues
[Github issues](https://docs.github.com/en/issues/tracking-your-work-with-issues/about-issues) are used for most long-form project discussions, bug reports and feature requests.
- Issues related to a specific authenticator or spawner should be opened in the appropriate repository for the authenticator or spawner.
- If you are using a specific JupyterHub distribution (such as [Zero to JupyterHub on Kubernetes](https://github.com/jupyterhub/zero-to-jupyterhub-k8s) or [The Littlest JupyterHub](https://github.com/jupyterhub/the-littlest-jupyterhub/)), you should open issues directly in their repository.
- If you cannot find a repository to open your issue in, do not worry! Open the issue in the [main JupyterHub repository](https://github.com/jupyterhub/jupyterhub/) and our community will help you figure it out.
```{note}
Our community is distributed across the world in various timezones, so please be patient if you do not get a response immediately!
```

View File

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

View File

@@ -1,76 +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.
## Building documentation locally
We use [sphinx](https://www.sphinx-doc.org) to build our documentation. It takes
our documentation source files (written in [markdown](https://daringfireball.net/projects/markdown/) or [reStructuredText](https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html) &
stored under the `docs/source` directory) and converts it into various
formats for people to read. To make sure the documentation you write or
change renders correctly, it is good practice to test it locally.
1. Make sure you have successfully completed {ref}`contributing/setup`.
2. Install the packages required to build the docs.
```bash
python3 -m pip install -r docs/requirements.txt
```
3. Build the html version of the docs. This is the most commonly used
output format, so verifying it renders correctly is usually good
enough.
```bash
cd docs
make html
```
This step will display any syntax or formatting errors in the documentation,
along with the filename / line number in which they occurred. Fix them,
and re-run the `make html` command to re-render the documentation.
4. View the rendered documentation by opening `_build/html/index.html` in
a web browser.
:::{tip}
**On Windows**, you can open a file from the terminal with `start <path-to-file>`.
**On macOS**, you can do the same with `open <path-to-file>`.
**On Linux**, you can do the same with `xdg-open <path-to-file>`.
After opening index.html in your browser you can just refresh the page whenever
you rebuild the docs via `make html`
:::
(contributing-docs-conventions)=
## Documentation conventions
This section lists various conventions we use in our documentation. This is a
living document that grows over time, so feel free to add to it / change it!
Our entire documentation does not yet fully conform to these conventions yet,
so help in making it so would be appreciated!
### `pip` invocation
There are many ways to invoke a `pip` command, we recommend the following
approach:
```bash
python3 -m pip
```
This invokes pip explicitly using the python3 binary that you are
currently using. This is the **recommended way** to invoke pip
in our documentation, since it is least likely to cause problems
with python3 and pip being from different environments.
For more information on how to invoke `pip` commands, see
[the pip documentation](https://pip.pypa.io/en/stable/).

View File

@@ -0,0 +1,78 @@
.. _contributing/docs:
==========================
Contributing Documentation
==========================
Documentation is often more important than code. This page helps
you get set up on how to contribute documentation to JupyterHub.
Building documentation locally
==============================
We use `sphinx <http://sphinx-doc.org>`_ to build our documentation. It takes
our documentation source files (written in `markdown
<https://daringfireball.net/projects/markdown/>`_ or `reStructuredText
<https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html>`_ &
stored under the ``docs/source`` directory) and converts it into various
formats for people to read. To make sure the documentation you write or
change renders correctly, it is good practice to test it locally.
#. Make sure you have successfuly completed :ref:`contributing/setup`.
#. Install the packages required to build the docs.
.. code-block:: bash
python3 -m pip install -r docs/requirements.txt
#. Build the html version of the docs. This is the most commonly used
output format, so verifying it renders as you should is usually good
enough.
.. code-block:: bash
cd docs
make html
This step will display any syntax or formatting errors in the documentation,
along with the filename / line number in which they occurred. Fix them,
and re-run the ``make html`` command to re-render the documentation.
#. View the rendered documentation by opening ``build/html/index.html`` in
a web browser.
.. tip::
On macOS, you can open a file from the terminal with ``open <path-to-file>``.
On Linux, you can do the same with ``xdg-open <path-to-file>``.
.. _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

@@ -1,22 +0,0 @@
# Contributing
We want you to contribute to JupyterHub in ways that are most exciting
and useful to you. We value documentation, testing, bug reporting & 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

@@ -4,7 +4,7 @@ This roadmap collects "next steps" for JupyterHub. It is about creating a
shared understanding of the project's vision and direction amongst
the community of users, contributors, and maintainers.
The goal is to communicate priorities and upcoming release plans.
It is not aimed at limiting contributions to what is listed here.
It is not a aimed at limiting contributions to what is listed here.
## Using the roadmap

View File

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

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

@@ -1,175 +0,0 @@
(contributing/setup)=
# Setting up a development install
## System requirements
JupyterHub can only run on macOS or Linux operating systems. If you are
using Windows, we recommend using [VirtualBox](https://virtualbox.org)
or a similar system to run [Ubuntu Linux](https://ubuntu.com) for
development.
### Install Python
JupyterHub is written in the [Python](https://python.org) programming language and
requires you have at least version 3.6 installed locally. If you havent
installed Python before, the recommended way to install it is to use
[Miniforge](https://github.com/conda-forge/miniforge#download).
### Install nodejs
[NodeJS 12+](https://nodejs.org/en/) is required for building some JavaScript components.
`configurable-http-proxy`, the default proxy implementation for JupyterHub, is written in Javascript.
If you have not installed NodeJS before, we recommend installing it in the `miniconda` environment you set up for Python.
You can do so with `conda install nodejs`.
Many in the Jupyter community use \[`nvm`\](<https://github.com/nvm-sh/nvm>) to
managing node dependencies.
### Install git
JupyterHub uses [Git](https://git-scm.com) & [GitHub](https://github.com)
for development & collaboration. You need to [install git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) to work on
JupyterHub. We also recommend getting a free account on GitHub.com.
## Setting up a development install
When developing JupyterHub, you would need to make changes and be able to instantly view the results of the changes. To achieve that, a developer install is required.
:::{note}
This guide does not attempt to dictate _how_ development
environments should be isolated since that is a personal preference and can
be achieved in many ways, for example, `tox`, `conda`, `docker`, etc. See this
[forum thread](https://discourse.jupyter.org/t/thoughts-on-using-tox/3497) for
a more detailed discussion.
:::
1. Clone the [JupyterHub git repository](https://github.com/jupyterhub/jupyterhub)
to your computer.
```bash
git clone https://github.com/jupyterhub/jupyterhub
cd jupyterhub
```
2. Make sure the `python` you installed and the `npm` you installed
are available to you on the command line.
```bash
python -V
```
This should return a version number greater than or equal to 3.6.
```bash
npm -v
```
This should return a version number greater than or equal to 5.0.
3. Install `configurable-http-proxy` (required to run and test the default JupyterHub configuration) and `yarn` (required to build some components):
```bash
npm install -g configurable-http-proxy yarn
```
If you get an error that says `Error: EACCES: permission denied`, you might need to prefix the command with `sudo`.
`sudo` may be required to perform a system-wide install.
If you do not have access to sudo, you may instead run the following commands:
```bash
npm install configurable-http-proxy yarn
export PATH=$PATH:$(pwd)/node_modules/.bin
```
The second line needs to be run every time you open a new terminal.
If you are using conda you can instead run:
```bash
conda install configurable-http-proxy yarn
```
4. Install an editable version of JupyterHub and its requirements for
development and testing. This lets you edit JupyterHub code in a text editor
& restart the JupyterHub process to see your code changes immediately.
```bash
python3 -m pip install --editable ".[test]"
```
5. Set up a database.
The default database engine is `sqlite` so if you are just trying
to get up and running quickly for local development that should be
available via [Python](https://docs.python.org/3.5/library/sqlite3.html).
See [The Hub's Database](hub-database) for details on other supported databases.
6. You are now ready to start JupyterHub!
```bash
jupyterhub
```
7. You can access JupyterHub from your browser at
`http://localhost:8000` now.
Happy developing!
## Using DummyAuthenticator & SimpleLocalProcessSpawner
To simplify testing of JupyterHub, it is helpful to use
{class}`~jupyterhub.auth.DummyAuthenticator` instead of the default JupyterHub
authenticator and SimpleLocalProcessSpawner instead of the default spawner.
There is a sample configuration file that does this in
`testing/jupyterhub_config.py`. To launch JupyterHub with this
configuration:
```bash
jupyterhub -f testing/jupyterhub_config.py
```
The default JupyterHub [authenticator](https://jupyterhub.readthedocs.io/en/stable/reference/authenticators.html#the-default-pam-authenticator)
& [spawner](https://jupyterhub.readthedocs.io/en/stable/api/spawner.html#localprocessspawner)
require your system to have user accounts for each user you want to log in to
JupyterHub as.
DummyAuthenticator allows you to log in with any username & password,
while SimpleLocalProcessSpawner allows you to start servers without having to
create a Unix user for each JupyterHub user. Together, these make it
much easier to test JupyterHub.
Tip: If you are working on parts of JupyterHub that are common to all
authenticators & spawners, we recommend using both DummyAuthenticator &
SimpleLocalProcessSpawner. If you are working on just authenticator-related
parts, use only SimpleLocalProcessSpawner. Similarly, if you are working on
just spawner-related parts, use only DummyAuthenticator.
## Troubleshooting
This section lists common ways setting up your development environment may
fail, and how to fix them. Please add to the list if you encounter yet
another way it can fail!
### `lessc` not found
If the `python3 -m pip install --editable .` command fails and complains about
`lessc` being unavailable, you may need to explicitly install some
additional JavaScript dependencies:
```bash
npm install
```
This will fetch client-side JavaScript dependencies necessary to compile
CSS.
You may also need to manually update JavaScript and CSS after some
development updates, with:
```bash
python3 setup.py js # fetch updated client-side js
python3 setup.py css # recompile CSS from LESS sources
python3 setup.py jsx # build React admin app
```

