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20 Commits

Author SHA1 Message Date
Min RK
69bb34b943 release 1.2.2 2020-11-27 14:44:42 +01:00
Min RK
728fbc68e0 Merge pull request #3285 from meeseeksmachine/auto-backport-of-pr-3284-on-1.2.x
Backport PR #3284 on branch 1.2.x (Changelog for 1.2.2)
2020-11-27 14:41:45 +01:00
Min RK
0dad9a3f39 Backport PR #3284: Changelog for 1.2.2 2020-11-27 13:41:32 +00:00
Min RK
41f291c0c9 Merge pull request #3282 from meeseeksmachine/auto-backport-of-pr-3257-on-1.2.x
Backport PR #3257 on branch 1.2.x (Update services-basics.md to use jupyterhub_idle_culler)
2020-11-27 10:05:01 +01:00
Min RK
9a5b11d5e1 Merge pull request #3283 from meeseeksmachine/auto-backport-of-pr-3250-on-1.2.x
Backport PR #3250 on branch 1.2.x (remove push-branch conditions for CI)
2020-11-27 10:04:11 +01:00
Erik Sundell
b47159b31e Backport PR #3250: remove push-branch conditions for CI 2020-11-27 09:03:54 +00:00
Erik Sundell
bbe377b70a Backport PR #3257: Update services-basics.md to use jupyterhub_idle_culler 2020-11-27 08:59:11 +00:00
Min RK
374a3a7b36 Merge pull request #3273 from meeseeksmachine/auto-backport-of-pr-3237-on-1.2.x
Backport PR #3237 on branch 1.2.x ([proxy.py] Improve robustness when detecting and closing existing proxy processes)
2020-11-26 10:01:46 +01:00
Min RK
32c493e5ab Merge pull request #3272 from meeseeksmachine/auto-backport-of-pr-3252-on-1.2.x
Backport PR #3252 on branch 1.2.x (Standardize "Sign in" capitalization on the login page)
2020-11-20 10:34:41 +01:00
Min RK
edfd363758 Merge pull request #3271 from meeseeksmachine/auto-backport-of-pr-3265-on-1.2.x
Backport PR #3265 on branch 1.2.x (Fix RootHandler when default_url is a callable)
2020-11-20 10:34:31 +01:00
Min RK
d72a5ca3e4 Merge pull request #3274 from meeseeksmachine/auto-backport-of-pr-3255-on-1.2.x
Backport PR #3255 on branch 1.2.x (Environment marker on pamela)
2020-11-20 10:34:22 +01:00
Min RK
3a6309a570 Backport PR #3255: Environment marker on pamela 2020-11-20 09:17:45 +00:00
Min RK
588407200f Backport PR #3237: [proxy.py] Improve robustness when detecting and closing existing proxy processes 2020-11-20 09:17:08 +00:00
Min RK
5cc36a6809 Backport PR #3252: Standardize "Sign in" capitalization on the login page 2020-11-20 09:16:57 +00:00
Min RK
5733eb76c2 Backport PR #3265: Fix RootHandler when default_url is a callable 2020-11-20 09:15:46 +00:00
Min RK
d9719e3538 Merge pull request #3269 from meeseeksmachine/auto-backport-of-pr-3261-on-1.2.x
Backport PR #3261 on branch 1.2.x (Only preserve params when ?next= is unspecified)
2020-11-20 10:10:43 +01:00
Min RK
7c91fbea93 Merge pull request #3270 from meeseeksmachine/auto-backport-of-pr-3246-on-1.2.x
Backport PR #3246 on branch 1.2.x (Migrate from travis to GitHub actions)
2020-11-20 10:10:30 +01:00
Min RK
5076745085 back to dev 2020-11-20 09:54:14 +01:00
Min RK
39eea2f053 Backport PR #3246: Migrate from travis to GitHub actions 2020-11-20 08:51:59 +00:00
Min RK
998f5d7b6c Backport PR #3261: Only preserve params when ?next= is unspecified 2020-11-20 08:48:02 +00:00
291 changed files with 6252 additions and 30166 deletions

33
.circleci/config.yml Normal file
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@@ -0,0 +1,33 @@
# Python CircleCI 2.0 configuration file
# Updating CircleCI configuration from v1 to v2
# Check https://circleci.com/docs/2.0/language-python/ for more details
#
version: 2
jobs:
build:
machine: true
steps:
- checkout
- run:
name: build images
command: |
docker build -t jupyterhub/jupyterhub .
docker build -t jupyterhub/jupyterhub-onbuild onbuild
docker build -t jupyterhub/jupyterhub:alpine -f dockerfiles/Dockerfile.alpine .
docker build -t jupyterhub/singleuser singleuser
- run:
name: smoke test jupyterhub
command: |
docker run --rm -it jupyterhub/jupyterhub jupyterhub --help
- run:
name: verify static files
command: |
docker run --rm -it -v $PWD/dockerfiles:/io jupyterhub/jupyterhub python3 /io/test.py
# Tell CircleCI to use this workflow when it builds the site
workflows:
version: 2
default:
jobs:
- build

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@@ -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"

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@@ -1,221 +0,0 @@
# This is a GitHub workflow defining a set of jobs with a set of steps.
# ref: https://docs.github.com/en/actions/learn-github-actions/workflow-syntax-for-github-actions
#
# Test build release artifacts (PyPI package, Docker images) and publish them on
# pushed git tags.
#
name: Release
on:
pull_request:
paths-ignore:
- "docs/**"
- "**.md"
- "**.rst"
- ".github/workflows/*"
- "!.github/workflows/release.yml"
push:
paths-ignore:
- "docs/**"
- "**.md"
- "**.rst"
- ".github/workflows/*"
- "!.github/workflows/release.yml"
branches-ignore:
- "dependabot/**"
- "pre-commit-ci-update-config"
tags:
- "**"
workflow_dispatch:
jobs:
build-release:
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: "3.9"
- uses: actions/setup-node@v3
with:
node-version: "14"
- name: install build requirements
run: |
npm install -g yarn
pip install --upgrade pip
pip install build
pip freeze
- name: build release
run: |
python -m build --sdist --wheel .
ls -l dist
- name: verify sdist
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
# ref: https://github.com/actions/upload-artifact#readme
- uses: actions/upload-artifact@v3
with:
name: jupyterhub-${{ github.sha }}
path: "dist/*"
if-no-files-found: error
- name: Publish to PyPI
if: startsWith(github.ref, 'refs/tags/')
env:
TWINE_USERNAME: __token__
TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
run: |
pip install twine
twine upload --skip-existing dist/*
publish-docker:
runs-on: ubuntu-20.04
services:
# So that we can test this in PRs/branches
local-registry:
image: registry:2
ports:
- 5000:5000
steps:
- name: Should we push this image to a public registry?
run: |
if [ "${{ startsWith(github.ref, 'refs/tags/') || (github.ref == 'refs/heads/main') }}" = "true" ]; then
# Empty => Docker Hub
echo "REGISTRY=" >> $GITHUB_ENV
else
echo "REGISTRY=localhost:5000/" >> $GITHUB_ENV
fi
- uses: actions/checkout@v3
# Setup docker to build for multiple platforms, see:
# https://github.com/docker/build-push-action/tree/v2.4.0#usage
# https://github.com/docker/build-push-action/blob/v2.4.0/docs/advanced/multi-platform.md
- name: Set up QEMU (for docker buildx)
uses: docker/setup-qemu-action@8b122486cedac8393e77aa9734c3528886e4a1a8 # associated tag: v1.0.2
- name: Set up Docker Buildx (for multi-arch builds)
uses: docker/setup-buildx-action@dc7b9719a96d48369863986a06765841d7ea23f6 # associated tag: v1.1.2
with:
# Allows pushing to registry on localhost:5000
driver-opts: network=host
- name: Setup push rights to Docker Hub
# This was setup by...
# 1. Creating a Docker Hub service account "jupyterhubbot"
# 2. Creating a access token for the service account specific to this
# repository: https://hub.docker.com/settings/security
# 3. Making the account part of the "bots" team, and granting that team
# permissions to push to the relevant images:
# https://hub.docker.com/orgs/jupyterhub/teams/bots/permissions
# 4. Registering the username and token as a secret for this repo:
# https://github.com/jupyterhub/jupyterhub/settings/secrets/actions
if: env.REGISTRY != 'localhost:5000/'
run: |
docker login -u "${{ secrets.DOCKERHUB_USERNAME }}" -p "${{ secrets.DOCKERHUB_TOKEN }}"
# image: jupyterhub/jupyterhub
#
# https://github.com/jupyterhub/action-major-minor-tag-calculator
# If this is a tagged build this will return additional parent tags.
# E.g. 1.2.3 is expanded to Docker tags
# [{prefix}:1.2.3, {prefix}:1.2, {prefix}:1, {prefix}:latest] unless
# this is a backported tag in which case the newer tags aren't updated.
# For branches this will return the branch name.
# If GITHUB_TOKEN isn't available (e.g. in PRs) returns no tags [].
- name: Get list of jupyterhub tags
id: jupyterhubtags
uses: jupyterhub/action-major-minor-tag-calculator@v2
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/jupyterhub:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub
uses: docker/build-push-action@1cb9d22b932e4832bb29793b7777ec860fc1cde0
with:
context: .
platforms: linux/amd64,linux/arm64
push: true
# tags parameter must be a string input so convert `gettags` JSON
# array into a comma separated list of tags
tags: ${{ join(fromJson(steps.jupyterhubtags.outputs.tags)) }}
# image: jupyterhub/jupyterhub-onbuild
#
- name: Get list of jupyterhub-onbuild tags
id: onbuildtags
uses: jupyterhub/action-major-minor-tag-calculator@v2
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/jupyterhub-onbuild:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub-onbuild:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub-onbuild
uses: docker/build-push-action@1cb9d22b932e4832bb29793b7777ec860fc1cde0
with:
build-args: |
BASE_IMAGE=${{ fromJson(steps.jupyterhubtags.outputs.tags)[0] }}
context: onbuild
platforms: linux/amd64,linux/arm64
push: true
tags: ${{ join(fromJson(steps.onbuildtags.outputs.tags)) }}
# image: jupyterhub/jupyterhub-demo
#
- name: Get list of jupyterhub-demo tags
id: demotags
uses: jupyterhub/action-major-minor-tag-calculator@v2
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/jupyterhub-demo:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub-demo:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub-demo
uses: docker/build-push-action@1cb9d22b932e4832bb29793b7777ec860fc1cde0
with:
build-args: |
BASE_IMAGE=${{ fromJson(steps.onbuildtags.outputs.tags)[0] }}
context: demo-image
# linux/arm64 currently fails:
# ERROR: Could not build wheels for argon2-cffi which use PEP 517 and cannot be installed directly
# ERROR: executor failed running [/bin/sh -c python3 -m pip install notebook]: exit code: 1
platforms: linux/amd64
push: true
tags: ${{ join(fromJson(steps.demotags.outputs.tags)) }}
# image: jupyterhub/singleuser
#
- name: Get list of jupyterhub/singleuser tags
id: singleusertags
uses: jupyterhub/action-major-minor-tag-calculator@v2
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/singleuser:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/singleuser:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub/singleuser
uses: docker/build-push-action@1cb9d22b932e4832bb29793b7777ec860fc1cde0
with:
build-args: |
JUPYTERHUB_VERSION=${{ github.ref_type == 'tag' && github.ref_name || format('git:{0}', github.sha) }}
context: singleuser
platforms: linux/amd64,linux/arm64
push: true
tags: ${{ join(fromJson(steps.singleusertags.outputs.tags)) }}

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@@ -1,31 +0,0 @@
# https://github.com/dessant/support-requests
name: "Support Requests"
on:
issues:
types: [labeled, unlabeled, reopened]
permissions:
issues: write
jobs:
action:
runs-on: ubuntu-latest
steps:
- uses: dessant/support-requests@v2
with:
github-token: ${{ github.token }}
support-label: "support"
issue-comment: |
Hi there @{issue-author} :wave:!
I closed this issue because it was labelled as a support question.
Please help us organize discussion by posting this on the http://discourse.jupyter.org/ forum.
Our goal is to sustain a positive experience for both users and developers. We use GitHub issues for specific discussions related to changing a repository's content, and let the forum be where we can more generally help and inspire each other.
Thanks you for being an active member of our community! :heart:
close-issue: true
lock-issue: false
issue-lock-reason: "off-topic"

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@@ -1,62 +0,0 @@
# This is a GitHub workflow defining a set of jobs with a set of steps.
# ref: https://docs.github.com/en/actions/learn-github-actions/workflow-syntax-for-github-actions
#
# This workflow validates the REST API definition and runs the pytest tests in
# the docs/ folder. This workflow does not build the documentation. That is
# instead tested via ReadTheDocs (https://readthedocs.org/projects/jupyterhub/).
#
name: Test docs
# The tests defined in docs/ are currently influenced by changes to _version.py
# and scopes.py.
on:
pull_request:
paths:
- "docs/**"
- "jupyterhub/_version.py"
- "jupyterhub/scopes.py"
- ".github/workflows/test-docs.yml"
push:
paths:
- "docs/**"
- "jupyterhub/_version.py"
- "jupyterhub/scopes.py"
- ".github/workflows/test-docs.yml"
branches-ignore:
- "dependabot/**"
- "pre-commit-ci-update-config"
tags:
- "**"
workflow_dispatch:
env:
# UTF-8 content may be interpreted as ascii and causes errors without this.
LANG: C.UTF-8
PYTEST_ADDOPTS: "--verbose --color=yes"
jobs:
validate-rest-api-definition:
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v3
- name: Validate REST API definition
uses: char0n/swagger-editor-validate@v1.3.1
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
with:
python-version: "3.9"
- name: Install requirements
run: |
pip install -r docs/requirements.txt pytest -e .
- name: pytest docs/
run: |
pytest docs/

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@@ -1,52 +0,0 @@
# This is a GitHub workflow defining a set of jobs with a set of steps.
# ref: https://docs.github.com/en/actions/learn-github-actions/workflow-syntax-for-github-actions
#
name: Test jsx (admin-react.js)
on:
pull_request:
paths:
- "jsx/**"
- ".github/workflows/test-jsx.yml"
push:
paths:
- "jsx/**"
- ".github/workflows/test-jsx.yml"
branches-ignore:
- "dependabot/**"
- "pre-commit-ci-update-config"
tags:
- "**"
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
# tests also has tests that this job is meant to run with `yarn test`
# according to the documentation in jsx/README.md.
test-jsx-admin-react:
runs-on: ubuntu-20.04
timeout-minutes: 5
steps:
- uses: actions/checkout@v3
- uses: actions/setup-node@v3
with:
node-version: "14"
- name: Install yarn
run: |
npm install -g yarn
- name: yarn
run: |
cd jsx
yarn
- name: yarn test
run: |
cd jsx
yarn test

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@@ -1,43 +1,60 @@
# This is a GitHub workflow defining a set of jobs with a set of steps.
# ref: https://docs.github.com/en/actions/learn-github-actions/workflow-syntax-for-github-actions
# ref: https://docs.github.com/en/free-pro-team@latest/actions/reference/workflow-syntax-for-github-actions
#
name: Test
name: Run tests
# Trigger the workflow's on all PRs but only on pushed tags or commits to
# main/master branch to avoid PRs developed in a GitHub fork's dedicated branch
# to trigger.
on:
pull_request:
paths-ignore:
- "docs/**"
- "**.md"
- "**.rst"
- ".github/workflows/*"
- "!.github/workflows/test.yml"
push:
paths-ignore:
- "docs/**"
- "**.md"
- "**.rst"
- ".github/workflows/*"
- "!.github/workflows/test.yml"
branches-ignore:
- "dependabot/**"
- "pre-commit-ci-update-config"
tags:
- "**"
workflow_dispatch:
defaults:
run:
# Declare bash be used by default in this workflow's "run" steps.
#
# NOTE: bash will by default run with:
# --noprofile: Ignore ~/.profile etc.
# --norc: Ignore ~/.bashrc etc.
# -e: Exit directly on errors
# -o pipefail: Don't mask errors from a command piped into another command
shell: bash
env:
# UTF-8 content may be interpreted as ascii and causes errors without this.
LANG: C.UTF-8
PYTEST_ADDOPTS: "--verbose --color=yes"
permissions:
contents: read
jobs:
# Run "pre-commit run --all-files"
pre-commit:
runs-on: ubuntu-20.04
timeout-minutes: 2
steps:
- uses: actions/checkout@v2
- uses: actions/setup-python@v2
with:
python-version: 3.8
# ref: https://github.com/pre-commit/action
- uses: pre-commit/action@v2.0.0
- name: Help message if pre-commit fail
if: ${{ failure() }}
run: |
echo "You can install pre-commit hooks to automatically run formatting"
echo "on each commit with:"
echo " pre-commit install"
echo "or you can run by hand on staged files with"
echo " pre-commit run"
echo "or after-the-fact on already committed files with"
echo " pre-commit run --all-files"
# Run "pytest jupyterhub/tests" in various configurations
pytest:
runs-on: ubuntu-20.04
timeout-minutes: 15
timeout-minutes: 10
strategy:
# Keep running even if one variation of the job fail
@@ -56,40 +73,27 @@ jobs:
# Tests everything when JupyterHub works against a dedicated mysql or
# postgresql server.
#
# legacy_notebook:
# jupyter_server:
# Tests everything when the user instances are started with
# the legacy notebook server instead of jupyter_server.
#
# ssl:
# Tests everything using internal SSL connections instead of
# unencrypted HTTP
# jupyter_server instead of notebook.
#
# main_dependencies:
# Tests everything when the we use the latest available dependencies
# from: traitlets.
# from: ipytraitlets.
#
# 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.
include:
- python: "3.7"
oldest_dependencies: oldest_dependencies
legacy_notebook: legacy_notebook
- python: "3.8"
legacy_notebook: legacy_notebook
- python: "3.9"
db: mysql
- python: "3.10"
db: postgres
- python: "3.10"
- python: "3.6"
subdomain: subdomain
- python: "3.10"
ssl: ssl
# can't test 3.11.0-beta.4 until a greenlet release
# greenlet is a dependency of sqlalchemy on linux
# see https://github.com/gevent/gevent/issues/1867
# - python: "3.11.0-beta.4"
- python: "3.10"
- python: "3.7"
db: mysql
- python: "3.8"
db: postgres
- python: "3.8"
jupyter_server: jupyter_server
- python: "3.9"
main_dependencies: main_dependencies
steps:
@@ -105,9 +109,6 @@ jobs:
echo "MYSQL_HOST=127.0.0.1" >> $GITHUB_ENV
echo "JUPYTERHUB_TEST_DB_URL=mysql+mysqlconnector://root@127.0.0.1:3306/jupyterhub" >> $GITHUB_ENV
fi
if [ "${{ matrix.ssl }}" == "ssl" ]; then
echo "SSL_ENABLED=1" >> $GITHUB_ENV
fi
if [ "${{ matrix.db }}" == "postgres" ]; then
echo "PGHOST=127.0.0.1" >> $GITHUB_ENV
echo "PGUSER=test_user" >> $GITHUB_ENV
@@ -117,44 +118,37 @@ jobs:
if [ "${{ 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: ${{ matrix.python }}
- name: Install Python dependencies
run: |
pip install --upgrade pip
pip install --upgrade . -r dev-requirements.txt
if [ "${{ matrix.oldest_dependencies }}" != "" ]; then
# take any dependencies in requirements.txt such as tornado>=5.0
# and transform them to tornado==5.0 so we can run tests with
# the earliest-supported versions
cat requirements.txt | grep '>=' | sed -e 's@>=@==@g' > oldest-requirements.txt
pip install -r oldest-requirements.txt
fi
if [ "${{ matrix.main_dependencies }}" != "" ]; then
pip install git+https://github.com/ipython/traitlets#egg=traitlets --force
fi
if [ "${{ matrix.legacy_notebook }}" != "" ]; then
pip uninstall jupyter_server --yes
pip install 'notebook<7'
if [ "${{ matrix.jupyter_server }}" != "" ]; then
pip uninstall notebook --yes
pip install jupyter_server
fi
if [ "${{ matrix.db }}" == "mysql" ]; then
pip install mysql-connector-python
@@ -190,47 +184,23 @@ jobs:
if: ${{ matrix.db }}
run: |
if [ "${{ matrix.db }}" == "mysql" ]; then
if [[ -z "$(which mysql)" ]]; then
sudo apt-get update
sudo apt-get install -y mysql-client
fi
DB=mysql bash ci/docker-db.sh
DB=mysql bash ci/init-db.sh
fi
if [ "${{ matrix.db }}" == "postgres" ]; then
if [[ -z "$(which psql)" ]]; then
sudo apt-get update
sudo apt-get install -y postgresql-client
fi
DB=postgres bash ci/docker-db.sh
DB=postgres bash ci/init-db.sh
fi
- name: Run pytest
# FIXME: --color=yes explicitly set because:
# https://github.com/actions/runner/issues/241
run: |
pytest --maxfail=2 --cov=jupyterhub jupyterhub/tests
pytest -v --maxfail=2 --color=yes --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
- name: build images
run: |
docker build -t jupyterhub/jupyterhub .
docker build -t jupyterhub/jupyterhub-onbuild onbuild
docker build -t jupyterhub/jupyterhub:alpine -f dockerfiles/Dockerfile.alpine .
docker build -t jupyterhub/singleuser singleuser
- name: smoke test jupyterhub
run: |
docker run --rm -t jupyterhub/jupyterhub jupyterhub --help
- name: verify static files
run: |
docker run --rm -t -v $PWD/dockerfiles:/io jupyterhub/jupyterhub python3 /io/test.py

6
.gitignore vendored
View File

@@ -8,9 +8,7 @@ dist
docs/_build
docs/build
docs/source/_static/rest-api
docs/source/rbac/scope-table.md
.ipynb_checkpoints
jsx/build/
# ignore config file at the top-level of the repo
# but not sub-dirs
/jupyterhub_config.py
@@ -20,7 +18,6 @@ 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
@@ -31,6 +28,3 @@ htmlcov
.pytest_cache
pip-wheel-metadata
docs/source/reference/metrics.rst
oldest-requirements.txt
jupyterhub-proxy.pid
examples/server-api/service-token

View File

@@ -1,52 +1,19 @@
# pre-commit is a tool to perform a predefined set of tasks manually and/or
# automatically before git commits are made.
#
# Config reference: https://pre-commit.com/#pre-commit-configyaml---top-level
#
# Common tasks
#
# - Run on all files: pre-commit run --all-files
# - Register git hooks: pre-commit install --install-hooks
#
repos:
# Autoformat: Python code, syntax patterns are modernized
- repo: https://github.com/asottile/pyupgrade
rev: v2.37.3
hooks:
- id: pyupgrade
args:
- --py36-plus
# Autoformat: Python code
- repo: https://github.com/pycqa/isort
rev: 5.10.1
hooks:
- id: isort
# Autoformat: Python code
- repo: https://github.com/psf/black
rev: 22.6.0
hooks:
- id: black
# Autoformat: markdown, yaml, javascript (see the file .prettierignore)
- repo: https://github.com/pre-commit/mirrors-prettier
rev: v2.7.1
hooks:
- id: prettier
# Autoformat and linting, misc. details
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.3.0
hooks:
- id: end-of-file-fixer
exclude: share/jupyterhub/static/js/admin-react.js
- id: requirements-txt-fixer
- id: check-case-conflict
- id: check-executables-have-shebangs
# Linting: Python code (see the file .flake8)
- repo: https://github.com/PyCQA/flake8
rev: "5.0.2"
hooks:
- id: flake8
- repo: https://github.com/asottile/reorder_python_imports
rev: v1.9.0
hooks:
- id: reorder-python-imports
- repo: https://github.com/psf/black
rev: 19.10b0
hooks:
- id: black
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v2.4.0
hooks:
- id: end-of-file-fixer
- id: check-json
- id: check-yaml
- id: check-case-conflict
- id: check-executables-have-shebangs
- id: requirements-txt-fixer
- id: flake8

View File

@@ -1,2 +0,0 @@
share/jupyterhub/templates/
share/jupyterhub/static/js/admin-react.js

26
CHECKLIST-Release.md Normal file
View File

@@ -0,0 +1,26 @@
# Release checklist
- [ ] Upgrade Docs prior to Release
- [ ] Change log
- [ ] New features documented
- [ ] Update the contributor list - thank you page
- [ ] Upgrade and test Reference Deployments
- [ ] Release software
- [ ] Make sure 0 issues in milestone
- [ ] Follow release process steps
- [ ] Send builds to PyPI (Warehouse) and Conda Forge
- [ ] Blog post and/or release note
- [ ] Notify users of release
- [ ] Email Jupyter and Jupyter In Education mailing lists
- [ ] Tweet (optional)
- [ ] Increment the version number for the next release
- [ ] Update roadmap

View File

@@ -1 +1 @@
Please refer to [Project Jupyter's Code of Conduct](https://github.com/jupyter/governance/blob/HEAD/conduct/code_of_conduct.md).
Please refer to [Project Jupyter's Code of Conduct](https://github.com/jupyter/governance/blob/master/conduct/code_of_conduct.md).

View File

@@ -1,14 +1,137 @@
# Contributing to JupyterHub
Welcome! As a [Jupyter](https://jupyter.org) project,
you can follow the [Jupyter contributor guide](https://jupyter.readthedocs.io/en/latest/contributing/content-contributor.html).
you can follow the [Jupyter contributor guide](https://jupyter.readthedocs.io/en/latest/contributor/content-contributor.html).
Make sure to also follow [Project Jupyter's Code of Conduct](https://github.com/jupyter/governance/blob/HEAD/conduct/code_of_conduct.md)
Make sure to also follow [Project Jupyter's Code of Conduct](https://github.com/jupyter/governance/blob/master/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

@@ -24,7 +24,7 @@ software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
@@ -46,8 +46,8 @@ Jupyter uses a shared copyright model. Each contributor maintains copyright
over their contributions to Jupyter. But, it is important to note that these
contributions are typically only changes to the repositories. Thus, the Jupyter
source code, in its entirety is not the copyright of any single person or
institution. Instead, it is the collective copyright of the entire Jupyter
Development Team. If individual contributors want to maintain a record of what
institution. Instead, it is the collective copyright of the entire Jupyter
Development Team. If individual contributors want to maintain a record of what
changes/contributions they have specific copyright on, they should indicate
their copyright in the commit message of the change, when they commit the
change to one of the Jupyter repositories.

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@sha256:6f2fb2f9fb5582f8b587837afd6ea8f37d8d1d9e41168c90f410a6ef15fa8ce5
FROM $BASE_IMAGE AS builder
USER root
@@ -37,7 +37,6 @@ RUN apt-get update \
python3-pycurl \
nodejs \
npm \
yarn \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*