View File

@@ -0,0 +1,188 @@
.. _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 3.5 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
--------------
``configurable-http-proxy``, the default proxy implementation for
JupyterHub, is written in Javascript to run on `NodeJS
<https://nodejs.org/en/>`_. If you have not installed nodejs before, we
recommend installing it in the ``miniconda`` environment you set up for
Python. You can do so with ``conda install nodejs``.
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 need to make changes to the code & see
their effects quickly. You need to do a developer install to make that
happen.
.. note:: This guide does not attempt to dictate *how* development
environements 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 3.5.
.. code:: bash
npm -v
This should return a version number greater than or equal to 5.0.
3. Install ``configurable-http-proxy``. This is required to run
JupyterHub.
.. code:: 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``. If you do not
have access to sudo, you may instead run the following commands:
.. code:: 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.
4. Install the python packages required for JupyterHub development.
.. code:: bash
python3 -m pip install -r dev-requirements.txt
python3 -m pip install -r requirements.txt
5. Setup a database.
The default database engine is ``sqlite`` so if you are just trying
to get up and running quickly for local development that should be
available via `python <https://docs.python.org/3.5/library/sqlite3.html>`__.
See :doc:`/reference/database` for details on other supported databases.
6. Install the development version of JupyterHub. This lets you edit
JupyterHub code in a text editor & restart the JupyterHub process to
see your code changes immediately.
.. code:: bash
python3 -m pip install --editable .
7. You are now ready to start JupyterHub!
.. code:: bash
jupyterhub
8. You can access JupyterHub from your browser at
``http://localhost:8000`` now.
Happy developing!
Using DummyAuthenticator & SimpleLocalProcessSpawner
====================================================
To simplify testing of JupyterHub, its 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

View File

@@ -1,157 +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.
## Running the tests
1. Make sure you have completed {ref}`contributing/setup`.
Once you are done, you would be able to run `jupyterhub` from the command line and access it from your web browser.
This ensures that the dev environment is properly set up for tests to run.
2. You can run all tests in JupyterHub
```bash
pytest -v jupyterhub/tests
```
This should display progress as it runs all the tests, printing
information about any test failures as they occur.
If you wish to confirm test coverage the run tests with the `--cov` flag:
```bash
pytest -v --cov=jupyterhub jupyterhub/tests
```
3. You can also run tests in just a specific file:
```bash
pytest -v jupyterhub/tests/<test-file-name>
```
4. To run a specific test only, you can do:
```bash
pytest -v jupyterhub/tests/<test-file-name>::<test-name>
```
This runs the test with function name `<test-name>` defined in
`<test-file-name>`. This is very useful when you are iteratively
developing a single test.
For example, to run the test `test_shutdown` in the file `test_api.py`,
you would run:
```bash
pytest -v jupyterhub/tests/test_api.py::test_shutdown
```
For more details, refer to the [pytest usage documentation](https://pytest.readthedocs.io/en/latest/usage.html).
## Test organisation
The tests live in `jupyterhub/tests` and are organized roughly into:
1. `test_api.py` tests the REST API
2. `test_pages.py` tests loading the HTML pages
and other collections of tests for different components.
When writing a new test, there should usually be a test of
similar functionality already written and related tests should
be added nearby.
The fixtures live in `jupyterhub/tests/conftest.py`. There are
fixtures that can be used for JupyterHub components, such as:
- `app`: an instance of JupyterHub with mocked parts
- `auth_state_enabled`: enables persisting auth_state (like authentication tokens)
- `db`: a sqlite in-memory DB session
- `` io_loop` ``: a Tornado event loop
- `event_loop`: a new asyncio event loop
- `user`: creates a new temporary user
- `admin_user`: creates a new temporary admin user
- single user servers
\- `cleanup_after`: allows cleanup of single user servers between tests
- mocked service
\- `MockServiceSpawner`: a spawner that mocks services for testing with a short poll interval
\- `` mockservice` ``: mocked service with no external service url
\- `mockservice_url`: mocked service with a url to test external services
And fixtures to add functionality or spawning behavior:
- `admin_access`: grants admin access
- `` no_patience` ``: sets slow-spawning timeouts to zero
- `slow_spawn`: enables the SlowSpawner (a spawner that takes a few seconds to start)
- `never_spawn`: enables the NeverSpawner (a spawner that will never start)
- `bad_spawn`: enables the BadSpawner (a spawner that fails immediately)
- `slow_bad_spawn`: enables the SlowBadSpawner (a spawner that fails after a short delay)
Refer to the [pytest fixtures documentation](https://pytest.readthedocs.io/en/latest/fixture.html) to learn how to use fixtures that exists already and to create new ones.
### 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.

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@@ -0,0 +1,68 @@
.. _contributing/tests:
==================
Testing JupyterHub
==================
Unit test help validate that JupyterHub works the way we think it does,
and continues to do so when changes occur. They also help communicate
precisely what we expect our code to do.
JupyterHub uses `pytest <https://pytest.org>`_ for all our tests. You
can find them under ``jupyterhub/tests`` directory in the git repository.
Running the tests
==================
#. Make sure you have completed :ref:`contributing/setup`. You should be able
to start ``jupyterhub`` from the commandline & access it from your
web browser. This ensures that the dev environment is properly set
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
Troubleshooting Test Failures
=============================
All the tests are failing
-------------------------
Make sure you have completed all the steps in :ref:`contributing/setup` successfully, and
can launch ``jupyterhub`` from the terminal.

View File

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

View File

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

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

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

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(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 /users](jupyterhub-rest-API) with `?state=active`,
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 well tested with JupyterHub.
[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) and ubiquitous (it is in the Python standard library).
It works very well for testing, small deployments, and workshops.
For production systems, SQLite has some disadvantages when used with JupyterHub:
- `upgrade-db` may not always work, and you may need to start with a fresh database
- `downgrade-db` **will not** work if you want to rollback to an earlier
version, so backup the `jupyterhub.sqlite` file before upgrading
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).
### Picking your database backend (PostgreSQL, MySQL)
When running a long term deployment or a production system, we recommend using a full-fledged relational database, such as [PostgreSQL](https://www.postgresql.org) or [MySQL](https://www.mysql.com), that supports the SQL `ALTER TABLE` statement.
## Notes and Tips
### 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.
### MySQL / MariaDB
- You should use the `pymysql` sqlalchemy provider (the other one, MySQLdb,
isn't available for py3).
- You also need to set `pool_recycle` to some value (typically 60 - 300)
which depends on your MySQL setup. This is necessary since MySQL kills
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.

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# 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
capacity-planning
database
websecurity
oauth
singleuser
../rbac/index
```

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@@ -1,109 +0,0 @@
(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`
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`.
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.

View File

@@ -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**
@@ -22,13 +20,13 @@ Please submit pull requests to update information or to add new institutions or
- [GitHub organization](https://github.com/data-8)
- [NERSC](https://www.nersc.gov/)
- [NERSC](http://www.nersc.gov/)
- [Press release on Jupyter and Cori](https://www.nersc.gov/news-publications/nersc-news/nersc-center-news/2016/jupyter-notebooks-will-open-up-new-possibilities-on-nerscs-cori-supercomputer/)
- [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](https://research-it.berkeley.edu)
- [JupyterHub server supports campus research computation](https://research-it.berkeley.edu/blog/17/01/24/free-fully-loaded-jupyterhub-server-supports-campus-research-computation)
- [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
@@ -73,21 +71,25 @@ easy to do with RStudio too.
### 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)
### University of Colorado Boulder
- (CU Research Computing) CURC
- [JupyterHub User Guide](https://curc.readthedocs.io/en/latest/gateways/jupyterhub.html)
- [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://curc.readthedocs.io/en/latest/gateways/parallel-programming-jupyter.html)
- [Parallel Processing with JupyterHub tutorial](https://www.rc.colorado.edu/support/examples-and-tutorials/parallel-processing-with-jupyterhub.html)
- [Parallel Programming with JupyterHub document](https://www.rc.colorado.edu/book/export/html/833)
- Earth Lab at CU
- [Tutorial on Parallel R on JupyterHub](https://earthdatascience.org/tutorials/parallel-r-on-jupyterhub/)
### 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
@@ -95,11 +97,11 @@ easy to do with RStudio too.
### University of Illinois
- https://datascience.business.illinois.edu (currently down; checked 10/26/22)
- https://datascience.business.illinois.edu (currently down; checked 04/26/19)
### 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
@@ -119,12 +121,16 @@ easy to do with RStudio too.
### Paderborn University
- [Data Science (DICE) group](https://dice-research.org)
- [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://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"
- [Press release](https://news.psu.edu/story/523093/2018/05/24/new-open-source-web-apps-available-students-and-faculty): "New open-source web apps available for students and faculty" (but Hub is currently down; checked 04/26/19)
### University of Rochester CIRC
- [JupyterHub Userguide](https://info.circ.rochester.edu/Web_Applications/JupyterHub.html) - Slurm, beehive
### University of California San Diego
@@ -138,7 +144,7 @@ easy to do with RStudio too.
- [Sample deployment of Jupyterhub in HPC on SDSC Comet](https://zonca.github.io/2017/02/sample-deployment-jupyterhub-hpc.html)
- Educational Technology Services - Paul Jamason
- [datahub.ucsd.edu](https://datahub.ucsd.edu)
- [jupyterhub.ucsd.edu](https://jupyterhub.ucsd.edu)
### TACC University of Texas
@@ -150,13 +156,13 @@ easy to do with RStudio too.
### Elucidata
- What's new in Jupyter Notebooks @[Elucidata](https://elucidata.io/):
- [Using Jupyter Notebooks with Jupyterhub on GCP, managed by GKE](https://medium.com/elucidata/why-you-should-be-using-a-jupyter-notebook-8385a4ccd93d)
- Using Jupyter Notebooks with Jupyterhub on GCP, managed by GKE - https://medium.com/elucidata/why-you-should-be-using-a-jupyter-notebook-8385a4ccd93d
## Service Providers
### AWS
- [Run Jupyter Notebook and JupyterHub on Amazon EMR](https://aws.amazon.com/blogs/big-data/running-jupyter-notebook-and-jupyterhub-on-amazon-emr/)
- [running-jupyter-notebook-and-jupyterhub-on-amazon-emr](https://aws.amazon.com/blogs/big-data/running-jupyter-notebook-and-jupyterhub-on-amazon-emr/)
### Google Cloud Platform
@@ -169,12 +175,12 @@ easy to do with RStudio too.
### Microsoft Azure
- [Azure Data Science Virtual Machine release notes](https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-linux-dsvm-intro)
- 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/#/
### Hadoop
@@ -183,13 +189,13 @@ easy to do with RStudio too.
## Miscellaneous
- https://medium.com/@ybarraud/setting-up-jupyterhub-with-sudospawner-and-anaconda-844628c0dbee#.rm3yt87e1
- [Mailing list UT deployment](https://groups.google.com/g/jupyter/c/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
- https://groups.google.com/forum/#!topic/jupyter/nkPSEeMr8c0 Mailing list UT deployment
- JupyterHub setup on Centos https://gist.github.com/johnrc/604971f7d41ebf12370bf5729bf3e0a4
- Deploy JupyterHub to Docker Swarm https://jupyterhub.surge.sh/#/welcome
- http://www.laketide.com/building-your-lab-part-3/
- http://estrellita.hatenablog.com/entry/2015/07/31/083202
- http://www.walkingrandomly.com/?p=5734
- https://wrdrd.com/docs/consulting/education-technology
- https://bitbucket.org/jackhale/fenics-jupyter
- [LinuxCluster blog](https://linuxcluster.wordpress.com/category/application/jupyterhub/)
- [Spark Cluster on OpenStack with Multi-User Jupyter Notebook](https://arnesund.com/2015/09/21/spark-cluster-on-openstack-with-multi-user-jupyter-notebook/)
- [Network Technology](https://arnesund.com/tag/jupyterhub/) [Spark Cluster on OpenStack with Multi-User Jupyter Notebook](https://arnesund.com/2015/09/21/spark-cluster-on-openstack-with-multi-user-jupyter-notebook/)

View File

@@ -1,12 +1,10 @@
(authenticators)=
# Authentication and User Basics
The default Authenticator uses [PAM][] (Pluggable Authentication Module) to authenticate system users with
their usernames and passwords. With the default Authenticator, any user
The default Authenticator uses [PAM][] 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`)
## Create a set of allowed users
You can restrict which users are allowed to login with a set,
`Authenticator.allowed_users`:
@@ -27,7 +25,7 @@ If this configuration value is not set, then **all authenticated users will be a
```{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
Instead, you can assign [roles][] to users or groups
with only the scopes they require.
```
@@ -44,10 +42,10 @@ 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
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:
group. For example we can let any user in the `wheel` group be admin:
```python
c.PAMAuthenticator.admin_groups = {'wheel'}
@@ -59,12 +57,12 @@ 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
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
Users can be added to and removed from the Hub via either the admin
panel or the REST API. When a user is **added**, the user will be
automatically added to the `allowed_users` set and database. Restarting the Hub
will not require manually updating the `allowed_users` set in your config file,
@@ -78,12 +76,12 @@ fresh.
## Use LocalAuthenticator to create system users
The `LocalAuthenticator` is a special kind of Authenticator that has
The `LocalAuthenticator` is a special kind of authenticator that has
the ability to manage users on the local system. When you try to add a
new user to the Hub, a `LocalAuthenticator` will check if the user
already exists. If you set the configuration value, `create_system_users`,
to `True` in the configuration file, the `LocalAuthenticator` has
the ability to add users to the system. The setting in the config
the privileges to add users to the system. The setting in the config
file is:
```python
@@ -93,7 +91,7 @@ c.LocalAuthenticator.create_system_users = True
Adding a user to the Hub that doesn't already exist on the system will
result in the Hub creating that user via the system `adduser` command
line tool. This option is typically used on hosted deployments of
JupyterHub to avoid the need to manually create all your users before
JupyterHub, to avoid the need to manually create all your users before
launching the service. This approach is not recommended when running
JupyterHub in situations where JupyterHub users map directly onto the
system's UNIX users.
@@ -103,25 +101,25 @@ system's UNIX users.
JupyterHub's [OAuthenticator][] currently supports the following
popular services:
- [Auth0](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.auth0.html)
- [Azure AD](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.azuread.html)
- [Bitbucket](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.bitbucket.html)
- [CILogon](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.cilogon.html)
- [GitHub](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.github.html)
- [GitLab](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.gitlab.html)
- [Globus](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.globus.html)
- [Google](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.google.html)
- [MediaWiki](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.mediawiki.html)
- [Okpy](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.okpy.html)
- [OpenShift](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.openshift.html)
- Auth0
- Azure AD
- Bitbucket
- CILogon
- GitHub
- GitLab
- Globus
- Google
- MediaWiki
- Okpy
- OpenShift
A [generic implementation](https://oauthenticator.readthedocs.io/en/latest/reference/api/gen/oauthenticator.generic.html), which you can use for OAuth authentication
A generic implementation, 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
The `DummyAuthenticator` is a simple authenticator that
allows for any username/password unless a global password has been set. If
set, it will allow for any username as long as the correct password is provided.
To set a global password, add this to the config file:

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)
The section contains basic information about configuring settings for a JupyterHub
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,
@@ -46,12 +44,12 @@ 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
To display all command line options that are available for configuration run the following command:
To display all command line options that are available for configuration:
```bash
jupyterhub --help-all
@@ -79,11 +77,11 @@ jupyterhub --Spawner.notebook_dir='~/assignments'
## Configure for various deployment environments
The default authentication and process spawning mechanisms can be replaced, and
specific [authenticators](authenticators-users-basics) and
[spawners](spawners-basics) can be set in the configuration file.
specific [authenticators](./authenticators-users-basics) and
[spawners](./spawners-basics) can be set in the configuration file.
This enables JupyterHub to be used with a variety of authentication methods or
process control and deployment environments. [Some examples](config-examples),
meant as illustrations, are:
process control and deployment environments. [Some examples](../reference/config-examples),
meant as illustration, are:
- Using GitHub OAuth instead of PAM with [OAuthenticator](https://github.com/jupyterhub/oauthenticator)
- Spawning single-user servers with Docker, using the [DockerSpawner](https://github.com/jupyterhub/dockerspawner)
@@ -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](separate-proxy).
for information see [the separate proxy page](../reference/separate-proxy).

View File

@@ -16,8 +16,7 @@ to come to _your server_ and look at the exact same file.
In most circumstances, this is forbidden by permissions because the person you share with does not have access to your server.
What actually happens when someone visits this URL will depend on whether your server is running and other factors.
**But what is our actual goal?**
But what is our actual goal?
A typical situation is that you have some shared or common filesystem,
such that the same path corresponds to the same document
(either the exact same document or another copy of it).

View File

@@ -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

View File

@@ -8,16 +8,10 @@ broken down by their roles within organizations.
### Is it appropriate for adoption within a larger institutional context?
Yes! JupyterHub has been used at-scale for large pools of users, as well
as complex and high-performance computing.
For example,
- UC Berkeley uses
JupyterHub for its Data Science Education Program courses (serving over
3,000 students).
- The Pangeo project uses JupyterHub to provide access
to scalable cloud computing with Dask.
JupyterHub is stable and customizable
as complex and high-performance computing. For example, UC Berkeley uses
JupyterHub for its Data Science Education Program courses (serving over
3,000 students). The Pangeo project uses JupyterHub to provide access
to scalable cloud computing with Dask. JupyterHub is stable and customizable
to the use-cases of large organizations.
### I keep hearing about Jupyter Notebook, JupyterLab, and now JupyterHub. Whats the difference?
@@ -32,7 +26,7 @@ Here is a quick breakdown of these three tools:
has several extensions that are tailored for using Jupyter Notebooks, as well as extensions
for other parts of the data science stack.
- **JupyterHub** is an application that manages interactive computing sessions for **multiple users**.
It also connects users with infrastructure they wish to access. It can provide
It also connects them with infrastructure those users wish to access. It can provide
remote access to Jupyter Notebooks and JupyterLab for many people.
## For management
@@ -41,7 +35,7 @@ Here is a quick breakdown of these three tools:
JupyterHub provides a shared platform for data science and collaboration.
It allows users to utilize familiar data science workflows (such as the scientific Python stack,
the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also gives administrators
the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also allows administrators
some control over access to resources, security, environments, and authentication.
### Is JupyterHub mature? Why should we trust it?
@@ -71,7 +65,7 @@ Here is a sample of organizations that use JupyterHub:
- **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?
@@ -84,7 +78,7 @@ gives administrators more control over their setup and hardware.
Because JupyterHub is an open-source, community-driven tool, it can be extended and
modified to fit an institution's needs. It plays nicely with the open source data science
stack, and can serve a variety of computing environments, user interfaces, and
stack, and can serve a variety of computing enviroments, user interfaces, and
computational hardware. It can also be deployed anywhere - on enterprise cloud infrastructure, on
High-Performance-Computing machines, on local hardware, or even on a single laptop, which
is not possible with most other tools for shared interactive computing.
@@ -105,12 +99,12 @@ that we currently suggest are:
guide that runs on Kubernetes. Better for larger or dynamic user groups (50-10,000) or more complex
compute/data needs.
- [The Littlest JupyterHub](https://tljh.jupyter.org) is a lightweight JupyterHub that runs on a single
machine (in the cloud or under your desk). Better for smaller user groups (4-80) or more
single machine (in the cloud or under your desk). Better for smaller user groups (4-80) or more
lightweight computational resources.
### Does JupyterHub run well in the cloud?
**Yes** - most deployments of JupyterHub are run via cloud infrastructure and on a variety of cloud providers.
Yes - most deployments of JupyterHub are run via cloud infrastructure and on a variety of cloud providers.
Depending on the distribution of JupyterHub that you'd like to use, you can also connect your JupyterHub
deployment with a number of other cloud-native services so that users have access to other resources from
their interactive computing sessions.
@@ -124,15 +118,14 @@ as more resources are needed - allowing you to utilize the benefits of a flexibl
### Is JupyterHub secure?
The short answer: yes.
JupyterHub as a standalone application has been battle-tested at an institutional
The short answer: yes. JupyterHub as a standalone application has been battle-tested at an institutional
level for several years, and makes a number of "default" security decisions that are reasonable for most
users.
- For security considerations in the base JupyterHub application,
[see the JupyterHub security page](https://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html).
- For security considerations when deploying JupyterHub on Kubernetes, see the
[JupyterHub on Kubernetes security page](https://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
@@ -141,11 +134,11 @@ in these cases, and the security of your JupyterHub deployment will often depend
If you are worried about security, don't hesitate to reach out to the JupyterHub community in the
[Jupyter Community Forum](https://discourse.jupyter.org/c/jupyterhub). This community of practice has many
individuals with experience running secure JupyterHub deployments and will be very glad to help you out.
individuals with experience running secure JupyterHub deployments.
### Does JupyterHub provide computing or data infrastructure?
**No** - JupyterHub manages user sessions and can _control_ computing infrastructure, but it does not provide these
No - JupyterHub manages user sessions and can _control_ computing infrastructure, but it does not provide these
things itself. You are expected to run JupyterHub on your own infrastructure (local or in the cloud). Moreover,
JupyterHub has no internal concept of "data", but is designed to be able to communicate with data repositories
(again, either locally or remotely) for use within interactive computing sessions.
@@ -198,7 +191,7 @@ complex computing infrastructures from the interactive sessions of a JupyterHub.
This is highly configurable by the administrator. If you wish for your users to have simple
data analytics environments for prototyping and light data exploring, you can restrict their
memory and CPU based on the resources that you have available. If you'd like your JupyterHub
to serve as a gateway to high-performance computing or data resources, you may increase the
to serve as a gateway to high-performance compute or data resources, you may increase the
resources available on user machines, or connect them with computing infrastructures elsewhere.
### Can I customize the look and feel of a JupyterHub?