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

@@ -6,37 +6,29 @@
**[License](#license)** |
**[Help and Resources](#help-and-resources)**
---
Please note that this repository is participating in a study into the sustainability of open source projects. Data will be gathered about this repository for approximately the next 12 months, starting from 2021-06-11.
Data collected will include the number of contributors, number of PRs, time taken to close/merge these PRs, and issues closed.
For more information, please visit
[our informational page](https://sustainable-open-science-and-software.github.io/) or download our [participant information sheet](https://sustainable-open-science-and-software.github.io/assets/PIS_sustainable_software.pdf).
---
# [JupyterHub](https://github.com/jupyterhub/jupyterhub)
[![Latest PyPI version](https://img.shields.io/pypi/v/jupyterhub?logo=pypi)](https://pypi.python.org/pypi/jupyterhub)
[![Latest conda-forge version](https://img.shields.io/conda/vn/conda-forge/jupyterhub?logo=conda-forge)](https://anaconda.org/conda-forge/jupyterhub)
[![Latest conda-forge version](https://img.shields.io/conda/vn/conda-forge/jupyterhub?logo=conda-forge)](https://www.npmjs.com/package/jupyterhub)
[![Documentation build status](https://img.shields.io/readthedocs/jupyterhub?logo=read-the-docs)](https://jupyterhub.readthedocs.org/en/latest/)
[![GitHub Workflow Status - Test](https://img.shields.io/github/workflow/status/jupyterhub/jupyterhub/Test?logo=github&label=tests)](https://github.com/jupyterhub/jupyterhub/actions)
[![TravisCI build status](https://img.shields.io/travis/com/jupyterhub/jupyterhub?logo=travis)](https://travis-ci.com/jupyterhub/jupyterhub)
[![DockerHub build status](https://img.shields.io/docker/build/jupyterhub/jupyterhub?logo=docker&label=build)](https://hub.docker.com/r/jupyterhub/jupyterhub/tags)
[![Test coverage of code](https://codecov.io/gh/jupyterhub/jupyterhub/branch/main/graph/badge.svg)](https://codecov.io/gh/jupyterhub/jupyterhub)
[![CircleCI build status](https://img.shields.io/circleci/build/github/jupyterhub/jupyterhub?logo=circleci)](https://circleci.com/gh/jupyterhub/jupyterhub)<!-- CircleCI Token: b5b65862eb2617b9a8d39e79340b0a6b816da8cc -->
[![Test coverage of code](https://codecov.io/gh/jupyterhub/jupyterhub/branch/master/graph/badge.svg)](https://codecov.io/gh/jupyterhub/jupyterhub)
[![GitHub](https://img.shields.io/badge/issue_tracking-github-blue?logo=github)](https://github.com/jupyterhub/jupyterhub/issues)
[![Discourse](https://img.shields.io/badge/help_forum-discourse-blue?logo=discourse)](https://discourse.jupyter.org/c/jupyterhub)
[![Gitter](https://img.shields.io/badge/social_chat-gitter-blue?logo=gitter)](https://gitter.im/jupyterhub/jupyterhub)
With [JupyterHub](https://jupyterhub.readthedocs.io) you can create a
**multi-user Hub** that spawns, manages, and proxies multiple instances of the
**multi-user Hub** which spawns, manages, and proxies multiple instances of the
single-user [Jupyter notebook](https://jupyter-notebook.readthedocs.io)
server.
[Project Jupyter](https://jupyter.org) created JupyterHub to support many
users. The Hub can offer notebook servers to a class of students, a corporate
data science workgroup, a scientific research project, or a high-performance
data science workgroup, a scientific research project, or a high performance
computing group.
## Technical overview
@@ -50,30 +42,37 @@ Three main actors make up JupyterHub:
Basic principles for operation are:
- Hub launches a proxy.
- The Proxy forwards all requests to Hub by default.
- Hub handles login and spawns single-user servers on demand.
- Hub configures proxy to forward URL prefixes to the single-user notebook
- Proxy forwards all requests to Hub by default.
- Hub handles login, and spawns single-user servers on demand.
- Hub configures proxy to forward url prefixes to the single-user notebook
servers.
JupyterHub also provides a
[REST API][]
[REST API](http://petstore.swagger.io/?url=https://raw.githubusercontent.com/jupyter/jupyterhub/master/docs/rest-api.yml#/default)
for administration of the Hub and its users.
[rest api]: https://jupyterhub.readthedocs.io/en/latest/reference/rest-api.html
## Installation
### Check prerequisites
- A Linux/Unix based system
- [Python](https://www.python.org/downloads/) 3.6 or greater
- [Python](https://www.python.org/downloads/) 3.5 or greater
- [nodejs/npm](https://www.npmjs.com/)
- If you are using **`conda`**, the nodejs and npm dependencies will be installed for
* If you are using **`conda`**, the nodejs and npm dependencies will be installed for
you by conda.
- If you are using **`pip`**, install a recent version (at least 12.0) of
* If you are using **`pip`**, install a recent version of
[nodejs/npm](https://docs.npmjs.com/getting-started/installing-node).
For example, install it on Linux (Debian/Ubuntu) using:
```
sudo apt-get install npm nodejs-legacy
```
The `nodejs-legacy` package installs the `node` executable and is currently
required for npm to work on Debian/Ubuntu.
- If using the default PAM Authenticator, a [pluggable authentication module (PAM)](https://en.wikipedia.org/wiki/Pluggable_authentication_module).
- TLS certificate and key for HTTPS communication
@@ -89,11 +88,12 @@ To install JupyterHub along with its dependencies including nodejs/npm:
conda install -c conda-forge jupyterhub
```
If you plan to run notebook servers locally, install JupyterLab or Jupyter notebook:
If you plan to run notebook servers locally, install the Jupyter notebook
or JupyterLab:
```bash
conda install jupyterlab
conda install notebook
conda install jupyterlab
```
#### Using `pip`
@@ -102,13 +102,13 @@ JupyterHub can be installed with `pip`, and the proxy with `npm`:
```bash
npm install -g configurable-http-proxy
python3 -m pip install jupyterhub
python3 -m pip install jupyterhub
```
If you plan to run notebook servers locally, you will need to install
[JupyterLab or Jupyter notebook](https://jupyter.readthedocs.io/en/latest/install.html):
If you plan to run notebook servers locally, you will need to install the
[Jupyter notebook](https://jupyter.readthedocs.io/en/latest/install.html)
package:
python3 -m pip install --upgrade jupyterlab
python3 -m pip install --upgrade notebook
### Run the Hub server
@@ -117,12 +117,13 @@ To start the Hub server, run the command:
jupyterhub
Visit `http://localhost:8000` in your browser, and sign in with your system username and password.
Visit `https://localhost:8000` in your browser, and sign in with your unix
PAM credentials.
_Note_: To allow multiple users to sign in to the server, you will need to
run the `jupyterhub` command as a _privileged user_, such as root.
*Note*: To allow multiple users to sign into the server, you will need to
run the `jupyterhub` command as a *privileged user*, such as root.
The [wiki](https://github.com/jupyterhub/jupyterhub/wiki/Using-sudo-to-run-JupyterHub-without-root-privileges)
describes how to run the server as a _less privileged user_, which requires
describes how to run the server as a *less privileged user*, which requires
more configuration of the system.
## Configuration
@@ -141,7 +142,7 @@ To generate a default config file with settings and descriptions:
### Start the Hub
To start the Hub on a specific url and port `10.0.1.2:443` with **https**:
To start the Hub on a specific url and port ``10.0.1.2:443`` with **https**:
jupyterhub --ip 10.0.1.2 --port 443 --ssl-key my_ssl.key --ssl-cert my_ssl.cert
@@ -203,7 +204,7 @@ These accounts will be used for authentication in JupyterHub's default configura
## Contributing
If you would like to contribute to the project, please read our
[contributor documentation](https://jupyter.readthedocs.io/en/latest/contributing/content-contributor.html)
[contributor documentation](http://jupyter.readthedocs.io/en/latest/contributor/content-contributor.html)
and the [`CONTRIBUTING.md`](CONTRIBUTING.md). The `CONTRIBUTING.md` file
explains how to set up a development installation, how to run the test suite,
and how to contribute to documentation.
@@ -230,17 +231,18 @@ docker container or Linux VM.
We use a shared copyright model that enables all contributors to maintain the
copyright on their contributions.
All code is licensed under the terms of the [revised BSD license](./COPYING.md).
All code is licensed under the terms of the revised BSD license.
## Help and resources
We encourage you to ask questions and share ideas on the [Jupyter community forum](https://discourse.jupyter.org/).
You can also talk with us on our JupyterHub [Gitter](https://gitter.im/jupyterhub/jupyterhub) channel.
We encourage you to ask questions on the [Jupyter mailing list](https://groups.google.com/forum/#!forum/jupyter).
To participate in development discussions or get help, talk with us on
our JupyterHub [Gitter](https://gitter.im/jupyterhub/jupyterhub) channel.
- [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/) | [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 JupyterHub's REST API](http://petstore.swagger.io/?url=https://raw.githubusercontent.com/jupyter/jupyterhub/master/docs/rest-api.yml#/default)
- [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

@@ -1,50 +0,0 @@
# 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.
For you to follow along according to these instructions, you need:
- To have push rights to the [jupyterhub GitHub
repository](https://github.com/jupyterhub/jupyterhub).
## Steps to make a release
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
```
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
tbump ${VERSION}
```
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`
```shell
tbump --no-tag ${NEXT_VERSION}.dev
```
1. Following the release to PyPI, an automated PR should arrive to
[conda-forge/jupyterhub-feedstock][],
check for the tests to succeed on this PR and then merge it to successfully
update the package for `conda` on the conda-forge channel.
[github-activity]: https://github.com/choldgraf/github-activity
[conda-forge/jupyterhub-feedstock]: https://github.com/conda-forge/jupyterhub-feedstock

View File

@@ -1,5 +0,0 @@
# Reporting a Vulnerability
If you believe youve found a security vulnerability in a Jupyter
project, please report it to security@ipython.org. If you prefer to
encrypt your security reports, you can use [this PGP public key](https://jupyter-notebook.readthedocs.io/en/stable/_downloads/1d303a645f2505a8fd283826fafc9908/ipython_security.asc).

View File

@@ -29,5 +29,5 @@ dependencies = package_json['dependencies']
for dep in dependencies:
src = join(node_modules, dep)
dest = join(components, dep)
print(f"{src} -> {dest}")
print("%s -> %s" % (src, dest))
shutil.copytree(src, dest)

View File

@@ -1,20 +0,0 @@
#!/usr/bin/env python
# Check that installed package contains everything we expect
import os
from jupyterhub._data import DATA_FILES_PATH
print("Checking jupyterhub._data")
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",
"static/js/admin-react.js",
):
path = os.path.join(DATA_FILES_PATH, subpath)
assert os.path.exists(path), path
print("OK")

View File

@@ -1,28 +0,0 @@
#!/usr/bin/env python
# Check that sdist contains everything we expect
import sys
import tarfile
from tarfile 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

@@ -20,7 +20,7 @@ fi
# Configure a set of databases in the database server for upgrade tests
set -x
for SUFFIX in '' _upgrade_100 _upgrade_122 _upgrade_130 _upgrade_150 _upgrade_211; do
for SUFFIX in '' _upgrade_072 _upgrade_081 _upgrade_094; do
$SQL_CLIENT "DROP DATABASE jupyterhub${SUFFIX};" 2>/dev/null || true
$SQL_CLIENT "CREATE DATABASE jupyterhub${SUFFIX} ${EXTRA_CREATE_DATABASE_ARGS:-};"
done

View File

@@ -15,10 +15,9 @@ This should only be used for demo or testing purposes!
It shouldn't be used as a base image to build on.
### Try it
1. `cd` to the root of your jupyterhub repo.
2. Build the demo image with `docker build -t jupyterhub-demo demo-image`.
2. Build the demo image with `docker build -t jupyterhub-demo demo-image`.
3. Run the demo image with `docker run -d -p 8000:8000 jupyterhub-demo`.

View File

@@ -7,18 +7,13 @@ codecov
coverage
cryptography
html5lib # needed for beautifulsoup
jupyterlab >=3
mock
# nbclassic provides the '/tree/' handler, which we use in tests
# it is a transitive dependency via jupyterlab,
# but depend on it directly
nbclassic
notebook
pre-commit
pytest>=3.3
pytest-asyncio>=0.17
pytest-asyncio
pytest-cov
pytest>=3.3
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

View File

@@ -1,14 +1,9 @@
FROM alpine:3.13
ENV LANG=en_US.UTF-8
RUN apk add --no-cache \
python3 \
py3-pip \
py3-ruamel.yaml \
py3-cryptography \
py3-sqlalchemy
FROM python:3.6.3-alpine3.6
ARG JUPYTERHUB_VERSION=0.8.1
ARG JUPYTERHUB_VERSION=1.3.0
RUN pip3 install --no-cache jupyterhub==${JUPYTERHUB_VERSION}
ENV LANG=en_US.UTF-8
USER nobody
CMD ["jupyterhub"]

View File

@@ -1,20 +1,20 @@
## What is Dockerfile.alpine
Dockerfile.alpine contains base image for jupyterhub. It does not work independently, but only as part of a full jupyterhub cluster
Dockerfile.alpine contains base image for jupyterhub. It does not work independently, but only as part of a full jupyterhub cluster
## How to use it?
1. A running configurable-http-proxy, whose API is accessible.
1. A running configurable-http-proxy, whose API is accessible.
2. A jupyterhub_config file.
3. Authentication and other libraries required by the specific jupyterhub_config file.
## Steps to test it outside a cluster
- start configurable-http-proxy in another container
- specify CONFIGPROXY_AUTH_TOKEN env in both containers
- put both containers on the same network (e.g. docker network create jupyterhub; docker run ... --net jupyterhub)
- tell jupyterhub where CHP is (e.g. c.ConfigurableHTTPProxy.api_url = 'http://chp:8001')
- tell jupyterhub not to start the proxy itself (c.ConfigurableHTTPProxy.should_start = False)
- Use dummy authenticator for ease of testing. Update following in jupyterhub_config file
- c.JupyterHub.authenticator_class = 'dummyauthenticator.DummyAuthenticator'
- c.DummyAuthenticator.password = "your strong password"
* start configurable-http-proxy in another container
* specify CONFIGPROXY_AUTH_TOKEN env in both containers
* put both containers on the same network (e.g. docker network create jupyterhub; docker run ... --net jupyterhub)
* tell jupyterhub where CHP is (e.g. c.ConfigurableHTTPProxy.api_url = 'http://chp:8001')
* tell jupyterhub not to start the proxy itself (c.ConfigurableHTTPProxy.should_start = False)
* Use 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

@@ -53,17 +53,20 @@ help:
clean:
rm -rf $(BUILDDIR)/*
node_modules: package.json
npm install && touch node_modules
rest-api: source/_static/rest-api/index.html
source/_static/rest-api/index.html: rest-api.yml node_modules
npm run rest-api
metrics: source/reference/metrics.rst
source/reference/metrics.rst: generate-metrics.py
python3 generate-metrics.py
scopes: source/rbac/scope-table.md
source/rbac/scope-table.md: source/rbac/generate-scope-table.py
python3 source/rbac/generate-scope-table.py
html: metrics scopes
html: rest-api metrics
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."

14
docs/package.json Normal file
View File

@@ -0,0 +1,14 @@
{
"name": "jupyterhub-docs-build",
"version": "0.8.0",
"description": "build JupyterHub swagger docs",
"scripts": {
"rest-api": "bootprint openapi ./rest-api.yml source/_static/rest-api"
},
"author": "",
"license": "BSD-3-Clause",
"devDependencies": {
"bootprint": "^1.0.0",
"bootprint-openapi": "^1.0.0"
}
}

View File

@@ -1,12 +1,12 @@
-r ../requirements.txt
alabaster_jupyterhub
autodoc-traits
myst-parser
pre-commit
# Temporary fix of #3021. Revert back to released autodoc-traits when
# 0.1.0 released.
https://github.com/jupyterhub/autodoc-traits/archive/75885ee24636efbfebfceed1043459715049cd84.zip
pydata-sphinx-theme
pytablewriter>=0.56
ruamel.yaml
sphinx>=1.7
recommonmark>=0.6
sphinx-copybutton
sphinx-jsonschema
sphinx>=1.7

881
docs/rest-api.yml Normal file
View File

@@ -0,0 +1,881 @@
# see me at: http://petstore.swagger.io/?url=https://raw.githubusercontent.com/jupyterhub/jupyterhub/master/docs/rest-api.yml#/default
swagger: '2.0'
info:
title: JupyterHub
description: The REST API for JupyterHub
version: 1.2.0dev
license:
name: BSD-3-Clause
schemes:
[http, https]
securityDefinitions:
token:
type: apiKey
name: Authorization
in: header
security:
- token: []
basePath: /hub/api
produces:
- application/json
consumes:
- application/json
paths:
/:
get:
summary: Get JupyterHub version
description: |
This endpoint is not authenticated for the purpose of clients and user
to identify the JupyterHub version before setting up authentication.
responses:
'200':
description: The JupyterHub version
schema:
type: object
properties:
version:
type: string
description: The version of JupyterHub itself
/info:
get:
summary: Get detailed info about JupyterHub
description: |
Detailed JupyterHub information, including Python version,
JupyterHub's version and executable path,
and which Authenticator and Spawner are active.
responses:
'200':
description: Detailed JupyterHub info
schema:
type: object
properties:
version:
type: string
description: The version of JupyterHub itself
python:
type: string
description: The Python version, as returned by sys.version
sys_executable:
type: string
description: The path to sys.executable running JupyterHub
authenticator:
type: object
properties:
class:
type: string
description: The Python class currently active for JupyterHub Authentication
version:
type: string
description: The version of the currently active Authenticator
spawner:
type: object
properties:
class:
type: string
description: The Python class currently active for spawning single-user notebook servers
version:
type: string
description: The version of the currently active Spawner
/users:
get:
summary: List users
responses:
'200':
description: The Hub's user list
schema:
type: array
items:
$ref: '#/definitions/User'
post:
summary: Create multiple users
parameters:
- name: body
in: body
required: true
schema:
type: object
properties:
usernames:
type: array
description: list of usernames to create on the Hub
items:
type: string
admin:
description: whether the created users should be admins
type: boolean
responses:
'201':
description: The users have been created
schema:
type: array
description: The created users
items:
$ref: '#/definitions/User'
/users/{name}:
get:
summary: Get a user by name
parameters:
- name: name
description: username
in: path
required: true
type: string
responses:
'200':
description: The User model
schema:
$ref: '#/definitions/User'
post:
summary: Create a single user
parameters:
- name: name
description: username
in: path
required: true
type: string
responses:
'201':
description: The user has been created
schema:
$ref: '#/definitions/User'
patch:
summary: Modify a user
description: Change a user's name or admin status
parameters:
- name: name
description: username
in: path
required: true
type: string
- name: body
in: body
required: true
description: Updated user info. At least one key to be updated (name or admin) is required.
schema:
type: object
properties:
name:
type: string
description: the new name (optional, if another key is updated i.e. admin)
admin:
type: boolean
description: update admin (optional, if another key is updated i.e. name)
responses:
'200':
description: The updated user info
schema:
$ref: '#/definitions/User'
delete:
summary: Delete a user
parameters:
- name: name
description: username
in: path
required: true
type: string
responses:
'204':
description: The user has been deleted
/users/{name}/activity:
post:
summary:
Notify Hub of activity for a given user.
description:
Notify the Hub of activity by the user,
e.g. accessing a service or (more likely)
actively using a server.
parameters:
- name: name
description: username
in: path
required: true
type: string
- name: body
in: body
schema:
type: object
properties:
last_activity:
type: string
format: date-time
description: |
Timestamp of last-seen activity for this user.
Only needed if this is not activity associated
with using a given server.
servers:
description: |
Register activity for specific servers by name.
The keys of this dict are the names of servers.
The default server has an empty name ('').
type: object
properties:
'<server name>':
description: |
Activity for a single server.
type: object
required:
- last_activity
properties:
last_activity:
type: string
format: date-time
description: |
Timestamp of last-seen activity on this server.
example:
last_activity: '2019-02-06T12:54:14Z'
servers:
'':
last_activity: '2019-02-06T12:54:14Z'
gpu:
last_activity: '2019-02-06T12:54:14Z'
responses:
'401':
$ref: '#/responses/Unauthorized'
'404':
description: No such user
/users/{name}/server:
post:
summary: Start a user's single-user notebook server
parameters:
- name: name
description: username
in: path
required: true
type: string
- name: options
description: |
Spawn options can be passed as a JSON body
when spawning via the API instead of spawn form.
The structure of the options
will depend on the Spawner's configuration.
The body itself will be available as `user_options` for the
Spawner.
in: body
required: false
schema:
type: object
responses:
'201':
description: The user's notebook server has started
'202':
description: The user's notebook server has not yet started, but has been requested
delete:
summary: Stop a user's server
parameters:
- name: name
description: username
in: path
required: true
type: string
responses:
'204':
description: The user's notebook server has stopped
'202':
description: The user's notebook server has not yet stopped as it is taking a while to stop
/users/{name}/servers/{server_name}:
post:
summary: Start a user's single-user named-server notebook server
parameters:
- name: name
description: username
in: path
required: true
type: string
- name: server_name
description: |
name given to a named-server.
Note that depending on your JupyterHub infrastructure there are chracterter size limitation to `server_name`. Default spawner with K8s pod will not allow Jupyter Notebooks to be spawned with a name that contains more than 253 characters (keep in mind that the pod will be spawned with extra characters to identify the user and hub).
in: path
required: true
type: string
- name: options
description: |
Spawn options can be passed as a JSON body
when spawning via the API instead of spawn form.
The structure of the options
will depend on the Spawner's configuration.
in: body
required: false
schema:
type: object
responses:
'201':
description: The user's notebook named-server has started
'202':
description: The user's notebook named-server has not yet started, but has been requested
delete:
summary: Stop a user's named-server
parameters:
- name: name
description: username
in: path
required: true
type: string
- name: server_name
description: name given to a named-server
in: path
required: true
type: string
- name: body
in: body
required: false
schema:
type: object
properties:
remove:
type: boolean
description: |
Whether to fully remove the server, rather than just stop it.
Removing a server deletes things like the state of the stopped server.
Default: false.
responses:
'204':
description: The user's notebook named-server has stopped
'202':
description: The user's notebook named-server has not yet stopped as it is taking a while to stop
/users/{name}/tokens:
parameters:
- name: name
description: username
in: path
required: true
type: string
get:
summary: List tokens for the user
responses:
'200':
description: The list of tokens
schema:
type: array
items:
$ref: '#/definitions/Token'
'401':
$ref: '#/responses/Unauthorized'
'404':
description: No such user
post:
summary: Create a new token for the user
parameters:
- name: token_params
in: body
required: false
schema:
type: object
properties:
expires_in:
type: number
description: lifetime (in seconds) after which the requested token will expire.
note:
type: string
description: A note attached to the token for future bookkeeping
responses:
'201':
description: The newly created token
schema:
$ref: '#/definitions/Token'
'400':
description: Body must be a JSON dict or empty
/users/{name}/tokens/{token_id}:
parameters:
- name: name
description: username
in: path
required: true
type: string
- name: token_id
in: path
required: true
type: string
get:
summary: Get the model for a token by id
responses:
'200':
description: The info for the new token
schema:
$ref: '#/definitions/Token'
delete:
summary: Delete (revoke) a token by id
responses:
'204':
description: The token has been deleted
/user:
get:
summary: Return authenticated user's model
responses:
'200':
description: The authenticated user's model is returned.
schema:
$ref: '#/definitions/User'
/groups:
get:
summary: List groups
responses:
'200':
description: The list of groups
schema:
type: array
items:
$ref: '#/definitions/Group'
/groups/{name}:
get:
summary: Get a group by name
parameters:
- name: name
description: group name
in: path
required: true
type: string
responses:
'200':
description: The group model
schema:
$ref: '#/definitions/Group'
post:
summary: Create a group
parameters:
- name: name
description: group name
in: path
required: true
type: string
responses:
'201':
description: The group has been created
schema:
$ref: '#/definitions/Group'
delete:
summary: Delete a group
parameters:
- name: name
description: group name
in: path
required: true
type: string
responses:
'204':
description: The group has been deleted
/groups/{name}/users:
post:
summary: Add users to a group
parameters:
- name: name
description: group name
in: path
required: true
type: string
- name: body
in: body
required: true
description: The users to add to the group
schema:
type: object
properties:
users:
type: array
description: List of usernames to add to the group
items:
type: string
responses:
'200':
description: The users have been added to the group
schema:
$ref: '#/definitions/Group'
delete:
summary: Remove users from a group
parameters:
- name: name
description: group name
in: path
required: true
type: string
- name: body
in: body
required: true
description: The users to remove from the group
schema:
type: object
properties:
users:
type: array
description: List of usernames to remove from the group
items:
type: string
responses:
'200':
description: The users have been removed from the group
/services:
get:
summary: List services
responses:
'200':
description: The service list
schema:
type: array
items:
$ref: '#/definitions/Service'
/services/{name}:
get:
summary: Get a service by name
parameters:
- name: name
description: service name
in: path
required: true
type: string
responses:
'200':
description: The Service model
schema:
$ref: '#/definitions/Service'
/proxy:
get:
summary: Get the proxy's routing table
description: A convenience alias for getting the routing table directly from the proxy
responses:
'200':
description: Routing table
schema:
type: object
description: configurable-http-proxy routing table (see configurable-http-proxy docs for details)
post:
summary: Force the Hub to sync with the proxy
responses:
'200':
description: Success
patch:
summary: Notify the Hub about a new proxy
description: Notifies the Hub of a new proxy to use.
parameters:
- name: body
in: body
required: true
description: Any values that have changed for the new proxy. All keys are optional.
schema:
type: object
properties:
ip:
type: string
description: IP address of the new proxy
port:
type: string
description: Port of the new proxy
protocol:
type: string
description: Protocol of new proxy, if changed
auth_token:
type: string
description: CONFIGPROXY_AUTH_TOKEN for the new proxy
responses:
'200':
description: Success
/authorizations/token:
post:
summary: Request a new API token
description: |
Request a new API token to use with the JupyterHub REST API.
If not already authenticated, username and password can be sent
in the JSON request body.
Logging in via this method is only available when the active Authenticator
accepts passwords (e.g. not OAuth).
parameters:
- name: credentials
in: body
schema:
type: object
properties:
username:
type: string
password:
type: string
responses:
'200':
description: The new API token
schema:
type: object
properties:
token:
type: string
description: The new API token.
'403':
description: The user can not be authenticated.
/authorizations/token/{token}:
get:
summary: Identify a user or service from an API token
parameters:
- name: token
in: path
required: true
type: string
responses:
'200':
description: The user or service identified by the API token
'404':
description: A user or service is not found.
/authorizations/cookie/{cookie_name}/{cookie_value}:
get:
summary: Identify a user from a cookie
description: Used by single-user notebook servers to hand off cookie authentication to the Hub
parameters:
- name: cookie_name
in: path
required: true
type: string
- name: cookie_value
in: path
required: true
type: string
responses:
'200':
description: The user identified by the cookie
schema:
$ref: '#/definitions/User'
'404':
description: A user is not found.
/oauth2/authorize:
get:
summary: 'OAuth 2.0 authorize endpoint'
description: |
Redirect users to this URL to begin the OAuth process.
It is not an API endpoint.
parameters:
- name: client_id
description: The client id
in: query
required: true
type: string
- name: response_type
description: The response type (always 'code')
in: query
required: true
type: string
- name: state
description: A state string
in: query
required: false
type: string
- name: redirect_uri
description: The redirect url
in: query
required: true
type: string
responses:
'200':
description: Success
'400':
description: OAuth2Error
/oauth2/token:
post:
summary: Request an OAuth2 token
description: |
Request an OAuth2 token from an authorization code.
This request completes the OAuth process.
consumes:
- application/x-www-form-urlencoded
parameters:
- name: client_id
description: The client id
in: formData
required: true
type: string
- name: client_secret
description: The client secret
in: formData
required: true
type: string
- name: grant_type
description: The grant type (always 'authorization_code')
in: formData
required: true
type: string
- name: code
description: The code provided by the authorization redirect
in: formData
required: true
type: string
- name: redirect_uri
description: The redirect url
in: formData
required: true
type: string
responses:
'200':
description: JSON response including the token
schema:
type: object
properties:
access_token:
type: string
description: The new API token for the user
token_type:
type: string
description: Will always be 'Bearer'
/shutdown:
post:
summary: Shutdown the Hub
parameters:
- name: body
in: body
schema:
type: object
properties:
proxy:
type: boolean
description: Whether the proxy should be shutdown as well (default from Hub config)
servers:
type: boolean
description: Whether users' notebook servers should be shutdown as well (default from Hub config)
responses:
'202':
description: Shutdown successful
'400':
description: Unexpeced value for proxy or servers
# Descriptions of common responses
responses:
NotFound:
description: The specified resource was not found
Unauthorized:
description: Authentication/Authorization error
definitions:
User:
type: object
properties:
name:
type: string
description: The user's name
admin:
type: boolean
description: Whether the user is an admin
groups:
type: array
description: The names of groups where this user is a member
items:
type: string
server:
type: string
description: The user's notebook server's base URL, if running; null if not.
pending:
type: string
enum: ["spawn", "stop", null]
description: The currently pending action, if any
last_activity:
type: string
format: date-time
description: Timestamp of last-seen activity from the user
servers:
type: array
description: The active servers for this user.
items:
$ref: '#/definitions/Server'
Server:
type: object
properties:
name:
type: string
description: The server's name. The user's default server has an empty name ('')
ready:
type: boolean
description: |
Whether the server is ready for traffic.
Will always be false when any transition is pending.
pending:
type: string
enum: ["spawn", "stop", null]
description: |
The currently pending action, if any.
A server is not ready if an action is pending.
url:
type: string
description: |
The URL where the server can be accessed
(typically /user/:name/:server.name/).
progress_url:
type: string
description: |
The URL for an event-stream to retrieve events during a spawn.
started:
type: string
format: date-time
description: UTC timestamp when the server was last started.
last_activity:
type: string
format: date-time
description: UTC timestamp last-seen activity on this server.
state:
type: object
description: Arbitrary internal state from this server's spawner. Only available on the hub's users list or get-user-by-name method, and only if a hub admin. None otherwise.
user_options:
type: object
description: User specified options for the user's spawned instance of a single-user server.
Group:
type: object
properties:
name:
type: string
description: The group's name
users:
type: array
description: The names of users who are members of this group
items:
type: string
Service:
type: object
properties:
name:
type: string
description: The service's name
admin:
type: boolean
description: Whether the service is an admin
url:
type: string
description: The internal url where the service is running
prefix:
type: string
description: The proxied URL prefix to the service's url
pid:
type: number
description: The PID of the service process (if managed)
command:
type: array
description: The command used to start the service (if managed)
items:
type: string
info:
type: object
description: |
Additional information a deployment can attach to a service.
JupyterHub does not use this field.
Token:
type: object
properties:
token:
type: string
description: The token itself. Only present in responses to requests for a new token.
id:
type: string
description: The id of the API token. Used for modifying or deleting the token.
user:
type: string
description: The user that owns a token (undefined if owned by a service)
service:
type: string
description: The service that owns the token (undefined of owned by a user)
note:
type: string
description: A note about the token, typically describing what it was created for.
created:
type: string
format: date-time
description: Timestamp when this token was created
expires_at:
type: string
format: date-time
description: Timestamp when this token expires. Null if there is no expiry.
last_activity:
type: string
format: date-time
description: |
Timestamp of last-seen activity using this token.
Can be null if token has never been used.

View File

@@ -1,10 +1,4 @@
/* Added to avoid logo being too squeezed */
.navbar-brand {
height: 4rem !important;
}
/* hide redundant funky-formatted swagger-ui version */
.swagger-ui .info .title small {
display: none !important;
}
/* Added to avoid logo being too squeezed */
.navbar-brand {
height: 4rem !important;
}

File diff suppressed because it is too large Load Diff

View File

@@ -1,72 +0,0 @@
# Interpreting common log messages
When debugging errors and outages, looking at the logs emitted by
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 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
```
### 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
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).
### Actions you can take
This log message is benign, and there is usually no action for you to take.
## JupyterHub Singleuser Version mismatch
### Example
```
jupyterhub version 1.5.0 != jupyterhub-singleuser version 1.3.0. This could cause failure to authenticate and result in redirect loops!
```
### Cause
JupyterHub requires the `jupyterhub` python package installed inside the image or
environment the user server starts in. This message indicates that the version of
the `jupyterhub` package installed inside the user image or environment is not
the same 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
changelog before upgrading!**. The primary problems this _could_ cause are:
1. Infinite redirect loops after the user server starts
2. Missing expected environment variables in the user server once it starts
3. Failure for the started user server to authenticate with the JupyterHub server -
note that this is _not_ the same as _user authentication_ failing!
However, for the most part, unless you are seeing these specific issues, the log
message should be counted as a warning to get the `jupyterhub` package versions
aligned, rather than as an indicator of an existing problem.
### Actions you can take
Upgrade the version of the `jupyterhub` package in your user environment or image
so 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

@@ -1,3 +1,5 @@
.. _admin/upgrading:
====================
Upgrading JupyterHub
====================

View File

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

File diff suppressed because one or more lines are too long

View File

@@ -1,3 +1,4 @@
# -*- coding: utf-8 -*-
#
import os
import sys
@@ -18,21 +19,16 @@ extensions = [
'autodoc_traits',
'sphinx_copybutton',
'sphinx-jsonschema',
'myst_parser',
'recommonmark',
]
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'
project = u'JupyterHub'
copyright = u'2016, Project Jupyter team'
author = u'Project Jupyter team'
# Autopopulate version
from os.path import dirname
@@ -48,7 +44,7 @@ version = '%i.%i' % jupyterhub.version_info[:2]
# The full version, including alpha/beta/rc tags.
release = jupyterhub.__version__
language = "en"
language = None
exclude_patterns = []
pygments_style = 'sphinx'
todo_include_todos = False
@@ -56,14 +52,17 @@ todo_include_todos = False
# Set the default role so we can use `foo` instead of ``foo``
default_role = 'literal'
from contextlib import redirect_stdout
from io import StringIO
# -- Source -------------------------------------------------------------
from docutils import nodes
from sphinx.directives.other import SphinxDirective
import recommonmark
from recommonmark.transform import AutoStructify
# -- 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
# create a temp instance of JupyterHub just to get the output of the generate-config
# and help --all commands.
@@ -112,7 +111,9 @@ class HelpAllDirective(SphinxDirective):
def setup(app):
app.add_config_value('recommonmark_config', {'enable_eval_rst': True}, True)
app.add_css_file('custom.css')
app.add_transform(AutoStructify)
app.add_directive('jupyterhub-generate-config', ConfigDirective)
app.add_directive('jupyterhub-help-all', HelpAllDirective)
@@ -133,30 +134,6 @@ html_static_path = ['_static']
htmlhelp_basename = 'JupyterHubdoc'
html_theme_options = {
"icon_links": [
{
"name": "GitHub",
"url": "https://github.com/jupyterhub/jupyterhub",
"icon": "fab fa-github-square",
},
{
"name": "Discourse",
"url": "https://discourse.jupyter.org/c/jupyterhub/10",
"icon": "fab fa-discourse",
},
],
"use_edit_page_button": True,
"navbar_align": "left",
}
html_context = {
"github_user": "jupyterhub",
"github_repo": "jupyterhub",
"github_version": "main",
"doc_path": "docs",
}
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
@@ -173,8 +150,8 @@ latex_documents = [
(
master_doc,
'JupyterHub.tex',
'JupyterHub Documentation',
'Project Jupyter team',
u'JupyterHub Documentation',
u'Project Jupyter team',
'manual',
)
]
@@ -191,7 +168,7 @@ latex_documents = [
# 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_pages = [(master_doc, 'jupyterhub', u'JupyterHub Documentation', [author], 1)]
# man_show_urls = False
@@ -205,7 +182,7 @@ texinfo_documents = [
(
master_doc,
'JupyterHub',
'JupyterHub Documentation',
u'JupyterHub Documentation',
author,
'JupyterHub',
'One line description of project.',
@@ -232,10 +209,7 @@ epub_exclude_files = ['search.html']
# -- Intersphinx ----------------------------------------------------------
intersphinx_mapping = {
'python': ('https://docs.python.org/3/', None),
'tornado': ('https://www.tornadoweb.org/en/stable/', None),
}
intersphinx_mapping = {'https://docs.python.org/3/': None}
# -- Read The Docs --------------------------------------------------------
@@ -245,7 +219,7 @@ if on_rtd:
# build both metrics and rest-api, since RTD doesn't run make
from subprocess import check_call as sh
sh(['make', 'metrics', 'scopes'], cwd=docs)
sh(['make', 'metrics', 'rest-api'], cwd=docs)
# -- Spell checking -------------------------------------------------------