View File

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

View File

@@ -0,0 +1,261 @@
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
configuration settings are the main aspects of security configuration:
1. :ref:`SSL encryption <ssl-encryption>` (to enable HTTPS)
2. :ref:`Cookie secret <cookie-secret>` (a key for encrypting browser cookies)
3. Proxy :ref:`authentication token <authentication-token>` (used for the Hub and
other services to authenticate to the Proxy)
The Hub hashes all secrets (e.g., auth tokens) before storing them in its
database. A loss of control over read-access to the database should have
minimal impact on your deployment; if your database has been compromised, it
is still a good idea to revoke existing tokens.
.. _ssl-encryption:
Enabling SSL encryption
-----------------------
Since JupyterHub includes authentication and allows arbitrary code execution,
you should not run it without SSL (HTTPS).
Using an SSL certificate
~~~~~~~~~~~~~~~~~~~~~~~~
This will require you to obtain an official, trusted SSL certificate or create a
self-signed certificate. Once you have obtained and installed a key and
certificate you need to specify their locations in the ``jupyterhub_config.py``
configuration file as follows:
.. code-block:: python
c.JupyterHub.ssl_key = '/path/to/my.key'
c.JupyterHub.ssl_cert = '/path/to/my.cert'
Some cert files also contain the key, in which case only the cert is needed. It
is important that these files be put in a secure location on your server, where
they are not readable by regular users.
If you are using a **chain certificate**, see also chained certificate for SSL
in the JupyterHub `Troubleshooting FAQ <../troubleshooting.html>`_.
Using letsencrypt
~~~~~~~~~~~~~~~~~
It is also possible to use `letsencrypt <https://letsencrypt.org/>`_ to obtain
a free, trusted SSL certificate. If you run letsencrypt using the default
options, the needed configuration is (replace ``mydomain.tld`` by your fully
qualified domain name):
.. code-block:: python
c.JupyterHub.ssl_key = '/etc/letsencrypt/live/{mydomain.tld}/privkey.pem'
c.JupyterHub.ssl_cert = '/etc/letsencrypt/live/{mydomain.tld}/fullchain.pem'
If the fully qualified domain name (FQDN) is ``example.com``, the following
would be the needed configuration:
.. code-block:: python
c.JupyterHub.ssl_key = '/etc/letsencrypt/live/example.com/privkey.pem'
c.JupyterHub.ssl_cert = '/etc/letsencrypt/live/example.com/fullchain.pem'
If SSL termination happens outside of the Hub
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In certain cases, for example if the hub is running behind a reverse proxy, and
`SSL termination is being provided by NGINX <https://www.nginx.com/resources/admin-guide/nginx-ssl-termination/>`_,
it is reasonable to run the hub without SSL.
To achieve this, simply omit the configuration settings
``c.JupyterHub.ssl_key`` and ``c.JupyterHub.ssl_cert``
(setting them to ``None`` does not have the same effect, and is an error).
.. _authentication-token:
Proxy authentication token
--------------------------
The Hub authenticates its requests to the Proxy using a secret token that
the Hub and Proxy agree upon. Note that this applies to the default
``ConfigurableHTTPProxy`` implementation. Not all proxy implementations
use an auth token.
The value of this token should be a random string (for example, generated by
``openssl rand -hex 32``). You can store it in the configuration file or an
environment variable
Generating and storing token in the configuration file
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You can set the value in the configuration file, ``jupyterhub_config.py``:
.. code-block:: python
c.ConfigurableHTTPProxy.api_token = 'abc123...' # any random string
Generating and storing as an environment variable
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You can pass this value of the proxy authentication token to the Hub and Proxy
using the ``CONFIGPROXY_AUTH_TOKEN`` environment variable:
.. code-block:: bash
export CONFIGPROXY_AUTH_TOKEN=$(openssl rand -hex 32)
This environment variable needs to be visible to the Hub and Proxy.
Default if token is not set
~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you don't set the Proxy authentication token, the Hub will generate a random
key itself, which means that any time you restart the Hub you **must also
restart the Proxy**. If the proxy is a subprocess of the Hub, this should happen
automatically (this is the default configuration).
.. _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. An example command to generate the
``jupyterhub_cookie_secret`` file is:
.. 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`` or 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 in the configuration file
itself, ``jupyterhub_config.py``, as a binary string:
.. code-block:: python
c.JupyterHub.cookie_secret = bytes.fromhex('64 CHAR HEX STRING')
.. important::
If the cookie secret value changes for the Hub, all single-user notebook
servers must also be restarted.
.. _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. And to avoid a stale cache the cache key is comprised of both
the token and session id.
Resetting the Hub cookie secret effectively revokes this cookie.
This cookie is restricted to the path ``/user/<username>``, so 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 logout of the multiple OAuth cookies.
This cookie is set to ``/`` so all endpoints can receive it, or 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 your 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.

View File

@@ -14,7 +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
@@ -24,31 +24,31 @@ Hub via the REST API.
## API Token basics
### Step 1: Generate an API token
### Create an API token
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-request.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
### Pass environment variable with token to the Hub
In the case of `cull_idle_servers`, it is passed as the environment
variable called `JUPYTERHUB_API_TOKEN`.
### Step 3: Use API tokens for services and tasks that require external access
### Use API tokens for services and tasks that require external access
While API tokens are often associated with a specific user, API tokens
can be used by services that require external access for activities
@@ -62,12 +62,12 @@ c.JupyterHub.services = [
]
```
### Step 4: Restart JupyterHub
### Restart JupyterHub
Upon restarting JupyterHub, you should see a message like below in the
logs:
```none
```
Adding API token for <username>
```
@@ -78,15 +78,16 @@ single-user servers, and only cookies can be used for authentication.
0.8 supports using JupyterHub API tokens to authenticate to single-user
servers.
## How to configure the idle culler to run as a Hub-Managed Service
## Configure the idle culler to run as a Hub-Managed Service
### Step 1: Install the idle culler:
Install the idle culler:
```
pip install jupyterhub-idle-culler
```
### Step 2: In `jupyterhub_config.py`, add the following dictionary for the `idle-culler` Service to the `c.JupyterHub.services` list:
In `jupyterhub_config.py`, add the following dictionary for the
`idle-culler` Service to the `c.JupyterHub.services` list:
```python
c.JupyterHub.services = [
@@ -126,7 +127,7 @@ It now needs the scopes:
- `admin:servers` to start/stop servers
```
## How to run `cull-idle` manually as a standalone script
## Run `cull-idle` manually as a standalone script
Now you can run your script by providing it
the API token and it will authenticate through the REST API to

View File

@@ -1,14 +1,12 @@
(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
multi-process application, many aspects of that server can be configured and there are a lot
Since the single-user server is an instance of `jupyter notebook`, an entire separate
multi-process application, there are many aspects of that server that can be configured, and a lot
of ways to express that configuration.
At the JupyterHub level, you can set some values on the Spawner. The simplest of these is
`Spawner.notebook_dir`, which lets you set the root directory for a user's server. This root
notebook directory is the highest-level directory users will be able to access in the notebook
notebook directory is the highest level directory users will be able to access in the notebook
dashboard. In this example, the root notebook directory is set to `~/notebooks`, where `~` is
expanded to the user's home directory.
@@ -22,7 +20,7 @@ You can also specify extra command line arguments to the notebook server with:
c.Spawner.args = ['--debug', '--profile=PHYS131']
```
This could be used to set the user's default page for the single-user server:
This could be used to set the users default page for the single user server:
```python
c.Spawner.args = ['--NotebookApp.default_url=/notebooks/Welcome.ipynb']

View File

@@ -1,245 +0,0 @@
# Configuring user environments
To deploy JupyterHub means you are providing Jupyter notebook environments for
multiple users. Often, this includes a desire to configure the user
environment in a custom way.
Since the `jupyterhub-singleuser` server extends the standard Jupyter notebook
server, most configuration and documentation that applies to Jupyter Notebook
applies to the single-user environments. Configuration of user environments
typically does not occur through JupyterHub itself, but rather through system-wide
configuration of Jupyter, which is inherited by `jupyterhub-singleuser`.
**Tip:** When searching for configuration tips for JupyterHub user environments, you might want to remove JupyterHub from your search because there are a lot more people out there configuring Jupyter than JupyterHub and the configuration is the same.
This section will focus on user environments, which includes the following:
- [Installing packages](#installing-packages)
- [Configuring Jupyter and IPython](#configuring-jupyter-and-ipython)
- [Installing kernelspecs](#installing-kernelspecs)
- [Using containers vs. multi-user hosts](#multi-user-hosts-vs-containers)
## Installing packages
To make packages available to users, you will typically install packages system-wide or in a shared environment.
This installation location should always be in the same environment where
`jupyterhub-singleuser` itself is installed in, and must be _readable and
executable_ by your users. If you want your users to be able to install additional
packages, the installation location must also be _writable_ by your users.
If you are using a standard Python installation on your system, use the following command:
```bash
sudo python3 -m pip install numpy
```
to install the numpy package in the default Python 3 environment on your system
(typically `/usr/local`).
You may also use conda to install packages. If you do, you should make sure
that the conda environment has appropriate permissions for users to be able to
run Python code in the env. The env must be _readable and executable_ by all
users. Additionally it must be _writeable_ if you want users to install
additional packages.
## Configuring Jupyter and IPython
[Jupyter](https://jupyter-notebook.readthedocs.io/en/stable/config_overview.html)
and [IPython](https://ipython.readthedocs.io/en/stable/development/config.html)
have their own configuration systems.
As a JupyterHub administrator, you will typically want to install and configure environments for all JupyterHub users. For example, let's say you wish for each student in a class to have the same user environment configuration.
Jupyter and IPython support **"system-wide"** locations for configuration, which is the logical place to put global configuration that you want to affect all users. It's generally more efficient to configure user environments "system-wide", and it's a good practice to avoid creating files in the users' home directories.
The typical locations for these config files are:
- **system-wide** in `/etc/{jupyter|ipython}`
- **env-wide** (environment wide) in `{sys.prefix}/etc/{jupyter|ipython}`.
### Jupyter environment configuration priority
When Jupyter runs in an environment (conda or virtualenv), it prefers to load configuration from the environment over each user's own configuration (e.g. in `~/.jupyter`).
This may cause issues if you use a _shared_ conda environment or virtualenv for users, because e.g. jupyterlab may try to write information like workspaces or settings to the environment instead of the user's own directory.
This could fail with something like `Permission denied: $PREFIX/etc/jupyter/lab`.
To avoid this issue, set `JUPYTER_PREFER_ENV_PATH=0` in the user environment:
```python
c.Spawner.environment.update(
{
"JUPYTER_PREFER_ENV_PATH": "0",
}
)
```
which tells Jupyter to prefer _user_ configuration paths (e.g. in `~/.jupyter`) to configuration set in the environment.
### Example: Enable an extension system-wide
For example, to enable the `cython` IPython extension for all of your users, create the file `/etc/ipython/ipython_config.py`:
```python
c.InteractiveShellApp.extensions.append("cython")
```
### Example: Enable a Jupyter notebook configuration setting for all users
:::{note}
These examples configure the Jupyter ServerApp, which is used by JupyterLab, the default in JupyterHub 2.0.
If you are using the classing Jupyter Notebook server,
the same things should work,
with the following substitutions:
- Search for `jupyter_server_config`, and replace with `jupyter_notebook_config`
- Search for `NotebookApp`, and replace with `ServerApp`
:::
To enable Jupyter notebook's internal idle-shutdown behavior (requires notebook ≥ 5.4), set the following in the `/etc/jupyter/jupyter_server_config.py` file:
```python
# shutdown the server after no activity for an hour
c.ServerApp.shutdown_no_activity_timeout = 60 * 60
# shutdown kernels after no activity for 20 minutes
c.MappingKernelManager.cull_idle_timeout = 20 * 60
# check for idle kernels every two minutes
c.MappingKernelManager.cull_interval = 2 * 60
```
## Installing kernelspecs
You may have multiple Jupyter kernels installed and want to make sure that they are available to all of your users. This means installing kernelspecs either system-wide (e.g. in /usr/local/) or in the `sys.prefix` of JupyterHub
itself.
Jupyter kernelspec installation is system-wide by default, but some kernels
may default to installing kernelspecs in your home directory. These will need
to be moved system-wide to ensure that they are accessible.
To see where your kernelspecs are, you can use the following command:
```bash
jupyter kernelspec list
```
### Example: Installing kernels system-wide
Let's assume that I have a Python 2 and Python 3 environment that I want to make sure are available, I can install their specs **system-wide** (in /usr/local) using the following command:
```bash
/path/to/python3 -m ipykernel install --prefix=/usr/local
/path/to/python2 -m ipykernel install --prefix=/usr/local
```
## Multi-user hosts vs. Containers
There are two broad categories of user environments that depend on what
Spawner you choose:
- Multi-user hosts (shared system)
- Container-based
How you configure user environments for each category can differ a bit
depending on what Spawner you are using.
The first category is a **shared system (multi-user host)** where
each user has a JupyterHub account, a home directory as well as being
a real system user. In this example, shared configuration and installation
must be in a 'system-wide' location, such as `/etc/`, or `/usr/local`
or a custom prefix such as `/opt/conda`.
When JupyterHub uses **container-based** Spawners (e.g. KubeSpawner or
DockerSpawner), the 'system-wide' environment is really the container image used for users.
In both cases, you want to _avoid putting configuration in user home
directories_ because users can change those configuration settings. Also, home directories typically persist once they are created, thereby making it difficult for admins to update later.
## Named servers
By default, in a JupyterHub deployment, each user has one server only.
JupyterHub can, however, have multiple servers per user.
This is mostly useful in deployments where users can configure the environment in which their server will start (e.g. resource requests on an HPC cluster), so that a given user can have multiple configurations running at the same time, without having to stop and restart their own server.
To allow named servers, include this code snippet in your config file:
```python
c.JupyterHub.allow_named_servers = True
```
Named servers were implemented in the REST API in JupyterHub 0.8,
and JupyterHub 1.0 introduces UI for managing named servers via the user home page:
![named servers on the home page](/images/named-servers-home.png)
as well as the admin page:
![named servers on the admin page](/images/named-servers-admin.png)
Named servers can be accessed, created, started, stopped, and deleted
from these pages. Activity tracking is now per server as well.
To limit the number of **named server** per user by setting a constant value, include this code snippet in your config file:
```python
c.JupyterHub.named_server_limit_per_user = 5
```
Alternatively, to use a callable/awaitable based on the handler object, include this code snippet in your config file:
```python
def named_server_limit_per_user_fn(handler):
user = handler.current_user
if user and user.admin:
return 0
return 5
c.JupyterHub.named_server_limit_per_user = named_server_limit_per_user_fn
```
This can be useful for quota service implementations. The example above limits the number of named servers for non-admin users only.
If `named_server_limit_per_user` is set to `0`, no limit is enforced.
When using named servers, Spawners may need additional configuration to take the `servername` into account. Whilst `KubeSpawner` takes the `servername` into account by default in [`pod_name_template`](https://jupyterhub-kubespawner.readthedocs.io/en/latest/spawner.html#kubespawner.KubeSpawner.pod_name_template), other Spawners may not. Check the documentation for the specific Spawner to see how singleuser servers are named, for example in `DockerSpawner` this involves modifying the [`name_template`](https://jupyterhub-dockerspawner.readthedocs.io/en/latest/api/index.html) setting to include `servername`, eg. `"{prefix}-{username}-{servername}"`.
(classic-notebook-ui)=
## Switching back to the classic notebook
By default, the single-user server launches JupyterLab,
which is based on [Jupyter Server][].
This is the default server when running JupyterHub ≥ 2.0.
To switch to using the legacy Jupyter Notebook server, you can set the `JUPYTERHUB_SINGLEUSER_APP` environment variable
(in the single-user environment) to:
```bash
export JUPYTERHUB_SINGLEUSER_APP='notebook.notebookapp.NotebookApp'
```
[jupyter server]: https://jupyter-server.readthedocs.io
[jupyter notebook]: https://jupyter-notebook.readthedocs.io
:::{versionchanged} 2.0
JupyterLab is now the default single-user UI, if available,
which is based on the [Jupyter Server][],
no longer the legacy [Jupyter Notebook][] server.
JupyterHub prior to 2.0 launched the legacy notebook server (`jupyter notebook`),
and the Jupyter server could be selected by specifying the following:
```python
# jupyterhub_config.py
c.Spawner.cmd = ["jupyter-labhub"]
```
Alternatively, for an otherwise customized Jupyter Server app,
set the environment variable using the following command:
```bash
export JUPYTERHUB_SINGLEUSER_APP='jupyter_server.serverapp.ServerApp'
```
:::

View File

@@ -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
```
(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
```

View File

@@ -1,141 +0,0 @@
(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.
## 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 & spawners you use, so
read the changelogs for those too!
## Notify your users
If you use the default configuration where `configurable-http-proxy`
is managed by JupyterHub, your users will see service disruption during
the upgrade process. You should notify them, and pick a time to do the
upgrade where they will be least disrupted.
If you use a different proxy or run `configurable-http-proxy`
independent of JupyterHub, your users will be able to continue using notebook
servers they had already launched, but will not be able to launch new servers or sign in.
## Backup database & config
Before doing an upgrade, it is critical to back up:
1. Your JupyterHub database (SQLite by default, or MySQL / Postgres if you used those).
If you use SQLite (the default), you should backup the `jupyterhub.sqlite` file.
2. Your `jupyterhub_config.py` file.
3. Your users' home directories. This is unlikely to be affected directly by
a JupyterHub upgrade, but we recommend a backup since user data is critical.
## Shut down JupyterHub
Shut down the JupyterHub process. This would vary depending on how you
have set up JupyterHub to run. It is most likely using a process
supervisor of some sort (`systemd` or `supervisord` or even `docker`).
Use the supervisor-specific command to stop the JupyterHub process.
## Upgrade JupyterHub packages
There are two environments where the `jupyterhub` package is installed:
1. The _hub environment_: where the JupyterHub server process
runs. This is started with the `jupyterhub` command, and is what
people generally think of as JupyterHub.
2. The _notebook user environments_: where the user notebook
servers are launched from, and is probably custom to your own
installation. This could be just one environment (different from the
hub environment) that is shared by all users, one environment
per user, or the same environment as the hub environment. The hub
launched the `jupyterhub-singleuser` command in this environment,
which in turn starts the notebook server.
You need to make sure the version of the `jupyterhub` package matches
in both these environments. If you installed `jupyterhub` with pip,
you can upgrade it with:
```bash
python3 -m pip install --upgrade jupyterhub==<version>
```
Where `<version>` is the version of JupyterHub you are upgrading to.
If you used `conda` to install `jupyterhub`, you should upgrade it
with:
```bash
conda install -c conda-forge jupyterhub==<version>
```
You should also check for new releases of the authenticator & spawner you
are using. You might wish to upgrade those packages, too, along with JupyterHub
or upgrade them separately.
## Upgrade JupyterHub database
Once new packages are installed, you need to upgrade the JupyterHub
database. From the hub environment, in the same directory as your
`jupyterhub_config.py` file, you should run:
```bash
jupyterhub upgrade-db
```
This should find the location of your database, and run the necessary upgrades
for it.
### SQLite database disadvantages
SQLite has some disadvantages when it comes to upgrading JupyterHub. These
are:
- `upgrade-db` may not work, and you may need to delete your database
and start with a fresh one.
- `downgrade-db` **will not** work if you want to rollback to an
earlier version, so backup the `jupyterhub.sqlite` file before
upgrading.
### What happens if I delete my database?
Losing the Hub database is often not a big deal. Information that
resides only in the Hub database includes:
- active login tokens (user cookies, service tokens)
- users added via JupyterHub UI, instead of config files
- info about running servers
If the following conditions are true, you should be fine clearing the
Hub database and starting over:
- users specified in the config file, or login using an external
authentication provider (Google, GitHub, LDAP, etc)
- user servers are stopped during the upgrade
- don't mind causing users to log in again after the upgrade
## Start JupyterHub
Once the database upgrade is completed, start the `jupyterhub`
process again.
1. Log in and start the server to make sure things work as
expected.
2. Check the logs for any errors or deprecation warnings. You
might have to update your `jupyterhub_config.py` file to
deal with any deprecated options.
Congratulations, your JupyterHub has been upgraded!