View File

@@ -13,7 +13,7 @@ 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>`_ &
<http://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.
@@ -39,8 +39,8 @@ change renders correctly, it is good practice to test it locally.
along with the filename / line number in which they occurred. Fix them,
and re-run the ``make html`` command to re-render the documentation.
#. View the rendered documentation by opening ``build/html/index.html`` in
a web browser.
#. View the rendered documentation by opening ``build/html/index.html`` in
a web browser.
.. tip::

View File

@@ -6,8 +6,8 @@ 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>`_)
Our `Code of Conduct <https://github.com/jupyter/governance/blob/master/conduct/code_of_conduct.md>`_
(`reporting guidelines <https://github.com/jupyter/governance/blob/master/conduct/reporting_online.md>`_)
helps keep our community welcoming to as many people as possible.
.. toctree::

View File

@@ -6,8 +6,8 @@ the community of users, contributors, and maintainers.
The goal is to communicate priorities and upcoming release plans.
It is not a aimed at limiting contributions to what is listed here.
## Using the roadmap
## Using the roadmap
### Sharing Feedback on the Roadmap
All of the community is encouraged to provide feedback as well as share new
@@ -22,17 +22,17 @@ maintainers will help identify what a good next step is for the issue.
When submitting an issue, think about what "next step" category best describes
your issue:
- **now**, concrete/actionable step that is ready for someone to start work on.
These might be items that have a link to an issue or more abstract like
"decrease typos and dead links in the documentation"
- **soon**, less concrete/actionable step that is going to happen soon,
discussions around the topic are coming close to an end at which point it can
move into the "now" category
- **later**, abstract ideas or tasks, need a lot of discussion or
experimentation to shape the idea so that it can be executed. Can also
contain concrete/actionable steps that have been postponed on purpose
(these are steps that could be in "now" but the decision was taken to work on
them later)
* **now**, concrete/actionable step that is ready for someone to start work on.
These might be items that have a link to an issue or more abstract like
"decrease typos and dead links in the documentation"
* **soon**, less concrete/actionable step that is going to happen soon,
discussions around the topic are coming close to an end at which point it can
move into the "now" category
* **later**, abstract ideas or tasks, need a lot of discussion or
experimentation to shape the idea so that it can be executed. Can also
contain concrete/actionable steps that have been postponed on purpose
(these are steps that could be in "now" but the decision was taken to work on
them later)
### Reviewing and Updating the Roadmap
@@ -47,8 +47,8 @@ For those please create a
The roadmap should give the reader an idea of what is happening next, what needs
input and discussion before it can happen and what has been postponed.
## The roadmap proper
## The roadmap proper
### Project vision
JupyterHub is a dependable tool used by humans that reduces the complexity of
@@ -58,19 +58,20 @@ creating the environment in which a piece of software can be executed.
These "Now" items are considered active areas of focus for the project:
- HubShare - a sharing service for use with JupyterHub.
- Users should be able to:
- Push a project to other users.
- Get a checkout of a project from other users.
- Push updates to a published project.
- Pull updates from a published project.
- Manage conflicts/merges by simply picking a version (our/theirs)
- Get a checkout of a project from the internet. These steps are completely different from saving notebooks/files.
- Have directories that are managed by git completely separately from our stuff.
- Look at pushed content that they have access to without an explicit pull.
- Define and manage teams of users.
- Adding/removing a user to/from a team gives/removes them access to all projects that team has access to.
- Build other services, such as static HTML publishing and dashboarding on top of these things.
* HubShare - a sharing service for use with JupyterHub.
* Users should be able to:
- Push a project to other users.
- Get a checkout of a project from other users.
- Push updates to a published project.
- Pull updates from a published project.
- Manage conflicts/merges by simply picking a version (our/theirs)
- Get a checkout of a project from the internet. These steps are completely different from saving notebooks/files.
- Have directories that are managed by git completely separately from our stuff.
- Look at pushed content that they have access to without an explicit pull.
- Define and manage teams of users.
- Adding/removing a user to/from a team gives/removes them access to all projects that team has access to.
- Build other services, such as static HTML publishing and dashboarding on top of these things.
### Soon
@@ -78,10 +79,11 @@ These "Soon" items are under discussion. Once an item reaches the point of an
actionable plan, the item will be moved to the "Now" section. Typically,
these will be moved at a future review of the roadmap.
- resource monitoring and management:
- (prometheus?) API for resource monitoring
- tracking activity on single-user servers instead of the proxy
- notes and activity tracking per API token
* resource monitoring and management:
- (prometheus?) API for resource monitoring
- tracking activity on single-user servers instead of the proxy
- notes and activity tracking per API token
### Later
@@ -90,6 +92,6 @@ time there is no active plan for an item. The project would like to find the
resources and time to discuss these ideas.
- real-time collaboration
- Enter into real-time collaboration mode for a project that starts a shared execution context.
- Once the single-user notebook package supports realtime collaboration,
implement sharing mechanism integrated into the Hub.
- Enter into real-time collaboration mode for a project that starts a shared execution context.
- Once the single-user notebook package supports realtime collaboration,
implement sharing mechanism integrated into the Hub.

View File

@@ -16,7 +16,7 @@ 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
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!
@@ -24,10 +24,11 @@ and **not** the Python 2 version!
Install nodejs
--------------
`NodeJS 12+ <https://nodejs.org/en/>`_ is required for building some JavaScript components.
``configurable-http-proxy``, the default proxy implementation for JupyterHub, is written in Javascript.
If you have not installed nodejs before, we recommend installing it in the ``miniconda`` environment you set up for Python.
You can do so with ``conda install nodejs``.
``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
-----------
@@ -45,7 +46,7 @@ their effects quickly. You need to do a developer install to make that
happen.
.. note:: This guide does not attempt to dictate *how* development
environments should be isolated since that is a personal preference and can
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.
@@ -65,7 +66,7 @@ happen.
python -V
This should return a version number greater than or equal to 3.6.
This should return a version number greater than or equal to 3.5.
.. code:: bash
@@ -73,11 +74,12 @@ happen.
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):
3. Install ``configurable-http-proxy``. This is required to run
JupyterHub.
.. code:: bash
npm install -g configurable-http-proxy yarn
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
@@ -85,17 +87,11 @@ happen.
.. code:: bash
npm install configurable-http-proxy yarn
npm install configurable-http-proxy
export PATH=$PATH:$(pwd)/node_modules/.bin
The second line needs to be run every time you open a new terminal.
If you are using conda you can instead run:
.. code:: bash
conda install configurable-http-proxy yarn
4. Install the python packages required for JupyterHub development.
.. code:: bash
@@ -190,4 +186,3 @@ development updates, with:
python3 setup.py js # fetch updated client-side js
python3 setup.py css # recompile CSS from LESS sources
python3 setup.py jsx # build React admin app

View File

@@ -1,8 +1,8 @@
.. _contributing/tests:
===================================
Testing JupyterHub and linting code
===================================
==================
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
@@ -57,50 +57,6 @@ Running the tests
pytest -v jupyterhub/tests/test_api.py::test_shutdown
See the `pytest usage documentation <https://pytest.readthedocs.io/en/latest/usage.html>`_ for more details.
Test organisation
=================
The tests live in ``jupyterhub/tests`` and are organized roughly into:
#. ``test_api.py`` tests the REST API
#. ``test_pages.py`` tests loading the HTML pages
and other collections of tests for different components.
When writing a new test, there should usually be a test of
similar functionality already written and related tests should
be added nearby.
The fixtures live in ``jupyterhub/tests/conftest.py``. There are
fixtures that can be used for JupyterHub components, such as:
- ``app``: an instance of JupyterHub with mocked parts
- ``auth_state_enabled``: enables persisting auth_state (like authentication tokens)
- ``db``: a sqlite in-memory DB session
- ``io_loop```: a Tornado event loop
- ``event_loop``: a new asyncio event loop
- ``user``: creates a new temporary user
- ``admin_user``: creates a new temporary admin user
- single user servers
- ``cleanup_after``: allows cleanup of single user servers between tests
- mocked service
- ``MockServiceSpawner``: a spawner that mocks services for testing with a short poll interval
- ``mockservice```: mocked service with no external service url
- ``mockservice_url``: mocked service with a url to test external services
And fixtures to add functionality or spawning behavior:
- ``admin_access``: grants admin access
- ``no_patience```: sets slow-spawning timeouts to zero
- ``slow_spawn``: enables the SlowSpawner (a spawner that takes a few seconds to start)
- ``never_spawn``: enables the NeverSpawner (a spawner that will never start)
- ``bad_spawn``: enables the BadSpawner (a spawner that fails immediately)
- ``slow_bad_spawn``: enables the SlowBadSpawner (a spawner that fails after a short delay)
See the `pytest fixtures documentation <https://pytest.readthedocs.io/en/latest/fixture.html>`_
for how to use the existing fixtures, and how to create new ones.
Troubleshooting Test Failures
=============================
@@ -110,27 +66,3 @@ 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.
Code formatting and linting
===========================
JupyterHub has adopted automatic code formatting and linting.
As long as your code is valid, the pre-commit hook should take care of how it should look.
You can invoke the pre-commit hook by hand at any time with:
.. code:: 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 running pre-commit you can fix everything using:
.. code:: bash
pre-commit run --all-files
And committing the changes.

View File

@@ -1,7 +1,10 @@
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_.
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 below_
emitted as JSON data, defined and validated by the JSON schemas listed below.
.. _logging: https://docs.python.org/3/library/logging.html
.. _`Telemetry System`: https://github.com/jupyter/telemetry
@@ -35,12 +38,13 @@ Here's a basic example:
The output is a file, ``"event.log"``, with events recorded as JSON data.
.. _page:
.. _below:
Event schemas
-------------
.. toctree::
:maxdepth: 2
:maxdepth: 2
server-actions.rst
server-actions.rst

View File

@@ -8,29 +8,27 @@ high performance computing.
Please submit pull requests to update information or to add new institutions or uses.
## Academic Institutions, Research Labs, and Supercomputer Centers
### University of California Berkeley
- [BIDS - Berkeley Institute for Data Science](https://bids.berkeley.edu/)
- [Teaching with Jupyter notebooks and JupyterHub](https://bids.berkeley.edu/resources/videos/teaching-ipythonjupyter-notebooks-and-jupyterhub)
- [Teaching with Jupyter notebooks and JupyterHub](https://bids.berkeley.edu/resources/videos/teaching-ipythonjupyter-notebooks-and-jupyterhub)
- [Data 8](http://data8.org/)
- [GitHub organization](https://github.com/data-8)
- [GitHub organization](https://github.com/data-8)
- [NERSC](http://www.nersc.gov/)
- [Press release on Jupyter and Cori](http://www.nersc.gov/news-publications/nersc-news/nersc-center-news/2016/jupyter-notebooks-will-open-up-new-possibilities-on-nerscs-cori-supercomputer/)
- [Moving and sharing data](https://www.nersc.gov/assets/Uploads/03-MovingAndSharingData-Cholia.pdf)
- [Press release on Jupyter and Cori](http://www.nersc.gov/news-publications/nersc-news/nersc-center-news/2016/jupyter-notebooks-will-open-up-new-possibilities-on-nerscs-cori-supercomputer/)
- [Moving and sharing data](https://www.nersc.gov/assets/Uploads/03-MovingAndSharingData-Cholia.pdf)
- [Research IT](http://research-it.berkeley.edu)
- [JupyterHub server supports campus research computation](http://research-it.berkeley.edu/blog/17/01/24/free-fully-loaded-jupyterhub-server-supports-campus-research-computation)
- [JupyterHub server supports campus research computation](http://research-it.berkeley.edu/blog/17/01/24/free-fully-loaded-jupyterhub-server-supports-campus-research-computation)
### University of California Davis
- [Spinning up multiple Jupyter Notebooks on AWS for a tutorial](https://github.com/mblmicdiv/course2017/blob/HEAD/exercises/sourmash-setup.md)
- [Spinning up multiple Jupyter Notebooks on AWS for a tutorial](https://github.com/mblmicdiv/course2017/blob/master/exercises/sourmash-setup.md)
Although not technically a JupyterHub deployment, this tutorial setup
may be helpful to others in the Jupyter community.
@@ -61,31 +59,23 @@ easy to do with RStudio too.
- [jupyterhub-deploy-teaching](https://github.com/jupyterhub/jupyterhub-deploy-teaching) based on work by Brian Granger for Cal Poly's Data Science 301 Course
### Chameleon
[Chameleon](https://www.chameleoncloud.org) is a NSF-funded configurable experimental environment for large-scale computer science systems research with [bare metal reconfigurability](https://chameleoncloud.readthedocs.io/en/latest/technical/baremetal.html). Chameleon users utilize JupyterHub to document and reproduce their complex CISE and networking experiments.
- [Shared JupyterHub](https://jupyter.chameleoncloud.org): provides a common "workbench" environment for any Chameleon user.
- [Trovi](https://www.chameleoncloud.org/experiment/share): a sharing portal of experiments, tutorials, and examples, which users can launch as a dedicated isolated environments on Chameleon's JupyterHub.
### Clemson University
- Advanced Computing
- [Palmetto cluster and JupyterHub](http://citi.sites.clemson.edu/2016/08/18/JupyterHub-for-Palmetto-Cluster.html)
- [Palmetto cluster and JupyterHub](http://citi.sites.clemson.edu/2016/08/18/JupyterHub-for-Palmetto-Cluster.html)
### University of Colorado Boulder
- (CU Research Computing) CURC
- [JupyterHub User Guide](https://www.rc.colorado.edu/support/user-guide/jupyterhub.html)
- Slurm job dispatched on Crestone compute cluster
- log troubleshooting
- Profiles in IPython Clusters tab
- [Parallel Processing with JupyterHub tutorial](https://www.rc.colorado.edu/support/examples-and-tutorials/parallel-processing-with-jupyterhub.html)
- [Parallel Programming with JupyterHub document](https://www.rc.colorado.edu/book/export/html/833)
- [JupyterHub User Guide](https://www.rc.colorado.edu/support/user-guide/jupyterhub.html)
- Slurm job dispatched on Crestone compute cluster
- log troubleshooting
- Profiles in IPython Clusters tab
- [Parallel Processing with JupyterHub tutorial](https://www.rc.colorado.edu/support/examples-and-tutorials/parallel-processing-with-jupyterhub.html)
- [Parallel Programming with JupyterHub document](https://www.rc.colorado.edu/book/export/html/833)
- Earth Lab at CU
- [Tutorial on Parallel R on JupyterHub](https://earthdatascience.org/tutorials/parallel-r-on-jupyterhub/)
- [Tutorial on Parallel R on JupyterHub](https://earthdatascience.org/tutorials/parallel-r-on-jupyterhub/)
### George Washington University
@@ -122,7 +112,7 @@ easy to do with RStudio too.
### Paderborn University
- [Data Science (DICE) group](https://dice.cs.uni-paderborn.de/)
- [nbgraderutils](https://github.com/dice-group/nbgraderutils): Use JupyterHub + nbgrader + iJava kernel for online Java exercises. Used in lecture Statistical Natural Language Processing.
- [nbgraderutils](https://github.com/dice-group/nbgraderutils): Use JupyterHub + nbgrader + iJava kernel for online Java exercises. Used in lecture Statistical Natural Language Processing.
### Penn State University
@@ -135,28 +125,27 @@ easy to do with RStudio too.
### University of California San Diego
- San Diego Supercomputer Center - Andrea Zonca
- [Deploy JupyterHub on a Supercomputer with SSH](https://zonca.github.io/2017/05/jupyterhub-hpc-batchspawner-ssh.html)
- [Run Jupyterhub on a Supercomputer](https://zonca.github.io/2015/04/jupyterhub-hpc.html)
- [Deploy JupyterHub on a VM for a Workshop](https://zonca.github.io/2016/04/jupyterhub-sdsc-cloud.html)
- [Customize your Python environment in Jupyterhub](https://zonca.github.io/2017/02/customize-python-environment-jupyterhub.html)
- [Jupyterhub deployment on multiple nodes with Docker Swarm](https://zonca.github.io/2016/05/jupyterhub-docker-swarm.html)
- [Sample deployment of Jupyterhub in HPC on SDSC Comet](https://zonca.github.io/2017/02/sample-deployment-jupyterhub-hpc.html)
- [Deploy JupyterHub on a Supercomputer with SSH](https://zonca.github.io/2017/05/jupyterhub-hpc-batchspawner-ssh.html)
- [Run Jupyterhub on a Supercomputer](https://zonca.github.io/2015/04/jupyterhub-hpc.html)
- [Deploy JupyterHub on a VM for a Workshop](https://zonca.github.io/2016/04/jupyterhub-sdsc-cloud.html)
- [Customize your Python environment in Jupyterhub](https://zonca.github.io/2017/02/customize-python-environment-jupyterhub.html)
- [Jupyterhub deployment on multiple nodes with Docker Swarm](https://zonca.github.io/2016/05/jupyterhub-docker-swarm.html)
- [Sample deployment of Jupyterhub in HPC on SDSC Comet](https://zonca.github.io/2017/02/sample-deployment-jupyterhub-hpc.html)
- Educational Technology Services - Paul Jamason
- [jupyterhub.ucsd.edu](https://jupyterhub.ucsd.edu)
- [jupyterhub.ucsd.edu](https://jupyterhub.ucsd.edu)
### TACC University of Texas
### Texas A&M
- Kristen Thyng - Oceanography
- [Teaching with JupyterHub and nbgrader](http://kristenthyng.com/blog/2016/09/07/jupyterhub+nbgrader/)
- [Teaching with JupyterHub and nbgrader](http://kristenthyng.com/blog/2016/09/07/jupyterhub+nbgrader/)
### Elucidata
- What's new in Jupyter Notebooks @[Elucidata](https://elucidata.io/):
- Using Jupyter Notebooks with Jupyterhub on GCP, managed by GKE - https://medium.com/elucidata/why-you-should-be-using-a-jupyter-notebook-8385a4ccd93d
- What's new in Jupyter Notebooks @[Elucidata](https://elucidata.io/):
- Using Jupyter Notebooks with Jupyterhub on GCP, managed by GKE
- https://medium.com/elucidata/why-you-should-be-using-a-jupyter-notebook-8385a4ccd93d
## Service Providers
@@ -186,6 +175,7 @@ easy to do with RStudio too.
- [Deploying JupyterHub on Hadoop](https://jupyterhub-on-hadoop.readthedocs.io)
## Miscellaneous
- https://medium.com/@ybarraud/setting-up-jupyterhub-with-sudospawner-and-anaconda-844628c0dbee#.rm3yt87e1

View File

@@ -9,6 +9,7 @@ with an account and password on the system will be allowed to login.
You can restrict which users are allowed to login with a set,
`Authenticator.allowed_users`:
```python
c.Authenticator.allowed_users = {'mal', 'zoe', 'inara', 'kaylee'}
```
@@ -16,36 +17,24 @@ c.Authenticator.allowed_users = {'mal', 'zoe', 'inara', 'kaylee'}
Users in the `allowed_users` set are added to the Hub database when the Hub is
started.
```{warning}
If this configuration value is not set, then **all authenticated users will be allowed into your hub**.
```
## Configure admins (`admin_users`)
```{note}
As of JupyterHub 2.0, the full permissions of `admin_users`
should not be required.
Instead, you can assign [roles][] to users or groups
with only the scopes they require.
```
Admin users of JupyterHub, `admin_users`, can add and remove users from
the user `allowed_users` set. `admin_users` can take actions on other users'
behalf, such as stopping and restarting their servers.
A set of initial admin users, `admin_users` can be configured as follows:
A set of initial admin users, `admin_users` can configured be as follows:
```python
c.Authenticator.admin_users = {'mal', 'zoe'}
```
Users in the admin set are automatically added to the user `allowed_users` set,
if they are not already present.
Each authenticator may have different ways of determining whether a user is an
administrator. By default JupyterHub uses the PAMAuthenticator which provides the
`admin_groups` option and can set administrator status based on a user
group. For example we can let any user in the `wheel` group be admin:
administrator. By default JupyterHub use the PAMAuthenticator which provide the
`admin_groups` option and can determine administrator status base on a user
groups. For example we can let any users in the `wheel` group be admin:
```python
c.PAMAuthenticator.admin_groups = {'wheel'}
@@ -53,10 +42,10 @@ c.PAMAuthenticator.admin_groups = {'wheel'}
## Give admin access to other users' notebook servers (`admin_access`)
Since the default `JupyterHub.admin_access` setting is `False`, the admins
Since the default `JupyterHub.admin_access` setting is False, the admins
do not have permission to log in to the single user notebook servers
owned by _other users_. If `JupyterHub.admin_access` is set to `True`,
then admins have permission to log in _as other users_ on their
owned by *other users*. If `JupyterHub.admin_access` is set to True,
then admins have permission to log in *as other users* on their
respective machines, for debugging. **As a courtesy, you should make
sure your users know if admin_access is enabled.**
@@ -64,8 +53,8 @@ sure your users know if admin_access is enabled.**
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,
automatically added to the allowed users set and database. Restarting the Hub
will not require manually updating the allowed users set in your config file,
as the users will be loaded from the database.
After starting the Hub once, it is not sufficient to **remove** a user
@@ -102,7 +91,6 @@ JupyterHub's [OAuthenticator][] currently supports the following
popular services:
- Auth0
- Azure AD
- Bitbucket
- CILogon
- GitHub
@@ -118,8 +106,8 @@ with any provider, is also available.
## Use DummyAuthenticator for testing
The `DummyAuthenticator` is a simple authenticator that
allows for any username/password unless a global password has been set. If
The :class:`~jupyterhub.auth.DummyAuthenticator` is a simple authenticator that
allows for any username/password unless if 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:
@@ -127,5 +115,5 @@ To set a global password, add this to the config file:
c.DummyAuthenticator.password = "some_password"
```
[pam]: https://en.wikipedia.org/wiki/Pluggable_authentication_module
[oauthenticator]: https://github.com/jupyterhub/oauthenticator
[PAM]: https://en.wikipedia.org/wiki/Pluggable_authentication_module
[OAuthenticator]: https://github.com/jupyterhub/oauthenticator

View File

@@ -44,7 +44,7 @@ jupyterhub -f /etc/jupyterhub/jupyterhub_config.py
```
The IPython documentation provides additional information on the
[config system](http://ipython.readthedocs.io/en/stable/development/config.html)
[config system](http://ipython.readthedocs.io/en/stable/development/config)
that Jupyter uses.
## Configure using command line options
@@ -56,18 +56,18 @@ To display all command line options that are available for configuration:
```
Configuration using the command line options is done when launching JupyterHub.
For example, to start JupyterHub on `10.0.1.2:443` with https, you
For example, to start JupyterHub on ``10.0.1.2:443`` with https, you
would enter:
```bash
jupyterhub --ip 10.0.1.2 --port 443 --ssl-key my_ssl.key --ssl-cert my_ssl.cert
```
All configurable options may technically be set on the command line,
All configurable options may technically be set on the command-line,
though some are inconvenient to type. To set a particular configuration
parameter, `c.Class.trait`, you would use the command line option,
`--Class.trait`, when starting JupyterHub. For example, to configure the
`c.Spawner.notebook_dir` trait from the command line, use the
`c.Spawner.notebook_dir` trait from the command-line, use the
`--Spawner.notebook_dir` option:
```bash
@@ -88,13 +88,13 @@ meant as illustration, are:
## Run the proxy separately
This is _not_ strictly necessary, but useful in many cases. If you
use a custom proxy (e.g. Traefik), this is also not needed.
This is *not* strictly necessary, but useful in many cases. If you
use a custom proxy (e.g. Traefik), this also not needed.
Connections to user servers go through the proxy, and _not_ the hub
itself. If the proxy stays running when the hub restarts (for
maintenance, re-configuration, etc.), then user connections are not
interrupted. For simplicity, by default the hub starts the proxy
Connections to user servers go through the proxy, and *not* the hub
itself. If the proxy stays running when the hub restarts (for
maintenance, re-configuration, etc.), then use connections are not
interrupted. For simplicity, by default the hub starts the proxy
automatically, so if the hub restarts, the proxy restarts, and user
connections are interrupted. It is easy to run the proxy separately,
connections are interrupted. It is easy to run the proxy separately,
for information see [the separate proxy page](../reference/separate-proxy).

View File

@@ -1,6 +1,7 @@
# Frequently asked questions
## How do I share links to notebooks?
### How do I share links to notebooks?
In short, where you see `/user/name/notebooks/foo.ipynb` use `/hub/user-redirect/notebooks/foo.ipynb` (replace `/user/name` with `/hub/user-redirect`).
@@ -10,9 +11,9 @@ Your first instinct might be to copy the URL you see in the browser,
e.g. `hub.jupyter.org/user/yourname/notebooks/coolthing.ipynb`.
However, let's break down what this URL means:
`hub.jupyter.org/user/yourname/` is the URL prefix handled by _your server_,
`hub.jupyter.org/user/yourname/` is the URL prefix handled by *your server*,
which means that sharing this URL is asking the person you share the link with
to come to _your server_ and look at the exact same file.
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.
@@ -21,12 +22,12 @@ A typical situation is that you have some shared or common filesystem,
such that the same path corresponds to the same document
(either the exact same document or another copy of it).
Typically, what folks want when they do sharing like this
is for each visitor to open the same file _on their own server_,
is for each visitor to open the same file *on their own server*,
so Breq would open `/user/breq/notebooks/foo.ipynb` and
Seivarden would open `/user/seivarden/notebooks/foo.ipynb`, etc.
JupyterHub has a special URL that does exactly this!
It's called `/hub/user-redirect/...`.
It's called `/hub/user-redirect/...` and after the visitor logs in,
So if you replace `/user/yourname` in your URL bar
with `/hub/user-redirect` any visitor should get the same
URL on their own server, rather than visiting yours.

View File

@@ -11,30 +11,30 @@ Yes! JupyterHub has been used at-scale for large pools of users, as well
as complex and high-performance computing. For example, UC Berkeley uses
JupyterHub for its Data Science Education Program courses (serving over
3,000 students). The Pangeo project uses JupyterHub to provide access
to scalable cloud computing with Dask. JupyterHub is stable and customizable
to scalable cloud computing with Dask. JupyterHub is stable customizable
to the use-cases of large organizations.
### I keep hearing about Jupyter Notebook, JupyterLab, and now JupyterHub. Whats the difference?
Here is a quick breakdown of these three tools:
- **The Jupyter Notebook** is a document specification (the `.ipynb`) file that interweaves
* **The Jupyter Notebook** is a document specification (the `.ipynb`) file that interweaves
narrative text with code cells and their outputs. It is also a graphical interface
that allows users to edit these documents. There are also several other graphical interfaces
that allow users to edit the `.ipynb` format (nteract, Jupyter Lab, Google Colab, Kaggle, etc).
- **JupyterLab** is a flexible and extendible user interface for interactive computing. It
* **JupyterLab** is a flexible and extendible user interface for interactive computing. It
has several extensions that are tailored for using Jupyter Notebooks, as well as extensions
for other parts of the data science stack.
- **JupyterHub** is an application that manages interactive computing sessions for **multiple users**.
* **JupyterHub** is an application that manages interactive computing sessions for **multiple users**.
It also connects them with infrastructure those users wish to access. It can provide
remote access to Jupyter Notebooks and JupyterLab for many people.
remote access to Jupyter Notebooks and Jupyter Lab for many people.
## For management
### Briefly, what problem does JupyterHub solve for us?
JupyterHub provides a shared platform for data science and collaboration.
It allows users to utilize familiar data science workflows (such as the scientific Python stack,
It allows users to utilize familiar data science workflows (such as the scientific python stack,
the R tidyverse, and Jupyter Notebooks) on institutional infrastructure. It also allows administrators
some control over access to resources, security, environments, and authentication.
@@ -50,20 +50,20 @@ scalable infrastructure, large datasets, and high-performance computing.
JupyterHub is used at a variety of institutions in academia,
industry, and government research labs. It is most-commonly used by two kinds of groups:
- Small teams (e.g., data science teams, research labs, or collaborative projects) to provide a
* Small teams (e.g., data science teams, research labs, or collaborative projects) to provide a
shared resource for interactive computing, collaboration, and analytics.
- Large teams (e.g., a department, a large class, or a large group of remote users) to provide
* Large teams (e.g., a department, a large class, or a large group of remote users) to provide
access to organizational hardware, data, and analytics environments at scale.
Here is a sample of organizations that use JupyterHub:
Here are a sample of organizations that use JupyterHub:
- **Universities and colleges**: UC Berkeley, UC San Diego, Cal Poly SLO, Harvard University, University of Chicago,
* **Universities and colleges**: UC Berkeley, UC San Diego, Cal Poly SLO, Harvard University, University of Chicago,
University of Oslo, University of Sheffield, Université Paris Sud, University of Versailles
- **Research laboratories**: NASA, NCAR, NOAA, the Large Synoptic Survey Telescope, Brookhaven National Lab,
* **Research laboratories**: NASA, NCAR, NOAA, the Large Synoptic Survey Telescope, Brookhaven National Lab,
Minnesota Supercomputing Institute, ALCF, CERN, Lawrence Livermore National Laboratory
- **Online communities**: Pangeo, Quantopian, mybinder.org, MathHub, Open Humans
- **Computing infrastructure providers**: NERSC, San Diego Supercomputing Center, Compute Canada
- **Companies**: Capital One, SANDVIK code, Globus
* **Online communities**: Pangeo, Quantopian, mybinder.org, MathHub, Open Humans
* **Computing infrastructure providers**: NERSC, San Diego Supercomputing Center, Compute Canada
* **Companies**: Capital One, SANDVIK code, Globus
See the [Gallery of JupyterHub deployments](../gallery-jhub-deployments.md) for
a more complete list of JupyterHub deployments at institutions.
@@ -95,13 +95,14 @@ The most common way to set up a JupyterHub is to use a JupyterHub distribution,
and opinionated ways to set up a JupyterHub on particular kinds of infrastructure. The two distributions
that we currently suggest are:
- [Zero to JupyterHub for Kubernetes](https://z2jh.jupyter.org) is a scalable JupyterHub deployment and
* [Zero to JupyterHub for Kubernetes](https://z2jh.jupyter.org) is a scalable JupyterHub deployment and
guide that runs on Kubernetes. Better for larger or dynamic user groups (50-10,000) or more complex
compute/data needs.
- [The Littlest JupyterHub](https://tljh.jupyter.org) is a lightweight JupyterHub that runs on a single
single machine (in the cloud or under your desk). Better for smaller user groups (4-80) or more
* [The Littlest JupyterHub](https://tljh.jupyter.org) is a lightweight JupyterHub that runs on a single
single machine (in the cloud or under your desk). Better for smaller usergroups (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.
@@ -122,9 +123,9 @@ The short answer: yes. JupyterHub as a standalone application has been battle-te
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
* For security considerations in the base JupyterHub application,
[see the JupyterHub security page](https://jupyterhub.readthedocs.io/en/stable/reference/websecurity.html)
* For security considerations when deploying JupyterHub on Kubernetes, see the
[JupyterHub on Kubernetes security page](https://zero-to-jupyterhub.readthedocs.io/en/latest/security.html).
The longer answer: it depends on your deployment. Because JupyterHub is very flexible, it can be used
@@ -136,13 +137,15 @@ If you are worried about security, don't hesitate to reach out to the JupyterHub
[Jupyter Community Forum](https://discourse.jupyter.org/c/jupyterhub). This community of practice has many
individuals with experience running secure JupyterHub deployments.
### Does JupyterHub provide computing or data infrastructure?
No - JupyterHub manages user sessions and can _control_ computing infrastructure, but it does not provide these
No - JupyterHub manages user sessions and can *control* computing infrastructure, but it does not provide these
things itself. You are expected to run JupyterHub on your own infrastructure (local or in the cloud). Moreover,
JupyterHub has no internal concept of "data", but is designed to be able to communicate with data repositories
(again, either locally or remotely) for use within interactive computing sessions.
### How do I manage users?
JupyterHub offers a few options for managing your users. Upon setting up a JupyterHub, you can choose what
@@ -151,7 +154,7 @@ email address, or choose a username / password when they first log-in, or offloa
another service such as an organization's OAuth.
The users of a JupyterHub are stored locally, and can be modified manually by an administrator of the JupyterHub.
Moreover, the _active_ users on a JupyterHub can be found on the administrator's page. This page
Moreover, the *active* users on a JupyterHub can be found on the administrator's page. This page
gives you the abiltiy to stop or restart kernels, inspect user filesystems, and even take over user
sessions to assist them with debugging.
@@ -179,11 +182,12 @@ connect with other infrastructure tools (like Dask or Spark). This allows users
scalable or high-performance resources from within their JupyterHub sessions. The logic of
how those resources are controlled is taken care of by the non-JupyterHub application.
### Can JupyterHub be used with my high-performance computing resources?
Yes - JupyterHub can provide access to many kinds of computing infrastructure.
Especially when combined with other open-source schedulers such as Dask, you can manage fairly
complex computing infrastructures from the interactive sessions of a JupyterHub. For example
complex computing infrastructure from the interactive sessions of a JupyterHub. For example
[see the Dask HPC page](https://docs.dask.org/en/latest/setup/hpc.html).
### How much resources do user sessions take?
@@ -192,7 +196,7 @@ This is highly configurable by the administrator. If you wish for your users to
data analytics environments for prototyping and light data exploring, you can restrict their
memory and CPU based on the resources that you have available. If you'd like your JupyterHub
to serve as a gateway to high-performance compute or data resources, you may increase the
resources available on user machines, or connect them with computing infrastructures elsewhere.
resources available on user machines, or connect them with computing infrastructure elsewhere.
### Can I customize the look and feel of a JupyterHub?
@@ -214,14 +218,16 @@ the technologies your JupyterHub will use (e.g., dev-ops knowledge with cloud co
In general, the base JupyterHub deployment is not the bottleneck for setup, it is connecting
your JupyterHub with the various services and tools that you wish to provide to your users.
### How well does JupyterHub scale? What are JupyterHub's limitations?
JupyterHub works well at both a small scale (e.g., a single VM or machine) as well as a
high scale (e.g., a scalable Kubernetes cluster). It can be used for teams as small as 2, and
high scale (e.g., a scalable Kubernetes cluster). It can be used for teams as small a 2, and
for user bases as large as 10,000. The scalability of JupyterHub largely depends on the
infrastructure on which it is deployed. JupyterHub has been designed to be lightweight and
flexible, so you can tailor your JupyterHub deployment to your needs.
### Is JupyterHub resilient? What happens when a machine goes down?
For JupyterHubs that are deployed in a containerized environment (e.g., Kubernetes), it is
@@ -249,7 +255,7 @@ share their results with one another.
JupyterHub also provides a computational framework to share computational narratives between
different levels of an organization. For example, data scientists can share Jupyter Notebooks
rendered as [Voilà dashboards](https://voila.readthedocs.io/en/stable/) with those who are not
rendered as [voila dashboards](https://voila.readthedocs.io/en/stable/) with those who are not
familiar with programming, or create publicly-available interactive analyses to allow others to
interact with your work.

View File

@@ -11,7 +11,7 @@ This section will help you with basic proxy and network configuration to:
The Proxy's main IP address setting determines where JupyterHub is available to users.
By default, JupyterHub is configured to be available on all network interfaces
(`''`) on port 8000. _Note_: Use of `'*'` is discouraged for IP configuration;
(`''`) on port 8000. *Note*: Use of `'*'` is discouraged for IP configuration;
instead, use of `'0.0.0.0'` is preferred.
Changing the Proxy's main IP address and port can be done with the following
@@ -43,7 +43,7 @@ port.
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.
API URL can be configured separately and override the default settings.
### Set api_url
@@ -74,7 +74,7 @@ The Hub service listens only on `localhost` (port 8081) by default.
The Hub needs to be accessible from both the proxy and all Spawners.
When spawning local servers, an IP address setting of `localhost` is fine.
If _either_ the Proxy _or_ (more likely) the Spawners will be remote or
If *either* the Proxy *or* (more likely) the Spawners will be remote or
isolated in containers, the Hub must listen on an IP that is accessible.
```python
@@ -82,20 +82,20 @@ c.JupyterHub.hub_ip = '10.0.1.4'
c.JupyterHub.hub_port = 54321
```
**Added in 0.8:** The `c.JupyterHub.hub_connect_ip` setting is the IP address or
**Added in 0.8:** The `c.JupyterHub.hub_connect_ip` setting is the ip address or
hostname that other services should use to connect to the Hub. A common
configuration for, e.g. docker, is:
```python
c.JupyterHub.hub_ip = '0.0.0.0' # listen on all interfaces
c.JupyterHub.hub_connect_ip = '10.0.1.4' # IP as seen on the docker network. Can also be a hostname.
c.JupyterHub.hub_connect_ip = '10.0.1.4' # ip as seen on the docker network. Can also be a hostname.
```
## Adjusting the hub's URL
The hub will most commonly be running on a hostname of its own. If it
The hub will most commonly be running on a hostname of its own. If it
is not for example, if the hub is being reverse-proxied and being
exposed at a URL such as `https://proxy.example.org/jupyter/` then
you will need to tell JupyterHub the base URL of the service. In such
you will need to tell JupyterHub the base URL of the service. In such
a case, it is both necessary and sufficient to set
`c.JupyterHub.base_url = '/jupyter/'` in the configuration.

View File

@@ -183,6 +183,12 @@ itself, ``jupyterhub_config.py``, as a binary string:
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

View File

@@ -2,7 +2,7 @@
When working with JupyterHub, a **Service** is defined as a process
that interacts with the Hub's REST API. A Service may perform a specific
action or task. For example, shutting down individuals' single user
or action or task. For example, shutting down individuals' single user
notebook servers that have been idle for some time is a good example of
a task that could be automated by a Service. Let's look at how the
[jupyterhub_idle_culler][] script can be used as a Service.
@@ -16,8 +16,8 @@ document will:
- clarify that API tokens can be used to authenticate to
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
- used in a Hub-managed service
- run as a standalone script
Both examples for `jupyterhub_idle_culler` will communicate tasks to the
Hub via the REST API.
@@ -93,40 +93,18 @@ In `jupyterhub_config.py`, add the following dictionary for the
c.JupyterHub.services = [
{
'name': 'idle-culler',
'admin': True,
'command': [sys.executable, '-m', 'jupyterhub_idle_culler', '--timeout=3600'],
}
]
c.JupyterHub.load_roles = [
{
"name": "list-and-cull", # name the role
"services": [
"idle-culler", # assign the service to this role
],
"scopes": [
# declare what permissions the service should have
"list:users", # list users
"read:users:activity", # read user last-activity
"admin:servers", # start/stop servers
],
}
]
```
where:
- `command` indicates that the Service will be launched as a
- `'admin': True` indicates that the Service has 'admin' permissions, and
- `'command'` indicates that the Service will be launched as a
subprocess, managed by the Hub.
```{versionchanged} 2.0
Prior to 2.0, the idle-culler required 'admin' permissions.
It now needs the scopes:
- `list:users` to access the user list endpoint
- `read:users:activity` to read activity info
- `admin:servers` to start/stop servers
```
## Run `cull-idle` manually as a standalone script
Now you can run your script by providing it
@@ -136,8 +114,7 @@ interact with it.
This will run the idle culler service manually. It can be run as a standalone
script anywhere with access to the Hub, and will periodically check for idle
servers and shut them down via the Hub's REST API. In order to shutdown the
servers, the token given to `cull-idle` must have permission to list users
and admin their servers.
servers, the token given to cull-idle must have admin privileges.
Generate an API token and store it in the `JUPYTERHUB_API_TOKEN` environment
variable. Run `jupyterhub_idle_culler` manually.