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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

View File

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

View File

@@ -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://k8s.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

157
docs/source/index.rst Normal file
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@@ -0,0 +1,157 @@
==========
JupyterHub
==========
`JupyterHub`_ is the best way to serve `Jupyter notebook`_ for multiple users.
It can be used in a class of students, a corporate data science group or scientific
research group. It is a multi-user **Hub** that spawns, manages, and proxies multiple
instances of the single-user `Jupyter notebook`_ server.
To make life easier, JupyterHub has distributions. Be sure to
take a look at them before continuing with the configuration of the broad
original system of `JupyterHub`_. Today, you can find two main cases:
1. If you need a simple case for a small amount of users (0-100) and single server
take a look at
`The Littlest JupyterHub <https://github.com/jupyterhub/the-littlest-jupyterhub>`__ distribution.
2. If you need to allow for even more users, a dynamic amount of servers can be used on a cloud,
take a look at the `Zero to JupyterHub with Kubernetes <https://github.com/jupyterhub/zero-to-jupyterhub-k8s>`__ .
Four subsystems make up JupyterHub:
* a **Hub** (tornado process) that is the heart of JupyterHub
* a **configurable http proxy** (node-http-proxy) that receives the requests from the client's browser
* multiple **single-user Jupyter notebook servers** (Python/IPython/tornado) that are monitored by Spawners
* an **authentication class** that manages how users can access the system
Besides these central pieces, you can add optional configurations through a `config.py` file and manage users kernels on an admin panel. A simplification of the whole system can be seen in the figure below:
.. image:: images/jhub-fluxogram.jpeg
:alt: JupyterHub subsystems
:width: 80%
:align: center
JupyterHub performs the following functions:
- The Hub launches a proxy
- The proxy forwards all requests to the Hub by default
- The Hub handles user login and spawns single-user servers on demand
- The Hub configures the proxy to forward URL prefixes to the single-user
notebook servers
For convenient administration of the Hub, its users, and services,
JupyterHub also provides a :doc:`REST API <reference/rest-api>`.
The JupyterHub team and Project Jupyter value our community, and JupyterHub
follows the Jupyter `Community Guides <https://jupyter.readthedocs.io/en/latest/community/content-community.html>`_.
Contents
========
.. _index/distributions:
Distributions
-------------
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.
The two popular ones are:
* `Zero to JupyterHub on Kubernetes <http://z2jh.jupyter.org>`_, for
running JupyterHub on top of `Kubernetes <https://k8s.io>`_. This
can scale to large number of machines & users.
* `The Littlest JupyterHub <http://tljh.jupyter.org>`_, for an easy
to set up & run JupyterHub supporting 1-100 users on a single machine.
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 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
contributing/index
About JupyterHub
----------------
.. toctree::
:maxdepth: 2
index-about
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
Questions? Suggestions?
=======================
- `Jupyter mailing list <https://groups.google.com/forum/#!forum/jupyter>`_
- `Jupyter website <https://jupyter.org>`_
.. _JupyterHub: https://github.com/jupyterhub/jupyterhub
.. _Jupyter notebook: https://jupyter-notebook.readthedocs.io/en/latest/

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

View File

@@ -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>

View File

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

View File

@@ -0,0 +1,13 @@
Installation
============
These sections cover how to get up-and-running with JupyterHub. They cover
some basics of the tools needed to deploy JupyterHub as well as how to get it
running on your own infrastructure.
.. toctree::
:maxdepth: 3
quickstart
quickstart-docker
installation-basics

View File

@@ -0,0 +1,49 @@
Using Docker
============
.. important::
We highly recommend following the `Zero to JupyterHub`_ tutorial for
installing JupyterHub.
Alternate installation using Docker
-----------------------------------
A ready to go `docker image <https://hub.docker.com/r/jupyterhub/jupyterhub/>`_
gives a straightforward deployment of JupyterHub.
.. note::
This ``jupyterhub/jupyterhub`` docker image is only an image for running
the Hub service itself. It does not provide the other Jupyter components,
such as Notebook installation, which are needed by the single-user servers.
To run the single-user servers, which may be on the same system as the Hub or
not, Jupyter Notebook version 4 or greater must be installed.
Starting JupyterHub with docker
-------------------------------
The JupyterHub docker image can be started with the following command::
docker run -d -p 8000:8000 --name jupyterhub jupyterhub/jupyterhub jupyterhub
This command will create a container named ``jupyterhub`` that you can
**stop and resume** with ``docker stop/start``.
The Hub service will be listening on all interfaces at port 8000, which makes
this a good choice for **testing JupyterHub on your desktop or laptop**.
If you want to run docker on a computer that has a public IP then you should
(as in MUST) **secure it with ssl** by adding ssl options to your docker
configuration or using a ssl enabled proxy.
`Mounting volumes <https://docs.docker.com/engine/admin/volumes/volumes/>`_
will allow you to store data outside the docker image (host system) so it will
be persistent, even when you start a new image.
The command ``docker exec -it jupyterhub bash`` will spawn a root shell in your
docker container. You can use the root shell to **create system users in the container**.
These accounts will be used for authentication in JupyterHub's default
configuration.
.. _Zero to JupyterHub: https://zero-to-jupyterhub.readthedocs.io/en/latest/