View File

@@ -1,8 +1,8 @@
# Spawners and single-user notebook servers
Since the single-user server is an instance of `jupyter notebook`, an entire separate
multi-process application, there are many aspects of that server that can be configured, and a lot
of ways to express that configuration.
multi-process application, there are many aspect of that server can configure, 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
@@ -14,7 +14,7 @@ expanded to the user's home directory.
c.Spawner.notebook_dir = '~/notebooks'
```
You can also specify extra command line arguments to the notebook server with:
You can also specify extra command-line arguments to the notebook server with:
```python
c.Spawner.args = ['--debug', '--profile=PHYS131']

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@@ -10,5 +10,4 @@ well as other information relevant to running your own JupyterHub over time.
troubleshooting
admin/upgrading
admin/log-messages
changelog

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@@ -43,7 +43,7 @@ JupyterHub performs the following functions:
notebook servers
For convenient administration of the Hub, its users, and services,
JupyterHub also provides a :doc:`REST API <reference/rest-api>`.
JupyterHub also provides a `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>`_.
@@ -108,14 +108,6 @@ API Reference
api/index
RBAC Reference
--------------
.. toctree::
:maxdepth: 2
rbac/index
Contributing
------------
@@ -123,8 +115,8 @@ 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>`_)
Our `Code of Conduct <https://github.com/jupyter/governance/blob/master/conduct/code_of_conduct.md>`_
(`reporting guidelines <https://github.com/jupyter/governance/blob/master/conduct/reporting_online.md>`_)
helps keep our community welcoming to as many people as possible.
.. toctree::
@@ -155,3 +147,4 @@ Questions? Suggestions?
.. _JupyterHub: https://github.com/jupyterhub/jupyterhub
.. _Jupyter notebook: https://jupyter-notebook.readthedocs.io/en/latest/
.. _REST API: http://petstore.swagger.io/?url=https://raw.githubusercontent.com/jupyterhub/jupyterhub/master/docs/rest-api.yml#!/default

View File

@@ -0,0 +1,347 @@
# Install JupyterHub and JupyterLab from the ground up
The combination of [JupyterHub](https://jupyterhub.readthedocs.io) and [JupyterLab](https://jupyterlab.readthedocs.io)
is a great way to make shared computing resources available to a group.
These instructions are a guide for a manual, 'bare metal' install of [JupyterHub](https://jupyterhub.readthedocs.io)
and [JupyterLab](https://jupyterlab.readthedocs.io). This is ideal for running on a single server: build a beast
of a machine and share it within your lab, or use a virtual machine from any VPS or cloud provider.
This guide has similar goals to [The Littlest JupyterHub](https://the-littlest-jupyterhub.readthedocs.io) setup
script. However, instead of bundling all these step for you into one installer, we will perform every step manually.
This makes it easy to customize any part (e.g. if you want to run other services on the same system and need to make them
work together), as well as giving you full control and understanding of your setup.
## Prerequisites
Your own server with administrator (root) access. This could be a local machine, a remotely hosted one, or a cloud instance
or VPS. Each user who will access JupyterHub should have a standard user account on the machine. The install will be done
through the command line - useful if you log into your machine remotely using SSH.
This tutorial was tested on **Ubuntu 18.04**. No other Linux distributions have been tested, but the instructions
should be reasonably straightforward to adapt.
## Goals
JupyterLab enables access to a multiple 'kernels', each one being a given environment for a given language. The most
common is a Python environment, for scientific computing usually one managed by the `conda` package manager.
This guide will set up JupyterHub and JupyterLab seperately from the Python environment. In other words, we treat
JupyterHub+JupyterLab as a 'app' or webservice, which will connect to the kernels available on the system. Specifically:
- We will create an installation of JupyterHub and JupyterLab using a virtualenv under `/opt` using the system Python.
- We will install conda globally.
- We will create a shared conda environment which can be used (but not modified) by all users.
- We will show how users can create their own private conda environments, where they can install whatever they like.
The default JupyterHub Authenticator uses PAM to authenticate system users with their username and password. One can
[choose the authenticator](https://jupyterhub.readthedocs.io/en/stable/reference/authenticators.html#authenticators)
that best suits their needs. In this guide we will use the default Authenticator because it makes it easy for everyone to manage data
in their home folder and to mix and match different services and access methods (e.g. SSH) which all work using the
Linux system user accounts. Therefore, each user of JupyterHub will need a standard system user account.
Another goal of this guide is to use system provided packages wherever possible. This has the advantage that these packages
get automatic patches and security updates (be sure to turn on automatic updates in Ubuntu). This means less maintenance
work and a more reliable system.
## Part 1: JupyterHub and JupyterLab
### Setup the JupyterHub and JupyterLab in a virtual environment
First we create a virtual environment under '/opt/jupyterhub'. The '/opt' folder is where apps not belonging to the operating
system are [commonly installed](https://unix.stackexchange.com/questions/11544/what-is-the-difference-between-opt-and-usr-local).
Both jupyterlab and jupyterhub will be installed into this virtualenv. Create it with the command:
```sh
sudo python3 -m venv /opt/jupyterhub/
```
Now we use pip to install the required Python packages into the new virtual environment. Be sure to install
`wheel` first. Since we are separating the user interface from the computing kernels, we don't install
any Python scientific packages here. The only exception is `ipywidgets` because this is needed to allow connection
between interactive tools running in the kernel and the user interface.
Note that we use `/opt/jupyterhub/bin/python3 -m pip install` each time - this [makes sure](https://snarky.ca/why-you-should-use-python-m-pip/)
that the packages are installed to the correct virtual environment.
Perform the install using the following commands:
```sh
sudo /opt/jupyterhub/bin/python3 -m pip install wheel
sudo /opt/jupyterhub/bin/python3 -m pip install jupyterhub jupyterlab
sudo /opt/jupyterhub/bin/python3 -m pip install ipywidgets
```
JupyterHub also currently defaults to requiring `configurable-http-proxy`, which needs `nodejs` and `npm`. The versions
of these available in Ubuntu therefore need to be installed first (they are a bit old but this is ok for our needs):
```sh
sudo apt install nodejs npm
```
Then install `configurable-http-proxy`:
```sh
sudo npm install -g configurable-http-proxy
```
### Create the configuration for JupyterHub
Now we start creating configuration files. To keep everything together, we put all the configuration into the folder
created for the virtualenv, under `/opt/jupyterhub/etc/`. For each thing needing configuration, we will create a further
subfolder and necessary files.
First create the folder for the JupyterHub configuration and navigate to it:
```sh
sudo mkdir -p /opt/jupyterhub/etc/jupyterhub/
cd /opt/jupyterhub/etc/jupyterhub/
```
Then generate the default configuration file
```sh
sudo /opt/jupyterhub/bin/jupyterhub --generate-config
```
This will produce the default configuration file `/opt/jupyterhub/etc/jupyterhub/jupyterhub_config.py`
You will need to edit the configuration file to make the JupyterLab interface by the default.
Set the following configuration option in your `jupyterhub_config.py` file:
```python
c.Spawner.default_url = '/lab'
```
Further configuration options may be found in the documentation.
### Setup Systemd service
We will setup JupyterHub to run as a system service using Systemd (which is responsible for managing all services and
servers that run on startup in Ubuntu). We will create a service file in a suitable location in the virtualenv folder
and then link it to the system services. First create the folder for the service file:
```sh
sudo mkdir -p /opt/jupyterhub/etc/systemd
```
Then create the following text file using your [favourite editor](https://micro-editor.github.io/) at
```sh
/opt/jupyterhub/etc/systemd/jupyterhub.service
```
Paste the following service unit definition into the file:
```
[Unit]
Description=JupyterHub
After=syslog.target network.target
[Service]
User=root
Environment="PATH=/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/opt/jupyterhub/bin"
ExecStart=/opt/jupyterhub/bin/jupyterhub -f /opt/jupyterhub/etc/jupyterhub/jupyterhub_config.py
[Install]
WantedBy=multi-user.target
```
This sets up the environment to use the virtual environment we created, tells Systemd how to start jupyterhub using
the configuration file we created, specifies that jupyterhub will be started as the `root` user (needed so that it can
start jupyter on behalf of other logged in users), and specifies that jupyterhub should start on boot after the network
is enabled.
Finally, we need to make systemd aware of our service file. First we symlink our file into systemd's directory:
```sh
sudo ln -s /opt/jupyterhub/etc/systemd/jupyterhub.service /etc/systemd/system/jupyterhub.service
```
Then tell systemd to reload its configuration files
```sh
sudo systemctl daemon-reload
```
And finally enable the service
```sh
sudo systemctl enable jupyterhub.service
```
The service will start on reboot, but we can start it straight away using:
```sh
sudo systemctl start jupyterhub.service
```
...and check that it's running using:
```sh
sudo systemctl status jupyterhub.service
```
You should now be already be able to access jupyterhub using `<your servers ip>:8000` (assuming you haven't already set
up a firewall or something). However, when you log in the jupyter notebooks will be trying to use the Python virtualenv
that was created to install JupyterHub, this is not what we want. So on to part 2
## Part 2: Conda environments
### Install conda for the whole system
We will use `conda` to manage Python environments. We will install the officially maintained `conda` packages for Ubuntu,
this means they will get automatic updates with the rest of the system. Setup repo for the official Conda debian packages,
instructions are copied from [here](https://docs.conda.io/projects/conda/en/latest/user-guide/install/rpm-debian.html):
Install Anacononda public gpg key to trusted store
```sh
curl https://repo.anaconda.com/pkgs/misc/gpgkeys/anaconda.asc | gpg --dearmor > conda.gpg
sudo install -o root -g root -m 644 conda.gpg /etc/apt/trusted.gpg.d/
```
Add Debian repo
```sh
echo "deb [arch=amd64] https://repo.anaconda.com/pkgs/misc/debrepo/conda stable main" | sudo tee /etc/apt/sources.list.d/conda.list
```
Install conda
```sh
sudo apt update
sudo apt install conda
```
This will install conda into the folder `/opt/conda/`, with the conda command available at `/opt/conda/bin/conda`.
Finally, we can make conda more easily available to users by symlinking the conda shell setup script to the profile
'drop in' folder so that it gets run on login
```sh
sudo ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh
```
### Install a default conda environment for all users
First create a folder for conda envs (might exist already):
```sh
sudo mkdir /opt/conda/envs/
```
Then create a conda environment to your liking within that folder. Here we have called it 'python' because it will
be the obvious default - call it whatever you like. You can install whatever you like into this environment, but you MUST at least install `ipykernel`.
```sh
sudo /opt/conda/bin/conda create --prefix /opt/conda/envs/python python=3.7 ipykernel
```
Once your env is set up as desired, make it visible to Jupyter by installing the kernel spec. There are two options here:
1 ) Install into the JupyterHub virtualenv - this ensures it overrides the default python version. It will only be visible
to the JupyterHub installation we have just created. This is useful to avoid conda environments appearing where they are not expected.
```sh
sudo /opt/conda/envs/python/bin/python -m ipykernel install --prefix=/opt/jupyterhub/ --name 'python' --display-name "Python (default)"
```
2 ) Install it system-wide by putting it into `/usr/local`. It will be visible to any parallel install of JupyterHub or
JupyterLab, and will persist even if you later delete or modify the JupyterHub installation. This is useful if the kernels
might be used by other services, or if you want to modify the JupyterHub installation independently from the conda environments.
```sh
sudo /opt/conda/envs/python/bin/python -m ipykernel install --prefix /usr/local/ --name 'python' --display-name "Python (default)"
````
### Setting up users' own conda environments
There is relatively little for the administrator to do here, as users will have to set up their own environments using the shell.
On login they should run `conda init` or `/opt/conda/bin/conda`. The can then use conda to set up their environment,
although they must also install `ipykernel`. Once done, they can enable their kernel using:
```sh
/path/to/kernel/env/bin/python -m ipykernel install --name 'python-my-env' --display-name "Python My Env"
```
This will place the kernel spec into their home folder, where Jupyter will look for it on startup.
## Setting up a reverse proxy
The guide so far results in JupyterHub running on port 8000. It is not generally advisable to run open web services in
this way - instead, use a reverse proxy running on standard HTTP/HTTPS ports.
> **Important**: Be aware of the security implications especially if you are running a server that is accessible from the open internet
> i.e. not protected within an institutional intranet or private home/office network. You should set up a firewall and
> HTTPS encryption, which is outside of the scope of this guide. For HTTPS consider using [LetsEncrypt](https://letsencrypt.org/)
> or setting up a [self-signed certificate](https://www.digitalocean.com/community/tutorials/how-to-create-a-self-signed-ssl-certificate-for-nginx-in-ubuntu-18-04).
> Firewalls may be set up using `ufw` or `firewalld` and combined with `fail2ban`.
### Using Nginx
Nginx is a mature and established web server and reverse proxy and is easy to install using `sudo apt install nginx`.
Details on using Nginx as a reverse proxy can be found elsewhere. Here, we will only outline the additional steps needed
to setup JupyterHub with Nginx and host it at a given URL e.g. `<your-server-ip-or-url>/jupyter`.
This could be useful for example if you are running several services or web pages on the same server.
To achieve this needs a few tweaks to both the JupyterHub configuration and the Nginx config. First, edit the
configuration file `/opt/jupyterhub/etc/jupyterhub/jupyterhub_config.py` and add the line:
```python
c.JupyterHub.bind_url = 'http://:8000/jupyter'
```
where `/jupyter` will be the relative URL of the JupyterHub.
Now Nginx must be configured with a to pass all traffic from `/jupyter` to the the local address `127.0.0.1:8000`.
Add the following snippet to your nginx configuration file (e.g. `/etc/nginx/sites-available/default`).
```
location /jupyter/ {
# NOTE important to also set base url of jupyterhub to /jupyter in its config
proxy_pass http://127.0.0.1:8000;
proxy_redirect off;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header Host $host;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
# websocket headers
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection $connection_upgrade;
}
```
Also add this snippet before the *server* block:
```
map $http_upgrade $connection_upgrade {
default upgrade;
'' close;
}
```
Nginx will not run if there are errors in the configuration, check your configuration using:
```sh
nginx -t
```
If there are no errors, you can restart the Nginx service for the new configuration to take effect.
```sh
sudo systemctl restart nginx.service
```
## Getting started using your new JupyterHub
Once you have setup JupyterHub and Nginx proxy as described, you can browse to your JupyterHub IP or URL
(e.g. if your server IP address is `123.456.789.1` and you decided to host JupyterHub at the `/jupyter` URL, browse
to `123.456.789.1/jupyter`). You will find a login page where you enter your Linux username and password. On login
you will be presented with the JupyterLab interface, with the file browser pane showing the contents of your users'
home directory on the server.

View File

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

View File

@@ -11,3 +11,4 @@ running on your own infrastructure.
quickstart
quickstart-docker
installation-basics
installation-guide-hard

View File

@@ -5,45 +5,39 @@
Before installing JupyterHub, you will need:
- 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
- [Python](https://www.python.org/downloads/) 3.5 or greater. An understanding
of using [`pip`](https://pip.pypa.io/en/stable/) or
[`conda`](https://conda.io/docs/get-started.html) for
installing Python packages is helpful.
- [nodejs/npm](https://www.npmjs.com/). [Install nodejs/npm](https://docs.npmjs.com/getting-started/installing-node),
using your operating system's package manager.
- If you are using **`conda`**, the nodejs and npm dependencies will be installed for
* If you are using **`conda`**, the nodejs and npm dependencies will be installed for
you by conda.
- If you are using **`pip`**, install a recent version of
* If you are using **`pip`**, install a recent version of
[nodejs/npm](https://docs.npmjs.com/getting-started/installing-node).
For example, install it on Linux (Debian/Ubuntu) using:
```
sudo apt-get install nodejs npm
sudo apt-get install npm nodejs-legacy
```
The `nodejs-legacy` package installs the `node` executable and is currently
required for npm to work on Debian/Ubuntu.
[nodesource][] is a great resource to get more recent versions of the nodejs runtime,
if your system package manager only has an old version of Node.js (e.g. 10 or older).
- A [pluggable authentication module (PAM)](https://en.wikipedia.org/wiki/Pluggable_authentication_module)
- A [pluggable authentication module (PAM)](https://en.wikipedia.org/wiki/Pluggable_authentication_module)
to use the [default Authenticator](./getting-started/authenticators-users-basics.md).
PAM is often available by default on most distributions, if this is not the case it can be installed by
PAM is often available by default on most distributions, if this is not the case it can be installed by
using the operating system's package manager.
- TLS certificate and key for HTTPS communication
- Domain name
[nodesource]: https://github.com/nodesource/distributions#table-of-contents
Before running the single-user notebook servers (which may be on the same
system as the Hub or not), you will need:
- [JupyterLab][] version 3 or greater,
or [Jupyter Notebook][]
4 or greater.
[jupyterlab]: https://jupyterlab.readthedocs.io
[jupyter notebook]: https://jupyter.readthedocs.io/en/latest/install.html
- [Jupyter Notebook](https://jupyter.readthedocs.io/en/latest/install.html)
version 4 or greater
## Installation
@@ -54,14 +48,14 @@ JupyterHub can be installed with `pip` (and the proxy with `npm`) or `conda`:
```bash
python3 -m pip install jupyterhub
npm install -g configurable-http-proxy
python3 -m pip install jupyterlab notebook # needed if running the notebook servers in the same environment
python3 -m pip install notebook # needed if running the notebook servers locally
```
**conda** (one command installs jupyterhub and proxy):
```bash
conda install -c conda-forge jupyterhub # installs jupyterhub and proxy
conda install jupyterlab notebook # needed if running the notebook servers in the same environment
conda install notebook # needed if running the notebook servers locally
```
Test your installation. If installed, these commands should return the packages'
@@ -80,16 +74,16 @@ To start the Hub server, run the command:
jupyterhub
```
Visit `http://localhost:8000` in your browser, and sign in with your unix
Visit `https://localhost:8000` in your browser, and sign in with your unix
credentials.
To **allow multiple users to sign in** to the Hub server, you must start
`jupyterhub` as a _privileged user_, such as root:
`jupyterhub` as a *privileged user*, such as root:
```bash
sudo jupyterhub
```
The [wiki](https://github.com/jupyterhub/jupyterhub/wiki/Using-sudo-to-run-JupyterHub-without-root-privileges)
describes how to run the server as a _less privileged user_. This requires
describes how to run the server as a *less privileged user*. This requires
additional configuration of the system.

View File

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

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@@ -1,39 +0,0 @@
(RBAC)=
# JupyterHub RBAC
Role Based Access Control (RBAC) in JupyterHub serves to provide fine grained control of access to Jupyterhub's API resources.
RBAC is new in JupyterHub 2.0.
## Motivation
The JupyterHub API requires authorization to access its APIs.
This ensures that an arbitrary user, or even an unauthenticated third party, are not allowed to perform such actions.
For instance, the behaviour prior to adoption of RBAC is that creating or deleting users requires _admin rights_.
The prior system is functional, but lacks flexibility. If your Hub serves a number of users in different groups, you might want to delegate permissions to other users or automate certain processes.
Prior to RBAC, appointing a 'group-only admin' or a bot that culls idle servers, requires granting full admin rights to all actions. This poses a risk of the user or service intentionally or unintentionally accessing and modifying any data within the Hub and violates the [principle of least privilege](https://en.wikipedia.org/wiki/Principle_of_least_privilege).
To remedy situations like this, JupyterHub is transitioning to an RBAC system. By equipping users, groups and services with _roles_ that supply them with a collection of permissions (_scopes_), administrators are able to fine-tune which parties are granted access to which resources.
## Definitions
**Scopes** are specific permissions used to evaluate API requests. For example: the API endpoint `users/servers`, which enables starting or stopping user servers, is guarded by the scope `servers`.
Scopes are not directly assigned to requesters. Rather, when a client performs an API call, their access will be evaluated based on their assigned roles.
**Roles** are collections of scopes that specify the level of what a client is allowed to do. For example, a group administrator may be granted permission to control the servers of group members, but not to create, modify or delete group members themselves.
Within the RBAC framework, this is achieved by assigning a role to the administrator that covers exactly those privileges.
## Technical Overview
```{toctree}
:maxdepth: 2
roles
scopes
use-cases
tech-implementation
upgrade
```

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@@ -1,161 +0,0 @@
(roles)=
# Roles
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.
- `admin` role contains all available scopes and grants full rights to all actions. This role **cannot be edited**.
- `token` role provides a {ref}`default token scope <default-token-scope-target>` `inherit` that resolves to the same permissions as the owner of the token has.
- `server` role allows for posting activity of "itself" only.
**These roles cannot be deleted.**
```
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 RBAC.
Only the `server` role is changed from pre-2.0, to reduce its permissions to activity-only
instead of the default of a full access token.
Additional custom roles can also be defined (see {ref}`define-role-target`).
Roles can be assigned to the following entities:
- Users
- Services
- Groups
An entity can have zero, one, or multiple roles, and there are no restrictions on which roles can be assigned to which entity. Roles can be added to or removed from entities at any time.
**Users** \
When a new user gets created, they are assigned their default role `user`. 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.
**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.
(define-role-target)=
## Defining Roles
Roles can be defined or modified in the configuration file as a list of dictionaries. An example:
% TODO: think about loading users into roles if membership has been changed via API.
% What should be the result?
```python
# in jupyterhub_config.py
c.JupyterHub.load_roles = [
{
'name': 'server-rights',
'description': 'Allows parties to start and stop user servers',
'scopes': ['servers'],
'users': ['alice', 'bob'],
'services': ['idle-culler'],
'groups': ['admin-group'],
}
]
```
The role `server-rights` now allows the starting and stopping of servers by any of the following:
- users `alice` and `bob`
- the service `idle-culler`
- any member of the `admin-group`.
```{attention}
Tokens cannot be assigned roles through role definition but may be assigned specific roles when requested via API (see {ref}`requesting-api-token-target`).
```
Another example:
```python
# in jupyterhub_config.py
c.JupyterHub.load_roles = [
{
'description': 'Read-only user models',
'name': 'reader',
'scopes': ['read:users'],
'services': ['external'],
'users': ['maria', 'joe']
}
]
```
The role `reader` allows users `maria` and `joe` and service `external` to read (but not modify) any users model.
```{admonition} Requirements
:class: warning
In a role definition, the `name` field is required, while all other fields are optional.\
**Role names must:**
- be 3 - 255 characters
- use ascii lowercase, numbers, 'unreserved' URL punctuation `-_.~`
- start with a letter
- end with letter or number.
`users`, `services`, and `groups` only accept objects that already exist in the database or are defined previously in the file.
It is not possible to implicitly add a new user to the database by defining a new role.
```
If no scopes are defined for _new role_, JupyterHub will raise a warning. Providing non-existing scopes will result in an error.
In case the role with a certain name already exists in the database, its definition and scopes will be overwritten. This holds true for all roles except the `admin` role, which cannot be overwritten; an error will be raised if trying to do so. All the role bearers permissions present in the definition will change accordingly.
(overriding-default-roles)=
### Overriding default roles
Role definitions can include those of the "default" roles listed above (admin excluded),
if the default scopes associated with those roles do not suit your deployment.
For example, to specify what permissions the $JUPYTERHUB_API_TOKEN issued to all single-user servers
has,
define the `server` role.
To restore the JupyterHub 1.x behavior of servers being able to do anything their owners can do,
use the scope `inherit` (for 'inheriting' the owner's permissions):
```python
c.JupyterHub.load_roles = [
{
'name': 'server',
'scopes': ['inherit'],
}
]
```
or, better yet, identify the specific [scopes][] you want server environments to have access to.
[scopes]: available-scopes-target
If you don't want to get too detailed,
one option is the `self` scope,
which will have no effect on non-admin users,
but will restrict the token issued to admin user servers to only have access to their own resources,
instead of being able to take actions on behalf of all other users.
```python
c.JupyterHub.load_roles = [
{
'name': 'server',
'scopes': ['self'],
}
]
```
(removing-roles-target)=
## Removing roles
Only the entities present in the role definition in the `jupyterhub_config.py` remain the role bearers. If a user, service or group is removed from the role definition, they will lose the role on the next startup.
Once a role is loaded, it remains in the database until removing it from the `jupyterhub_config.py` and restarting the Hub. All previously defined role bearers will lose the role and associated permissions. Default roles, even if previously redefined through the config file and removed, will not be deleted from the database.

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@@ -1,303 +0,0 @@
# Scopes in JupyterHub
A scope has a syntax-based design that reveals which resources it provides access to. Resources are objects with a type, associated data, relationships to other resources, and a set of methods that operate on them (see [RESTful API](https://restful-api-design.readthedocs.io/en/latest/resources.html) documentation for more information).
`<resource>` in the RBAC scope design refers to the resource name in the [JupyterHub's API](../reference/rest-api.rst) endpoints in most cases. For instance, `<resource>` equal to `users` corresponds to JupyterHub's API endpoints beginning with _/users_.
(scope-conventions-target)=
## Scope conventions
- `<resource>` \
The top-level `<resource>` scopes, such as `users` or `groups`, grant read, write, and list permissions to the resource itself as well as its sub-resources. For example, the scope `users:activity` is included in the scope `users`.
- `read:<resource>` \
Limits permissions to read-only operations on single resources.
- `list:<resource>` \
Read-only access to listing endpoints.
Use `read:<resource>:<subresource>` to control what fields are returned.
- `admin:<resource>` \
Grants additional permissions such as create/delete on the corresponding resource in addition to read and write permissions.
- `access:<resource>` \
Grants access permissions to the `<resource>` via API or browser.
- `<resource>:<subresource>` \
The {ref}`vertically filtered <vertical-filtering-target>` scopes provide access to a subset of the information granted by the `<resource>` scope. E.g., the scope `users:activity` only provides permission to post user activity.
- `<resource>!<object>=<objectname>` \
{ref}`horizontal-filtering-target` is implemented by the `!<object>=<objectname>`scope structure. A resource (or sub-resource) can be filtered based on `user`, `server`, `group` or `service` name. For instance, `<resource>!user=charlie` limits access to only return resources of user `charlie`. \
Only one filter per scope is allowed, but filters for the same scope have an additive effect; a larger filter can be used by supplying the scope multiple times with different filters.
By adding a scope to an existing role, all role bearers will gain the associated permissions.
## Metascopes
Metascopes do not follow the general scope syntax. Instead, a metascope resolves to a set of scopes, which can refer to different resources, based on their owning entity. In JupyterHub, there are currently two metascopes:
1. default user scope `self`, and
2. default token scope `inherit`.
(default-user-scope-target)=
### Default user scope
Access to the user's own resources and subresources is covered by metascope `self`. This metascope includes the user's model, activity, servers and tokens. For example, `self` for a user named "gerard" includes:
- `users!user=gerard` where the `users` scope provides access to the full user model and activity. The filter restricts this access to the user's own resources.
- `servers!user=gerard` which grants the user access to their own servers without being able to create/delete any.
- `tokens!user=gerard` which allows the user to access, request and delete their own tokens.
- `access:servers!user=gerard` which allows the user to access their own servers via API or browser.
The `self` scope is only valid for user entities. In other cases (e.g., for services) it resolves to an empty set of scopes.
(default-token-scope-target)=
### Default token scope
The token metascope `inherit` causes the token to have the same permissions as the token's owner. For example, if a token owner has roles containing the scopes `read:groups` and `read:users`, the `inherit` scope resolves to the set of scopes `{read:groups, read:users}`.
If the token owner has default `user` role, the `inherit` scope resolves to `self`, which will subsequently be expanded to include all the user-specific scopes (or empty set in the case of services).
If the token owner is a member of any group with roles, the group scopes will also be included in resolving the `inherit` scope.
(horizontal-filtering-target)=
## Horizontal filtering
Horizontal filtering, also called _resource filtering_, is the concept of reducing the payload of an API call to cover only the subset of the _resources_ that the scopes of the client provides them access to.
Requested resources are filtered based on the filter of the corresponding scope. For instance, if a service requests a user list (guarded with scope `read:users`) with a role that only contains scopes `read:users!user=hannah` and `read:users!user=ivan`, the returned list of user models will be an intersection of all users and the collection `{hannah, ivan}`. In case this intersection is empty, the API call returns an HTTP 404 error, regardless if any users exist outside of the clients scope filter collection.
In case a user resource is being accessed, any scopes with _group_ filters will be expanded to filters for each _user_ in those groups.
(self-referencing-filters)=
### Self-referencing filters
There are some 'shortcut' filters,
which can be applied to all scopes,
that filter based on the entities associated with the request.
The `!user` filter is a special horizontal filter that strictly refers to the **"owner only"** scopes, where _owner_ is a user entity. The filter resolves internally into `!user=<ownerusername>` ensuring that only the owner's resources may be accessed through the associated scopes.
For example, the `server` role assigned by default to server tokens contains `access:servers!user` and `users:activity!user` scopes. This allows the token to access and post activity of only the servers owned by the token owner.
:::{versionadded} 3.0
`!service` and `!server` filters.
:::
In addition to `!user`, _tokens_ may have filters `!service`
or `!server`, which expand similarly to `!service=servicename`
and `!server=servername`.
This only applies to tokens issued via the OAuth flow.
In these cases, the name is the _issuing_ entity (a service or single-user server),
so that access can be restricted to the issuing service,
e.g. `access:servers!server` would grant access only to the server that requested the token.
These filters can be applied to any scope.
(vertical-filtering-target)=
## Vertical filtering
Vertical filtering, also called _attribute filtering_, is the concept of reducing the payload of an API call to cover only the _attributes_ of the resources that the scopes of the client provides them access to. This occurs when the client scopes are subscopes of the API endpoint that is called.
For instance, if a client requests a user list with the only scope being `read:users:groups`, the returned list of user models will contain only a list of groups per user.
In case the client has multiple subscopes, the call returns the union of the data the client has access to.
The payload of an API call can be filtered both horizontally and vertically simultaneously. For instance, performing an API call to the endpoint `/users/` with the scope `users:name!user=juliette` returns a payload of `[{name: 'juliette'}]` (provided that this name is present in the database).
(available-scopes-target)=
## Available scopes
Table below lists all available scopes and illustrates their hierarchy. Indented scopes indicate subscopes of the scope(s) above them.
There are four exceptions to the general {ref}`scope conventions <scope-conventions-target>`:
- `read:users:name` is a subscope of both `read:users` and `read:servers`. \
The `read:servers` scope requires access to the user name (server owner) due to named servers distinguished internally in the form `!server=username/servername`.
- `read:users:activity` is a subscope of both `read:users` and `users:activity`. \
Posting activity via the `users:activity`, which is not included in `users` scope, needs to check the last valid activity of the user.
- `read:roles:users` is a subscope of both `read:roles` and `admin:users`. \
Admin privileges to the _users_ resource include the information about user roles.
- `read:roles:groups` is a subscope of both `read:roles` and `admin:groups`. \
Similar to the `read:roles:users` above.
```{include} scope-table.md
```
:::{versionadded} 3.0
The `admin-ui` scope is added to explicitly grant access to the admin page,
rather than combining `admin:users` and `admin:servers` permissions.
This means a deployment can enable the admin page with only a subset of functionality enabled.
Note that this means actions to take _via_ the admin UI
and access _to_ the admin UI are separated.
For example, it generally doesn't make sense to grant
`admin-ui` without at least `list:users` for at least some subset of users.
For example:
```python
c.JupyterHub.load_roles = [
{
"name": "instructor-data8",
"scopes": [
# access to the admin page
"admin-ui",
# list users in the class group
"list:users!group=students-data8",
# start/stop servers for users in the class
"admin:servers!group=students-data8",
# access servers for users in the class
"access:servers!group=students-data8",
],
"group": ["instructors-data8"],
}
]
```
will grant instructors in the data8 course permission to:
1. view the admin UI
2. see students in the class (but not all users)
3. start/stop/access servers for users in the class
4. but _not_ permission to administer the users themselves (e.g. change their permissions, etc.)
:::
```{Caution}
Note that only the {ref}`horizontal filtering <horizontal-filtering-target>` can be added to scopes to customize them. \
Metascopes `self` and `all`, `<resource>`, `<resource>:<subresource>`, `read:<resource>`, `admin:<resource>`, and `access:<resource>` scopes are predefined and cannot be changed otherwise.
```
(custom-scopes)=
### Custom scopes
:::{versionadded} 3.0
:::
JupyterHub 3.0 introduces support for custom scopes.
Services that use JupyterHub for authentication and want to implement their own granular access may define additional _custom_ scopes and assign them to users with JupyterHub roles.
% Note: keep in sync with pattern/description in jupyterhub/scopes.py
Custom scope names must start with `custom:`
and contain only lowercase ascii letters, numbers, hyphen, underscore, colon, and asterisk (`-_:*`).
The part after `custom:` must start with a letter or number.
Scopes may not end with a hyphen or colon.
The only strict requirement is that a custom scope definition must have a `description`.
It _may_ also have `subscopes` if you are defining multiple scopes that have a natural hierarchy,
For example:
```python
c.JupyterHub.custom_scopes = {
"custom:myservice:read": {
"description": "read-only access to myservice",
},
"custom:myservice:write": {
"description": "write access to myservice",
# write permission implies read permission
"subscopes": [
"custom:myservice:read",
],
},
}
c.JupyterHub.load_roles = [
# graders have read-only access to the service
{
"name": "service-user",
"groups": ["graders"],
"scopes": [
"custom:myservice:read",
"access:service!service=myservice",
],
},
# instructors have read and write access to the service
{
"name": "service-admin",
"groups": ["instructors"],
"scopes": [
"custom:myservice:write",
"access:service!service=myservice",
],
},
]
```
In the above configuration, two scopes are defined:
- `custom:myservice:read` grants read-only access to the service, and
- `custom:myservice:write` grants write access to the service
- write access _implies_ read access via the `subscope`
These custom scopes are assigned to two groups via `roles`:
- users in the group `graders` are granted read access to the service
- users in the group `instructors` are
- both are granted _access_ to the service via `access:service!service=myservice`
When the service completes OAuth, it will retrieve the user model from `/hub/api/user`.
This model includes a `scopes` field which is a list of authorized scope for the request,
which can be used.
```python
def require_scope(scope):
"""decorator to require a scope to perform an action"""
def wrapper(func):
@functools.wraps(func)
def wrapped_func(request):
user = fetch_hub_api_user(request.token)
if scope not in user["scopes"]:
raise HTTP403(f"Requires scope {scope}")
else:
return func()
return wrapper
@require_scope("custom:myservice:read")
async def read_something(request):
...
@require_scope("custom:myservice:write")
async def write_something(request):
...
```
If you use {class}`~.HubOAuthenticated`, this check is performed automatically
against the `.hub_scopes` attribute of each Handler
(the default is populated from `$JUPYTERHUB_OAUTH_ACCESS_SCOPES` and usually `access:services!service=myservice`).
:::{versionchanged} 3.0
The JUPYTERHUB_OAUTH_SCOPES environment variable is deprecated and renamed to JUPYTERHUB_OAUTH_ACCESS_SCOPES,
to avoid ambiguity with JUPYTERHUB_OAUTH_CLIENT_ALLOWED_SCOPES
:::
```python
from tornado import web
from jupyterhub.services.auth import HubOAuthenticated
class MyHandler(HubOAuthenticated, BaseHandler):
hub_scopes = ["custom:myservice:read"]
@web.authenticated
def get(self):
...
```
Existing scope filters (`!user=`, etc.) may be applied to custom scopes.
Custom scope _filters_ are NOT supported.
### Scopes and APIs
The scopes are also listed in the [](../reference/rest-api.rst) documentation. Each API endpoint has a list of scopes which can be used to access the API; if no scopes are listed, the API is not authenticated and can be accessed without any permissions (i.e., no scopes).
Listed scopes by each API endpoint reflect the "lowest" permissions required to gain any access to the corresponding API. For example, posting user's activity (_POST /users/:name/activity_) needs `users:activity` scope. If scope `users` is passed during the request, the access will be granted as the required scope is a subscope of the `users` scope. If, on the other hand, `read:users:activity` scope is passed, the access will be denied.