View File

@@ -4,10 +4,10 @@
Before installing JupyterHub, you will need:
- a Linux/Unix-based system
- a Linux/Unix based system
- [Python](https://www.python.org/downloads/) 3.6 or greater. An understanding
of using [`pip`](https://pip.pypa.io) or
[`conda`](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html) for
[`conda`](https://conda.io/docs/get-started.html) for
installing Python packages is helpful.
- [nodejs/npm](https://www.npmjs.com/). [Install nodejs/npm](https://docs.npmjs.com/getting-started/installing-node),
using your operating system's package manager.
@@ -27,7 +27,7 @@ Before installing JupyterHub, you will need:
if your system package manager only has an old version of Node.js (e.g. 10 or older).
- A [pluggable authentication module (PAM)](https://en.wikipedia.org/wiki/Pluggable_authentication_module)
to use the [default Authenticator](authenticators).
to use the [default Authenticator](./getting-started/authenticators-users-basics.md).
PAM is often available by default on most distributions, if this is not the case it can be installed by
using the operating system's package manager.
- TLS certificate and key for HTTPS communication
@@ -80,7 +80,7 @@ To start the Hub server, run the command:
jupyterhub
```
Visit `http://localhost:8000` in your browser, and sign in with your Unix
Visit `http://localhost:8000` in your browser, and sign in with your unix
credentials.
To **allow multiple users to sign in** to the Hub server, you must start

View File

@@ -1,10 +1,3 @@
<!---
RBAC docs are part of the Explanation section of the JupyterHub documentation.
As a result, this index file is referenced in the toctree within the explanation/index.md file.
--->
(rbac)=
# JupyterHub RBAC
Role Based Access Control (RBAC) in JupyterHub serves to provide fine grained control of access to Jupyterhub's API resources.

View File

@@ -1,6 +1,8 @@
(roles)=
# Roles
JupyterHub provides four (4) roles that are available by default:
JupyterHub provides four roles that are available by default:
```{admonition} **Default roles**
- `user` role provides a {ref}`default user scope <default-user-scope-target>` `self` that grants access to the user's own resources.
@@ -11,11 +13,11 @@ JupyterHub provides four (4) roles that are available by default:
**These roles cannot be deleted.**
```
We call these 'default' roles because they are available by default and have a default collection of scopes.
However, you can define the scopes associated with each role (excluding the admin role) to suit your needs,
These default roles have a default collection of scopes,
but you can define the scopes associated with each role (excluding admin) to suit your needs,
as seen [below](overriding-default-roles).
The `user`, `admin`, and `token` roles, by default, all preserve the permissions prior to Role-based Access Control (RBAC).
The `user`, `admin`, and `token` roles by default all preserve the permissions prior to RBAC.
Only the `server` role is changed from pre-2.0, to reduce its permissions to activity-only
instead of the default of a full access token.
@@ -25,20 +27,21 @@ Roles can be assigned to the following entities:
- Users
- Services
- Groups
- Tokens
An entity can have zero, one, or multiple roles, and there are no restrictions on which roles can be assigned to which entity. Roles can be added to or removed from entities at any time.
**Users** \
When a new user gets created, they are assigned their default role, `user`. Additionally, if the user is created with admin privileges (via `c.Authenticator.admin_users` in `jupyterhub_config.py` or `admin: true` via API), they will be also granted `admin` role. If existing user's admin status changes via API or `jupyterhub_config.py`, their default role will be updated accordingly (after next startup for the latter).
When a new user gets created, they are assigned their default role `user`. Additionaly, if the user is created with admin privileges (via `c.Authenticator.admin_users` in `jupyterhub_config.py` or `admin: true` via API), they will be also granted `admin` role. If existing user's admin status changes via API or `jupyterhub_config.py`, their default role will be updated accordingly (after next startup for the latter).
**Services** \
Services do not have a default role. Services without roles have no access to the guarded API end-points. So, most services will require assignment of a role in order to function.
Services do not have a default role. Services without roles have no access to the guarded API end-points, so most services will require assignment of a role in order to function.
**Groups** \
A group does not require any role, and has no roles by default. If a user is a member of a group, they automatically inherit any of the group's permissions (see {ref}`resolving-roles-scopes-target` for more details). This is useful for assigning a set of common permissions to several users.
**Tokens** \
A tokens permissions are evaluated based on their owning entity. Since a token is always issued for a user or service, it can never have more permissions than its owner. If no specific scopes are requested for a new token, the token is assigned the scopes of the `token` role.
A tokens permissions are evaluated based on their owning entity. Since a token is always issued for a user or service, it can never have more permissions than its owner. If no specific role is requested for a new token, the token is assigned the `token` role.
(define-role-target)=
@@ -111,7 +114,7 @@ In case the role with a certain name already exists in the database, its definit
(overriding-default-roles)=
### Overriding Default Roles
### Overriding default roles
Role definitions can include those of the "default" roles listed above (admin excluded),
if the default scopes associated with those roles do not suit your deployment.
@@ -152,7 +155,7 @@ c.JupyterHub.load_roles = [
(removing-roles-target)=
## Removing Roles
## Removing roles
Only the entities present in the role definition in the `jupyterhub_config.py` remain the role bearers. If a user, service or group is removed from the role definition, they will lose the role on the next startup.

View File

@@ -1,10 +1,8 @@
(jupyterhub-scopes)=
# Scopes in JupyterHub
A scope has a syntax-based design that reveals which resources it provides access to. Resources are objects with a type, associated data, relationships to other resources, and a set of methods that operate on them (see [RESTful API](https://restful-api-design.readthedocs.io/en/latest/resources.html) documentation for more information).
`<resource>` in the RBAC scope design refers to the resource name in the [JupyterHub's API](jupyterhub-rest-API) endpoints in most cases. For instance, `<resource>` equal to `users` corresponds to JupyterHub's API endpoints beginning with _/users_.
`<resource>` in the RBAC scope design refers to the resource name in the [JupyterHub's API](../reference/rest-api.rst) endpoints in most cases. For instance, `<resource>` equal to `users` corresponds to JupyterHub's API endpoints beginning with _/users_.
(scope-conventions-target)=
@@ -74,31 +72,13 @@ Requested resources are filtered based on the filter of the corresponding scope.
In case a user resource is being accessed, any scopes with _group_ filters will be expanded to filters for each _user_ in those groups.
(self-referencing-filters)=
### Self-referencing filters
There are some 'shortcut' filters,
which can be applied to all scopes,
that filter based on the entities associated with the request.
### `!user` filter
The `!user` filter is a special horizontal filter that strictly refers to the **"owner only"** scopes, where _owner_ is a user entity. The filter resolves internally into `!user=<ownerusername>` ensuring that only the owner's resources may be accessed through the associated scopes.
For example, the `server` role assigned by default to server tokens contains `access:servers!user` and `users:activity!user` scopes. This allows the token to access and post activity of only the servers owned by the token owner.
:::{versionadded} 3.0
`!service` and `!server` filters.
:::
In addition to `!user`, _tokens_ may have filters `!service`
or `!server`, which expand similarly to `!service=servicename`
and `!server=servername`.
This only applies to tokens issued via the OAuth flow.
In these cases, the name is the _issuing_ entity (a service or single-user server),
so that access can be restricted to the issuing service,
e.g. `access:servers!server` would grant access only to the server that requested the token.
These filters can be applied to any scope.
The filter can be applied to any scope.
(vertical-filtering-target)=
@@ -134,172 +114,13 @@ There are four exceptions to the general {ref}`scope conventions <scope-conventi
```
:::{versionadded} 3.0
The `admin-ui` scope is added to explicitly grant access to the admin page,
rather than combining `admin:users` and `admin:servers` permissions.
This means a deployment can enable the admin page with only a subset of functionality enabled.
Note that this means actions to take _via_ the admin UI
and access _to_ the admin UI are separated.
For example, it generally doesn't make sense to grant
`admin-ui` without at least `list:users` for at least some subset of users.
For example:
```python
c.JupyterHub.load_roles = [
{
"name": "instructor-data8",
"scopes": [
# access to the admin page
"admin-ui",
# list users in the class group
"list:users!group=students-data8",
# start/stop servers for users in the class
"admin:servers!group=students-data8",
# access servers for users in the class
"access:servers!group=students-data8",
],
"group": ["instructors-data8"],
}
]
```
will grant instructors in the data8 course permission to:
1. view the admin UI
2. see students in the class (but not all users)
3. start/stop/access servers for users in the class
4. but _not_ permission to administer the users themselves (e.g. change their permissions, etc.)
:::
```{Caution}
Note that only the {ref}`horizontal filtering <horizontal-filtering-target>` can be added to scopes to customize them. \
Metascopes `self` and `all`, `<resource>`, `<resource>:<subresource>`, `read:<resource>`, `admin:<resource>`, and `access:<resource>` scopes are predefined and cannot be changed otherwise.
```
(custom-scopes)=
### Custom scopes
:::{versionadded} 3.0
:::
JupyterHub 3.0 introduces support for custom scopes.
Services that use JupyterHub for authentication and want to implement their own granular access may define additional _custom_ scopes and assign them to users with JupyterHub roles.
% Note: keep in sync with pattern/description in jupyterhub/scopes.py
Custom scope names must start with `custom:`
and contain only lowercase ascii letters, numbers, hyphen, underscore, colon, and asterisk (`-_:*`).
The part after `custom:` must start with a letter or number.
Scopes may not end with a hyphen or colon.
The only strict requirement is that a custom scope definition must have a `description`.
It _may_ also have `subscopes` if you are defining multiple scopes that have a natural hierarchy,
For example:
```python
c.JupyterHub.custom_scopes = {
"custom:myservice:read": {
"description": "read-only access to myservice",
},
"custom:myservice:write": {
"description": "write access to myservice",
# write permission implies read permission
"subscopes": [
"custom:myservice:read",
],
},
}
c.JupyterHub.load_roles = [
# graders have read-only access to the service
{
"name": "service-user",
"groups": ["graders"],
"scopes": [
"custom:myservice:read",
"access:service!service=myservice",
],
},
# instructors have read and write access to the service
{
"name": "service-admin",
"groups": ["instructors"],
"scopes": [
"custom:myservice:write",
"access:service!service=myservice",
],
},
]
```
In the above configuration, two scopes are defined:
- `custom:myservice:read` grants read-only access to the service, and
- `custom:myservice:write` grants write access to the service
- write access _implies_ read access via the `subscope`
These custom scopes are assigned to two groups via `roles`:
- users in the group `graders` are granted read access to the service
- users in the group `instructors` are
- both are granted _access_ to the service via `access:service!service=myservice`
When the service completes OAuth, it will retrieve the user model from `/hub/api/user`.
This model includes a `scopes` field which is a list of authorized scope for the request,
which can be used.
```python
def require_scope(scope):
"""decorator to require a scope to perform an action"""
def wrapper(func):
@functools.wraps(func)
def wrapped_func(request):
user = fetch_hub_api_user(request.token)
if scope not in user["scopes"]:
raise HTTP403(f"Requires scope {scope}")
else:
return func()
return wrapper
@require_scope("custom:myservice:read")
async def read_something(request):
...
@require_scope("custom:myservice:write")
async def write_something(request):
...
```
If you use {class}`~.HubOAuthenticated`, this check is performed automatically
against the `.hub_scopes` attribute of each Handler
(the default is populated from `$JUPYTERHUB_OAUTH_ACCESS_SCOPES` and usually `access:services!service=myservice`).
:::{versionchanged} 3.0
The JUPYTERHUB_OAUTH_SCOPES environment variable is deprecated and renamed to JUPYTERHUB_OAUTH_ACCESS_SCOPES,
to avoid ambiguity with JUPYTERHUB_OAUTH_CLIENT_ALLOWED_SCOPES
:::
```python
from tornado import web
from jupyterhub.services.auth import HubOAuthenticated
class MyHandler(HubOAuthenticated, BaseHandler):
hub_scopes = ["custom:myservice:read"]
@web.authenticated
def get(self):
...
```
Existing scope filters (`!user=`, etc.) may be applied to custom scopes.
Custom scope _filters_ are NOT supported.
### Scopes and APIs
The scopes are also listed in the [](jupyterhub-rest-API) documentation. Each API endpoint has a list of scopes which can be used to access the API; if no scopes are listed, the API is not authenticated and can be accessed without any permissions (i.e., no scopes).
The scopes are also listed in the [](../reference/rest-api.rst) documentation. Each API endpoint has a list of scopes which can be used to access the API; if no scopes are listed, the API is not authenticated and can be accessed without any permissions (i.e., no scopes).
Listed scopes by each API endpoint reflect the "lowest" permissions required to gain any access to the corresponding API. For example, posting user's activity (_POST /users/:name/activity_) needs `users:activity` scope. If scope `users` is passed during the request, the access will be granted as the required scope is a subscope of the `users` scope. If, on the other hand, `read:users:activity` scope is passed, the access will be denied.

View File

@@ -1,71 +1,52 @@
# Technical Implementation
[Roles](roles) are stored in the database, where they are associated with users, services, and groups. Roles can be added or modified as explained in the {ref}`define-role-target` section. Users, services, groups, and tokens can gain, change, and lose roles. This is currently achieved via `jupyterhub_config.py` (see {ref}`define-role-target`) and will be made available via API in the future. The latter will allow for changing a user's role, and thereby its permissions, without the need to restart JupyterHub.
Roles are stored in the database, where they are associated with users, services, etc., and can be added or modified as explained in {ref}`define-role-target` section. Users, services, groups, and tokens can gain, change, and lose roles. This is currently achieved via `jupyterhub_config.py` (see {ref}`define-role-target`) and will be made available via API in future. The latter will allow for changing a token's role, and thereby its permissions, without the need to issue a new token.
Roles and scopes utilities can be found in `roles.py` and `scopes.py` modules. Scope variables take on five different formats that are reflected throughout the utilities via specific nomenclature:
Roles and scopes utilities can be found in `roles.py` and `scopes.py` modules. Scope variables take on five different formats which is reflected throughout the utilities via specific nomenclature:
```{admonition} **Scope variable nomenclature**
:class: tip
- _scopes_ \
List of scopes that may contain abbreviations (used in role definitions). E.g., `["users:activity!user", "self"]`.
List of scopes with abbreviations (used in role definitions). E.g., `["users:activity!user"]`.
- _expanded scopes_ \
Set of fully expanded scopes without abbreviations (i.e., resolved metascopes, filters, and subscopes). E.g., `{"users:activity!user=charlie", "read:users:activity!user=charlie"}`.
Set of expanded scopes without abbreviations (i.e., resolved metascopes, filters and subscopes). E.g., `{"users:activity!user=charlie", "read:users:activity!user=charlie"}`.
- _parsed scopes_ \
Dictionary representation of expanded scopes. E.g., `{"users:activity": {"user": ["charlie"]}, "read:users:activity": {"users": ["charlie"]}}`.
Dictionary JSON like format of expanded scopes. E.g., `{"users:activity": {"user": ["charlie"]}, "read:users:activity": {"users": ["charlie"]}}`.
- _intersection_ \
Set of expanded scopes as intersection of 2 expanded scope sets.
- _identify scopes_ \
Set of expanded scopes needed for identity (whoami) endpoints.
Set of expanded scopes needed for identify (whoami) endpoints.
```
(resolving-roles-scopes-target)=
## Resolving roles and scopes
**Resolving roles** involves determining which roles a user, service, or group has, extracting the list of scopes from each role and combining them into a single set of scopes.
**Resolving roles** refers to determining which roles a user, service, token, or group has, extracting the list of scopes from each role and combining them into a single set of scopes.
**Resolving scopes** involves expanding scopes into all their possible subscopes (_expanded scopes_), parsing them into the format used for access evaluation (_parsed scopes_) and, if applicable, comparing two sets of scopes (_intersection_). All procedures take into account the scope hierarchy, {ref}`vertical <vertical-filtering-target>` and {ref}`horizontal filtering <horizontal-filtering-target>`, limiting or elevated permissions (`read:<resource>` or `admin:<resource>`, respectively), and metascopes.
**Resolving scopes** involves expanding scopes into all their possible subscopes (_expanded scopes_), parsing them into format used for access evaluation (_parsed scopes_) and, if applicable, comparing two sets of scopes (_intersection_). All procedures take into account the scope hierarchy, {ref}`vertical <vertical-filtering-target>` and {ref}`horizontal filtering <horizontal-filtering-target>`, limiting or elevated permissions (`read:<resource>` or `admin:<resource>`, respectively), and metascopes.
Roles and scopes are resolved on several occasions, for example when requesting an API token with specific scopes or when making an API request. The following sections provide more details.
Roles and scopes are resolved on several occasions, for example when requesting an API token with specific roles or making an API request. The following sections provide more details.
(requesting-api-token-target)=
### Requesting API token with specific scopes
### Requesting API token with specific roles
:::{versionchanged} 3.0
API tokens have _scopes_ instead of roles,
so that their permissions cannot be updated.
API tokens grant access to JupyterHub's APIs. The RBAC framework allows for requesting tokens with specific existing roles. To date, it is only possible to add roles to a token through the _POST /users/:name/tokens_ API where the roles can be specified in the token parameters body (see [](../reference/rest-api.rst)).
You may still request roles for a token,
but those roles will be evaluated to the corresponding _scopes_ immediately.
RBAC adds several steps into the token issue flow.
Prior to 3.0, tokens stored _roles_,
which meant their scopes were resolved on each request.
:::
If no roles are requested, the token is issued with the default `token` role (providing the requester is allowed to create the token).
API tokens grant access to JupyterHub's APIs. The [RBAC framework](./index.md) allows for requesting tokens with specific permissions.
If the token is requested with any roles, the permissions of requesting entity are checked against the requested permissions to ensure the token would not grant its owner additional privileges.
RBAC is involved in several stages of the OAuth token flow.
If, due to modifications of roles or entities, at API request time a token has any scopes that its owner does not, those scopes are removed. The API request is resolved without additional errors using the scopes _intersection_, but the Hub logs a warning (see {ref}`Figure 2 <api-request-chart>`).
When requesting a token via the tokens API (`/users/:name/tokens`), or the token page (`/hub/token`),
if no scopes are requested, the token is issued with the permissions stored on the default `token` role
(provided the requester is allowed to create the token).
OAuth tokens are also requested via OAuth flow
If the token is requested with any scopes, the permissions of requesting entity are checked against the requested permissions to ensure the token would not grant its owner additional privileges.
If a token has any scopes that its owner does not possess
at the time of making the API request, those scopes are removed.
The API request is resolved without additional errors using the scope _intersection_;
the Hub logs a warning in this case (see {ref}`Figure 2 <api-request-chart>`).
Resolving a token's scope (yellow box in {ref}`Figure 1 <token-request-chart>`) corresponds to resolving all the roles of the token's owner (including the roles associated with their groups) and the token's own scopes into a set of scopes. The two sets are compared (Resolve the scopes box in orange in {ref}`Figure 1 <token-request-chart>`), taking into account the scope hierarchy.
If the token's scopes are a subset of the token owner's scopes, the token is issued with the requested scopes; if not, JupyterHub will raise an error.
Resolving a token's roles (yellow box in {ref}`Figure 1 <token-request-chart>`) corresponds to resolving all the token's owner roles (including the roles associated with their groups) and the token's requested roles into a set of scopes. The two sets are compared (Resolve the scopes box in orange in {ref}`Figure 1 <token-request-chart>`), taking into account the scope hierarchy but, solely for role assignment, omitting any {ref}`horizontal filter <horizontal-filtering-target>` comparison. If the token's scopes are a subset of the token owner's scopes, the token is issued with the requested roles; if not, JupyterHub will raise an error.
{ref}`Figure 1 <token-request-chart>` below illustrates the steps involved. The orange rectangles highlight where in the process the roles and scopes are resolved.
```{figure} /images/rbac-token-request-chart.png
```{figure} ../images/rbac-token-request-chart.png
:align: center
:name: token-request-chart
@@ -74,10 +55,10 @@ Figure 1. Resolving roles and scopes during API token request
### Making an API request
With the RBAC framework, each authenticated JupyterHub API request is guarded by a scope decorator that specifies which scopes are required in order to gain the access to the API.
With the RBAC framework each authenticated JupyterHub API request is guarded by a scope decorator that specifies which scopes are required to gain the access to the API.
When an API request is made, the requesting API token's scopes are again intersected with its owner's (yellow box in {ref}`Figure 2 <api-request-chart>`) to ensure that the token does not grant more permissions than its owner has at the request time (e.g., due to changing/losing roles).
If the owner's roles do not include some scopes of the token, only the _intersection_ of the token's and owner's scopes will be used. For example, using a token with scope `users` whose owner's role scope is `read:users:name` will result in only the `read:users:name` scope being passed on. In the case of no _intersection_, an empty set of scopes will be used.
When an API request is performed, the requesting API token's roles are again resolved (yellow box in {ref}`Figure 2 <api-request-chart>`) to ensure the token does not grant more permissions than its owner has at the request time (e.g., due to changing/losing roles).
If the owner's roles do not include some scopes of the token's scopes, only the _intersection_ of the token's and owner's scopes will be used. For example, using a token with scope `users` whose owner's role scope is `read:users:name` will result in only the `read:users:name` scope being passed on. In the case of no _intersection_, an empty set of scopes will be used.
The passed scopes are compared to the scopes required to access the API as follows:
@@ -85,13 +66,13 @@ The passed scopes are compared to the scopes required to access the API as follo
- if that is not the case, another check is utilized to determine if subscopes of the required API scopes can be found in the passed scope set:
- if found, the RBAC framework employs the {ref}`filtering <vertical-filtering-target>` procedures to refine the API response to access only resource attributes corresponding to the passed scopes. For example, providing a scope `read:users:activity!group=class-C` for the `GET /users` API will return a list of user models from group `class-C` containing only the `last_activity` attribute for each user model
- if found, the RBAC framework employs the {ref}`filtering <vertical-filtering-target>` procedures to refine the API response to access only resource attributes corresponding to the passed scopes. For example, providing a scope `read:users:activity!group=class-C` for the _GET /users_ API will return a list of user models from group `class-C` containing only the `last_activity` attribute for each user model
- if not found, the access to API is denied
{ref}`Figure 2 <api-request-chart>` illustrates this process highlighting the steps where the role and scope resolutions as well as filtering occur in orange.
```{figure} /images/rbac-api-request-chart.png
```{figure} ../images/rbac-api-request-chart.png
:align: center
:name: api-request-chart

View File

@@ -11,7 +11,7 @@ No other database records are affected.
## Upgrade steps
1. All running **servers must be stopped** before proceeding with the upgrade.
2. To upgrade the Hub, follow the [Upgrading JupyterHub](upgrading-jupyterhub) instructions.
2. To upgrade the Hub, follow the [Upgrading JupyterHub](../admin/upgrading.rst) instructions.
```{attention}
We advise against defining any new roles in the `jupyterhub.config.py` file right after the upgrade is completed and JupyterHub restarted for the first time. This preserves the 'current' state of the Hub. You can define and assign new roles on any other following startup.
```
@@ -45,7 +45,7 @@ OAuth token is issued by the Hub to a single-user server when the user logs in.
API token is issued by the Hub to a single-user server when launched and is used to communicate with the Hub's APIs such as posting activity or completing the OAuth flow. This token has no expiry by default.
API tokens can also be issued to users via API ([_/hub/token_](jupyterhub-url) or [_POST /users/:username/tokens_](jupyterhub-rest-API)) and services via `jupyterhub_config.py` to perform API requests.
API tokens can also be issued to users via API ([_/hub/token_](../reference/urls.md) or [_POST /users/:username/tokens_](../reference/rest-api.rst)) and services via `jupyterhub_config.py` to perform API requests.
### With RBAC

View File

@@ -3,18 +3,18 @@
To determine which scopes a role should have, one can follow these steps:
1. Determine what actions the role holder should have/have not access to
2. Match the actions against the [JupyterHub's APIs](jupyterhub-rest-API)
2. Match the actions against the [JupyterHub's APIs](../reference/rest-api.rst)
3. Check which scopes are required to access the APIs
4. Combine scopes and subscopes if applicable
5. Customize the scopes with filters if needed
6. Define the role with required scopes and assign to users/services/groups/tokens
Below, different use cases are presented on how to use the [RBAC framework](rbac)
Below, different use cases are presented on how to use the RBAC framework.
## Service to cull idle servers
Finding and shutting down idle servers can save a lot of computational resources.
**We can make use of [jupyterhub-idle-culler](https://github.com/jupyterhub/jupyterhub-idle-culler) to manage this for us.**
We can make use of [jupyterhub-idle-culler](https://github.com/jupyterhub/jupyterhub-idle-culler) to manage this for us.
Below follows a short tutorial on how to add a cull-idle service in the RBAC system.
1. Install the cull-idle server script with `pip install jupyterhub-idle-culler`.

View File

@@ -1,23 +0,0 @@
# This file contains rediraffe redirects as generated from the docs/source/conf.py file
# For more information, see rediraffe configuration in the conf.py file.
"changelog.md" "reference/changelog.md"
"getting-started/faq.md" "faq/faq.md"
"reference/api-only.md" "howto/api-only.md"
"reference/config-ghoauth.md" "howto/configuration/config-ghoauth.md"
"reference/config-proxy.md" "howto/configuration/config-proxy.md"
"admin/log-messages.md" "howto/log-messages.md"
"reference/proxy.md" "howto/proxy.md"
"reference/templates.md" "howto/templates.md"
"quickstart-docker.md" "tutorial/quickstart-docker.md"
"reference/config-examples.md" "howto/index.md"
"getting-started/institutional-faq.md" "faq/institutional-faq.md"
"troubleshooting.md" "faq/troubleshooting.md"
"reference/config-sudo.md" "howto/configuration/config-sudo.md"
"reference/config-user-env.md" "howto/configuration/config-user-env.md"
"reference/rest.md" "howto/rest.md"
"reference/separate-proxy.md" "howto/separate-proxy.md"
"admin/upgrading.md" "howto/upgrading.md"
"installation-basics.md" "tutorial/installation-basics.md"
"quickstart.md" "tutorial/quickstart.md"
"events/index.md" "reference/event-logging.md"

View File

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

View File

@@ -1,33 +0,0 @@
# Authenticators
## Module: {mod}`jupyterhub.auth`
```{eval-rst}
.. automodule:: jupyterhub.auth
```
### {class}`Authenticator`
```{eval-rst}
.. autoconfigurable:: Authenticator
:members:
```
### {class}`LocalAuthenticator`
```{eval-rst}
.. autoconfigurable:: LocalAuthenticator
:members:
```
### {class}`PAMAuthenticator`
```{eval-rst}
.. autoconfigurable:: PAMAuthenticator
```
### {class}`DummyAuthenticator`
```{eval-rst}
.. autoconfigurable:: DummyAuthenticator
```

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