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@@ -1,100 +0,0 @@
# Technical Implementation
Roles are stored in the database, where they are associated with users, services, etc., and can be added or modified as explained in {ref}`define-role-target` section. Users, services, groups, and tokens can gain, change, and lose roles. This is currently achieved via `jupyterhub_config.py` (see {ref}`define-role-target`) and will be made available via API in future. The latter will allow for changing a token's role, and thereby its permissions, without the need to issue a new token.
Roles 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"]`.
- _expanded scopes_ \
Set of fully expanded scopes without abbreviations (i.e., resolved metascopes, filters, and subscopes). E.g., `{"users:activity!user=charlie", "read:users:activity!user=charlie"}`.
- _parsed scopes_ \
Dictionary represenation of expanded scopes. E.g., `{"users:activity": {"user": ["charlie"]}, "read:users:activity": {"users": ["charlie"]}}`.
- _intersection_ \
Set of expanded scopes as intersection of 2 expanded scope sets.
- _identify scopes_ \
Set of expanded scopes needed for identify (whoami) endpoints.
```
(resolving-roles-scopes-target)=
## Resolving roles and scopes
**Resolving roles** refers to determining which roles a user, service, or group has, extracting the list of scopes from each role and combining them into a single set of scopes.
**Resolving 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 making an API request. The following sections provide more details.
(requesting-api-token-target)=
### Requesting API token with specific scopes
:::{versionchanged} 3.0
API tokens have _scopes_ instead of roles,
so that their permissions cannot be updated.
You may still request roles for a token,
but those roles will be evaluated to the corresponding _scopes_ immediately.
Prior to 3.0, tokens stored _roles_,
which meant their scopes were resolved on each request.
:::
API tokens grant access to JupyterHub's APIs. The RBAC framework allows for requesting tokens with specific permissions.
RBAC is involved in several stages of the OAuth token flow.
When requesting a token via the tokens API (`/users/:name/tokens`), or the token page (`/hub/token`),
if no scopes are requested, the token is issued with the permissions stored on the default `token` role
(providing the requester is allowed to create the token).
OAuth tokens are also requested via OAuth flow
If the token is requested with any scopes, the permissions of requesting entity are checked against the requested permissions to ensure the token would not grant its owner additional privileges.
If, due to modifications of permissions of the token or token owner,
at API request time a token has any scopes that its owner does not,
those scopes are removed.
The API request is resolved without additional errors using the scope _intersection_;
the Hub logs a warning in this case (see {ref}`Figure 2 <api-request-chart>`).
Resolving a token's scope (yellow box in {ref}`Figure 1 <token-request-chart>`) corresponds to resolving all the token's owner roles (including the roles associated with their groups) and the token's own scopes into a set of scopes. The two sets are compared (Resolve the scopes box in orange in {ref}`Figure 1 <token-request-chart>`), taking into account the scope hierarchy.
If the token's scopes are a subset of the token owner's scopes, the token is issued with the requested scopes; if not, JupyterHub will raise an error.
{ref}`Figure 1 <token-request-chart>` below illustrates the steps involved. The orange rectangles highlight where in the process the roles and scopes are resolved.
```{figure} ../images/rbac-token-request-chart.png
:align: center
:name: token-request-chart
Figure 1. Resolving roles and scopes during API token request
```
### Making an API request
With the RBAC framework, each authenticated JupyterHub API request is guarded by a scope decorator that specifies which scopes are required to gain the access to the API.
When an API request is performed, the requesting API token's scopes are again intersected with its owner's (yellow box in {ref}`Figure 2 <api-request-chart>`) to ensure the token does not grant more permissions than its owner has at the request time (e.g., due to changing/losing roles).
If the owner's roles do not include some scopes of the token's scopes, only the _intersection_ of the token's and owner's scopes will be used. For example, using a token with scope `users` whose owner's role scope is `read:users:name` will result in only the `read:users:name` scope being passed on. In the case of no _intersection_, an empty set of scopes will be used.
The passed scopes are compared to the scopes required to access the API as follows:
- if the API scopes are present within the set of passed scopes, the access is granted and the API returns its "full" response
- if that is not the case, another check is utilized to determine if subscopes of the required API scopes can be found in the passed scope set:
- if found, the RBAC framework employs the {ref}`filtering <vertical-filtering-target>` procedures to refine the API response to access only resource attributes corresponding to the passed scopes. For example, providing a scope `read:users:activity!group=class-C` for the _GET /users_ API will return a list of user models from group `class-C` containing only the `last_activity` attribute for each user model
- if not found, the access to API is denied
{ref}`Figure 2 <api-request-chart>` illustrates this process highlighting the steps where the role and scope resolutions as well as filtering occur in orange.
```{figure} ../images/rbac-api-request-chart.png
:align: center
:name: api-request-chart
Figure 2. Resolving roles and scopes when an API request is made
```

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

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@@ -1,130 +0,0 @@
# Use Cases
To determine which scopes a role should have, one can follow these steps:
1. Determine what actions the role holder should have/have not access to
2. Match the actions against the [JupyterHub's APIs](../reference/rest-api.rst)
3. Check which scopes are required to access the APIs
4. Combine scopes and subscopes if applicable
5. Customize the scopes with filters if needed
6. Define the role with required scopes and assign to users/services/groups/tokens
Below, different use cases are presented on how to use the RBAC framework.
## Service to cull idle servers
Finding and shutting down idle servers can save a lot of computational resources.
We can make use of [jupyterhub-idle-culler](https://github.com/jupyterhub/jupyterhub-idle-culler) to manage this for us.
Below follows a short tutorial on how to add a cull-idle service in the RBAC system.
1. Install the cull-idle server script with `pip install jupyterhub-idle-culler`.
2. Define a new service `idle-culler` and a new role for this service:
```python
# in jupyterhub_config.py
c.JupyterHub.services = [
{
"name": "idle-culler",
"command": [
sys.executable, "-m",
"jupyterhub_idle_culler",
"--timeout=3600"
],
}
]
c.JupyterHub.load_roles = [
{
"name": "idle-culler",
"description": "Culls idle servers",
"scopes": ["read:users:name", "read:users:activity", "servers"],
"services": ["idle-culler"],
}
]
```
```{important}
Note that in the RBAC system the `admin` field in the `idle-culler` service definition is omitted. Instead, the `idle-culler` role provides the service with only the permissions it needs.
If the optional actions of deleting the idle servers and/or removing inactive users are desired, **change the following scopes** in the `idle-culler` role definition:
- `servers` to `admin:servers` for deleting servers
- `read:users:name`, `read:users:activity` to `admin:users` for deleting users.
```
3. Restart JupyterHub to complete the process.
## API launcher
A service capable of creating/removing users and launching multiple servers should have access to:
1. _POST_ and _DELETE /users_
2. _POST_ and _DELETE /users/:name/server_ or _/users/:name/servers/:server_name_
3. Creating/deleting servers
The scopes required to access the API enpoints:
1. `admin:users`
2. `servers`
3. `admin:servers`
From the above, the role definition is:
```python
# in jupyterhub_config.py
c.JupyterHub.load_roles = [
{
"name": "api-launcher",
"description": "Manages servers",
"scopes": ["admin:users", "admin:servers"],
"services": [<service_name>]
}
]
```
If needed, the scopes can be modified to limit the permissions to e.g. a particular group with `!group=groupname` filter.
## Group admin roles
Roles can be used to specify different group member privileges.
For example, a group of students `class-A` may have a role allowing all group members to access information about their group. Teacher `johan`, who is a student of `class-A` but a teacher of another group of students `class-B`, can have additional role permitting him to access information about `class-B` students as well as start/stop their servers.
The roles can then be defined as follows:
```python
# in jupyterhub_config.py
c.JupyterHub.load_groups = {
'class-A': ['johan', 'student1', 'student2'],
'class-B': ['student3', 'student4']
}
c.JupyterHub.load_roles = [
{
'name': 'class-A-student',
'description': 'Grants access to information about the group',
'scopes': ['read:groups!group=class-A'],
'groups': ['class-A']
},
{
'name': 'class-B-student',
'description': 'Grants access to information about the group',
'scopes': ['read:groups!group=class-B'],
'groups': ['class-B']
},
{
'name': 'teacher',
'description': 'Allows for accessing information about teacher group members and starting/stopping their servers',
'scopes': [ 'read:users!group=class-B', 'servers!group=class-B'],
'users': ['johan']
}
]
```
In the above example, `johan` has privileges inherited from `class-A-student` role and the `teacher` role on top of those.
```{note}
The scope filters (`!group=`) limit the privileges only to the particular groups. `johan` can access the servers and information of `class-B` group members only.
```

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

View File

@@ -1,6 +1,6 @@
# Authenticators
The {class}`.Authenticator` is the mechanism for authorizing users to use the
The [Authenticator][] is the mechanism for authorizing users to use the
Hub and single user notebook servers.
## The default PAM Authenticator
@@ -37,7 +37,7 @@ with any provider, is also available.
## The Dummy Authenticator
When testing, it may be helpful to use the
{class}`jupyterhub.auth.DummyAuthenticator`. This allows for any username and
:class:`~jupyterhub.auth.DummyAuthenticator`. This allows for any username and
password unless if a global password has been set. Once set, any username will
still be accepted but the correct password will need to be provided.
@@ -89,6 +89,7 @@ class DictionaryAuthenticator(Authenticator):
return data['username']
```
#### Normalize usernames
Since the Authenticator and Spawner both use the same username,
@@ -110,10 +111,11 @@ When using `PAMAuthenticator`, you can set
normalize usernames using PAM (basically round-tripping them: username
to uid to username), which is useful in case you use some external
service that allows multiple usernames mapping to the same user (such
as ActiveDirectory, yes, this really happens). When
`pam_normalize_username` is on, usernames are _not_ normalized to
as ActiveDirectory, yes, this really happens). When
`pam_normalize_username` is on, usernames are *not* normalized to
lowercase.
#### Validate usernames
In most cases, there is a very limited set of acceptable usernames.
@@ -130,6 +132,7 @@ To only allow usernames that start with 'w':
c.Authenticator.username_pattern = r'w.*'
```
### How to write a custom authenticator
You can use custom Authenticator subclasses to enable authentication
@@ -137,11 +140,12 @@ via other mechanisms. One such example is using [GitHub OAuth][].
Because the username is passed from the Authenticator to the Spawner,
a custom Authenticator and Spawner are often used together.
For example, the Authenticator methods, {meth}`.Authenticator.pre_spawn_start`
and {meth}`.Authenticator.post_spawn_stop`, are hooks that can be used to do
For example, the Authenticator methods, [pre_spawn_start(user, spawner)][]
and [post_spawn_stop(user, spawner)][], are hooks that can be used to do
auth-related startup (e.g. opening PAM sessions) and cleanup
(e.g. closing PAM sessions).
See a list of custom Authenticators [on the wiki](https://github.com/jupyterhub/jupyterhub/wiki/Authenticators).
If you are interested in writing a custom authenticator, you can read
@@ -182,6 +186,7 @@ Additionally, configurable attributes for your authenticator will
appear in jupyterhub help output and auto-generated configuration files
via `jupyterhub --generate-config`.
### Authentication state
JupyterHub 0.8 adds the ability to persist state related to authentication,
@@ -215,22 +220,25 @@ To store auth_state, two conditions must be met:
export JUPYTERHUB_CRYPT_KEY=$(openssl rand -hex 32)
```
JupyterHub uses [Fernet](https://cryptography.io/en/latest/fernet/) to encrypt auth_state.
To facilitate key-rotation, `JUPYTERHUB_CRYPT_KEY` may be a semicolon-separated list of encryption keys.
If there are multiple keys present, the **first** key is always used to persist any new auth_state.
#### Using auth_state
Typically, if `auth_state` is persisted it is desirable to affect the Spawner environment in some way.
This may mean defining environment variables, placing certificate in the user's home directory, etc.
The {meth}`Authenticator.pre_spawn_start` method can be used to pass information from authenticator state
The `Authenticator.pre_spawn_start` method can be used to pass information from authenticator state
to Spawner environment:
```python
class MyAuthenticator(Authenticator):
async def authenticate(self, handler, data=None):
username = await identify_user(handler, data)
upstream_token = await token_for_user(username)
@gen.coroutine
def authenticate(self, handler, data=None):
username = yield identify_user(handler, data)
upstream_token = yield token_for_user(username)
return {
'name': username,
'auth_state': {
@@ -238,51 +246,20 @@ class MyAuthenticator(Authenticator):
},
}
async def pre_spawn_start(self, user, spawner):
@gen.coroutine
def pre_spawn_start(self, user, spawner):
"""Pass upstream_token to spawner via environment variable"""
auth_state = await user.get_auth_state()
auth_state = yield user.get_auth_state()
if not auth_state:
# auth_state not enabled
return
spawner.environment['UPSTREAM_TOKEN'] = auth_state['upstream_token']
```
(authenticator-groups)=
## Authenticator-managed group membership
:::{versionadded} 2.2
:::
Some identity providers may have their own concept of group membership that you would like to preserve in JupyterHub.
This is now possible with `Authenticator.managed_groups`.
You can set the config:
```python
c.Authenticator.manage_groups = True
```
to enable this behavior.
The default is False for Authenticators that ship with JupyterHub,
but may be True for custom Authenticators.
Check your Authenticator's documentation for manage_groups support.
If True, {meth}`.Authenticator.authenticate` and {meth}`.Authenticator.refresh_user` may include a field `groups`
which is a list of group names the user should be a member of:
- Membership will be added for any group in the list
- Membership in any groups not in the list will be revoked
- Any groups not already present in the database will be created
- If `None` is returned, no changes are made to the user's group membership
If authenticator-managed groups are enabled,
all group-management via the API is disabled.
## pre_spawn_start and post_spawn_stop hooks
Authenticators uses two hooks, {meth}`.Authenticator.pre_spawn_start` and
{meth}`.Authenticator.post_spawn_stop(user, spawner)` to add pass additional state information
Authenticators uses two hooks, [pre_spawn_start(user, spawner)][] and
[post_spawn_stop(user, spawner)][] to add pass additional state information
between the authenticator and a spawner. These hooks are typically used auth-related
startup, i.e. opening a PAM session, and auth-related cleanup, i.e. closing a
PAM session.
@@ -291,7 +268,11 @@ PAM session.
Beginning with version 0.8, JupyterHub is an OAuth provider.
[pam]: https://en.wikipedia.org/wiki/Pluggable_authentication_module
[oauth]: https://en.wikipedia.org/wiki/OAuth
[github oauth]: https://developer.github.com/v3/oauth/
[oauthenticator]: https://github.com/jupyterhub/oauthenticator
[Authenticator]: https://github.com/jupyterhub/jupyterhub/blob/master/jupyterhub/auth.py
[PAM]: https://en.wikipedia.org/wiki/Pluggable_authentication_module
[OAuth]: https://en.wikipedia.org/wiki/OAuth
[GitHub OAuth]: https://developer.github.com/v3/oauth/
[OAuthenticator]: https://github.com/jupyterhub/oauthenticator
[pre_spawn_start(user, spawner)]: https://jupyterhub.readthedocs.io/en/latest/api/auth.html#jupyterhub.auth.Authenticator.pre_spawn_start
[post_spawn_stop(user, spawner)]: https://jupyterhub.readthedocs.io/en/latest/api/auth.html#jupyterhub.auth.Authenticator.post_spawn_stop

View File

@@ -3,17 +3,18 @@
In this example, we show a configuration file for a fairly standard JupyterHub
deployment with the following assumptions:
- Running JupyterHub on a single cloud server
- Using SSL on the standard HTTPS port 443
- Using GitHub OAuth (using oauthenticator) for login
- Using the default spawner (to configure other spawners, uncomment and edit
* Running JupyterHub on a single cloud server
* Using SSL on the standard HTTPS port 443
* Using GitHub OAuth (using oauthenticator) for login
* Using the default spawner (to configure other spawners, uncomment and edit
`spawner_class` as well as follow the instructions for your desired spawner)
- Users exist locally on the server
- Users' notebooks to be served from `~/assignments` to allow users to browse
* Users exist locally on the server
* Users' notebooks to be served from `~/assignments` to allow users to browse
for notebooks within other users' home directories
- You want the landing page for each user to be a `Welcome.ipynb` notebook in
* You want the landing page for each user to be a `Welcome.ipynb` notebook in
their assignments directory.
- All runtime files are put into `/srv/jupyterhub` and log files in `/var/log`.
* All runtime files are put into `/srv/jupyterhub` and log files in `/var/log`.
The `jupyterhub_config.py` file would have these settings:

View File

@@ -6,12 +6,12 @@ SSL port `443`. This could be useful if the JupyterHub server machine is also
hosting other domains or content on `443`. The goal in this example is to
satisfy the following:
- JupyterHub is running on a server, accessed _only_ via `HUB.DOMAIN.TLD:443`
- On the same machine, `NO_HUB.DOMAIN.TLD` strictly serves different content,
* JupyterHub is running on a server, accessed *only* via `HUB.DOMAIN.TLD:443`
* On the same machine, `NO_HUB.DOMAIN.TLD` strictly serves different content,
also on port `443`
- `nginx` or `apache` is used as the public access point (which means that
only nginx/apache will bind to `443`)
- After testing, the server in question should be able to score at least an A on the
* `nginx` or `apache` is used as the public access point (which means that
only nginx/apache will bind to `443`)
* After testing, the server in question should be able to score at least an A on the
Qualys SSL Labs [SSL Server Test](https://www.ssllabs.com/ssltest/)
Let's start out with needed JupyterHub configuration in `jupyterhub_config.py`:
@@ -86,7 +86,6 @@ server {
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection $connection_upgrade;
proxy_set_header X-Scheme $scheme;
proxy_buffering off;
}
@@ -144,7 +143,6 @@ Now restart `nginx`, restart the JupyterHub, and enjoy accessing
`https://NO_HUB.DOMAIN.TLD`.
### SELinux permissions for nginx
On distributions with SELinux enabled (e.g. Fedora), one may encounter permission errors
when the nginx service is started.
@@ -156,16 +154,16 @@ semanage port -a -t http_port_t -p tcp 8000
setsebool -P httpd_can_network_relay 1
setsebool -P httpd_can_network_connect 1
```
Replace 8000 with the port the jupyterhub server is running from.
## Apache
As with nginx above, you can use [Apache](https://httpd.apache.org) as the reverse proxy.
First, we will need to enable the apache modules that we are going to need:
```bash
a2enmod ssl rewrite proxy headers proxy_http proxy_wstunnel
a2enmod ssl rewrite proxy proxy_http proxy_wstunnel
```
Our Apache configuration is equivalent to the nginx configuration above:
@@ -188,24 +186,13 @@ Listen 443
ServerName HUB.DOMAIN.TLD
# enable HTTP/2, if available
Protocols h2 http/1.1
# HTTP Strict Transport Security (mod_headers is required) (63072000 seconds)
Header always set Strict-Transport-Security "max-age=63072000"
# configure SSL
SSLEngine on
SSLCertificateFile /etc/letsencrypt/live/HUB.DOMAIN.TLD/fullchain.pem
SSLCertificateKeyFile /etc/letsencrypt/live/HUB.DOMAIN.TLD/privkey.pem
SSLProtocol All -SSLv2 -SSLv3
SSLOpenSSLConfCmd DHParameters /etc/ssl/certs/dhparam.pem
# intermediate configuration from ssl-config.mozilla.org (2022-03-03)
# Please note, that this configuration might be out-dated - please update it accordingly using https://ssl-config.mozilla.org/
SSLProtocol all -SSLv3 -TLSv1 -TLSv1.1
SSLCipherSuite ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES128-GCM-SHA256:ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-CHACHA20-POLY1305:ECDHE-RSA-CHACHA20-POLY1305:DHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384
SSLHonorCipherOrder off
SSLSessionTickets off
SSLCipherSuite EECDH+AESGCM:EDH+AESGCM:AES256+EECDH:AES256+EDH
# Use RewriteEngine to handle websocket connection upgrades
RewriteEngine On
@@ -219,29 +206,26 @@ Listen 443
# proxy to JupyterHub
ProxyPass http://127.0.0.1:8000/
ProxyPassReverse http://127.0.0.1:8000/
RequestHeader set "X-Forwarded-Proto" expr=%{REQUEST_SCHEME}
</Location>
</VirtualHost>
```
In case of the need to run the jupyterhub under /jhub/ or other location please use the below configurations:
- JupyterHub running locally at http://127.0.0.1:8000/jhub/ or other location
httpd.conf amendments:
```bash
RewriteRule /jhub/(.*) ws://127.0.0.1:8000/jhub/$1 [P,L]
RewriteRule /jhub/(.*) http://127.0.0.1:8000/jhub/$1 [P,L]
RewriteRule /jhub/(.*) ws://127.0.0.1:8000/jhub/$1 [NE.P,L]
RewriteRule /jhub/(.*) http://127.0.0.1:8000/jhub/$1 [NE,P,L]
ProxyPass /jhub/ http://127.0.0.1:8000/jhub/
ProxyPassReverse /jhub/ http://127.0.0.1:8000/jhub/
```
```
jupyterhub_config.py amendments:
```bash
--The public facing URL of the whole JupyterHub application.
--This is the address on which the proxy will bind. Sets protocol, ip, base_url
c.JupyterHub.bind_url = 'http://127.0.0.1:8000/jhub/'
```
```bash
--The public facing URL of the whole JupyterHub application.
--This is the address on which the proxy will bind. Sets protocol, ip, base_url
c.JupyterHub.bind_url = 'http://127.0.0.1:8000/jhub/'
```

View File

@@ -9,7 +9,7 @@ Only do this if you are very sure you must.
There are many Authenticators and Spawners available for JupyterHub. Some, such
as DockerSpawner or OAuthenticator, do not need any elevated permissions. This
document describes how to get the full default behavior of JupyterHub while
running notebook servers as real system users on a shared system without
running notebook servers as real system users on a shared system without
running the Hub itself as root.
Since JupyterHub needs to spawn processes as other users, the simplest way
@@ -50,9 +50,10 @@ To do this we add to `/etc/sudoers` (use `visudo` for safe editing of sudoers):
- specify the list of users `JUPYTER_USERS` for whom `rhea` can spawn servers
- set the command `JUPYTER_CMD` that `rhea` can execute on behalf of users
- give `rhea` permission to run `JUPYTER_CMD` on behalf of `JUPYTER_USERS`
- give `rhea` permission to run `JUPYTER_CMD` on behalf of `JUPYTER_USERS`
without entering a password
For example:
```bash
@@ -90,16 +91,16 @@ $ adduser -G jupyterhub newuser
Test that the new user doesn't need to enter a password to run the sudospawner
command.
This should prompt for your password to switch to rhea, but _not_ prompt for
This should prompt for your password to switch to rhea, but *not* prompt for
any password for the second switch. It should show some help output about
logging options:
```bash
$ sudo -u rhea sudo -n -u $USER /usr/local/bin/sudospawner --help
Usage: /usr/local/bin/sudospawner [OPTIONS]
Options:
--help show this help information
...
```
@@ -150,13 +151,12 @@ We want our new user to be able to read the shadow passwords, so add it to the s
$ sudo usermod -a -G shadow rhea
```
If you want jupyterhub to serve pages on a restricted port (such as port 80 for http),
If you want jupyterhub to serve pages on a restricted port (such as port 80 for http),
then you will need to give `node` permission to do so:
```bash
sudo setcap 'cap_net_bind_service=+ep' /usr/bin/node
```
However, you may want to further understand the consequences of this.
You may also be interested in limiting the amount of CPU any process can use
@@ -165,6 +165,7 @@ distributions' packaging system. This can be used to keep any user's process
from using too much CPU cycles. You can configure it accoring to [these
instructions](http://ubuntuforums.org/showthread.php?t=992706).
### Shadow group (FreeBSD)
**NOTE:** This has not been tested and may not work as expected.
@@ -185,7 +186,7 @@ $ sudo chgrp shadow /etc/master.passwd
$ sudo chmod g+r /etc/master.passwd
```
We want our new user to be able to read the shadow passwords, so add it to the
We want our new user to be able to read the shadow passwords, so add it to the
shadow group:
```bash
@@ -219,7 +220,7 @@ Finally, start the server as our newly configured user, `rhea`:
```bash
$ cd /etc/jupyterhub
$ sudo -u rhea jupyterhub --JupyterHub.spawner_class=sudospawner.SudoSpawner
```
```
And try logging in.
@@ -227,7 +228,7 @@ And try logging in.
If you still get a generic `Permission denied` `PermissionError`, it's possible SELinux is blocking you.
Here's how you can make a module to allow this.
First, put this in a file named `sudo_exec_selinux.te`:
First, put this in a file named `sudo_exec_selinux.te`:
```bash
module sudo_exec_selinux 1.1;

View File

@@ -22,18 +22,20 @@ This section will focus on user environments, including:
- Installing kernelspecs
- Using containers vs. multi-user hosts
## Installing packages
To make packages available to users, you generally will install packages
system-wide or in a shared environment.
This installation location should always be in the same environment that
`jupyterhub-singleuser` itself is installed in, and must be _readable and
executable_ by your users. If you want users to be able to install additional
packages, it must also be _writable_ by your users.
`jupyterhub-singleuser` itself is installed in, and must be *readable and
executable* by your users. If you want users to be able to install additional
packages, it must also be *writable* by your users.
If you are using a standard system Python install, you would use:
```bash
sudo python3 -m pip install numpy
```
@@ -45,6 +47,7 @@ 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.
## Configuring Jupyter and IPython
[Jupyter](https://jupyter-notebook.readthedocs.io/en/stable/config_overview.html)
@@ -61,7 +64,6 @@ users. It's generally more efficient to configure user environments "system-wide
and it's a good idea to avoid creating files in users' home directories.
The typical locations for these config files are:
- **system-wide** in `/etc/{jupyter|ipython}`
- **env-wide** (environment wide) in `{sys.prefix}/etc/{jupyter|ipython}`.
@@ -76,32 +78,20 @@ 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:
- Where you see `jupyter_server_config`, use `jupyter_notebook_config`
- Where you see `NotebookApp`, use `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`
notebook ≥ 5.4), set the following in the `/etc/jupyter/jupyter_notebook_config.py`
file:
```python
# shutdown the server after no activity for an hour
c.ServerApp.shutdown_no_activity_timeout = 60 * 60
c.NotebookApp.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
@@ -125,10 +115,11 @@ Assuming I have a Python 2 and Python 3 environment that I want to make
sure are available, I can install their specs system-wide (in /usr/local) with:
```bash
/path/to/python3 -m ipykernel install --prefix=/usr/local
/path/to/python2 -m ipykernel install --prefix=/usr/local
/path/to/python3 -m IPython kernel install --prefix=/usr/local
/path/to/python2 -m IPython kernel install --prefix=/usr/local
```
## Multi-user hosts vs. Containers
There are two broad categories of user environments that depend on what
@@ -150,8 +141,8 @@ When JupyterHub uses **container-based** Spawners (e.g. KubeSpawner or
DockerSpawner), the 'system-wide' environment is really the container image
which you are using for users.
In both cases, you want to _avoid putting configuration in user home
directories_ because users can change those configuration settings. Also,
In both cases, you want to *avoid putting configuration in user home
directories* because users can change those configuration settings. Also,
home directories typically persist once they are created, so they are
difficult for admins to update later.
@@ -188,41 +179,3 @@ The number of named servers per user can be limited by setting
```python
c.JupyterHub.named_server_limit_per_user = 5
```
(classic-notebook-ui)=
## Switching back to 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.
You can switch to using the legacy Jupyter Notebook server by setting 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 singleuser UI, if available,
which is based on the [Jupyter Server][],
no longer the legacy [Jupyter Notebook][] server.
JupyterHub prior to 2.0 launched the legacy notebook server (`jupyter notebook`),
and Jupyter server could be selected by specifying
```python
# jupyterhub_config.py
c.Spawner.cmd = ["jupyter-labhub"]
```
or for an otherwise customized Jupyter Server app,
set the environment variable:
```bash
export JUPYTERHUB_SINGLEUSER_APP='jupyter_server.serverapp.ServerApp'
```
:::

View File

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

View File

@@ -16,12 +16,9 @@ what happens under-the-hood when you deploy and configure your JupyterHub.
proxy
separate-proxy
rest
rest-api
server-api
monitoring
database
templates
api-only
../events/index
config-user-env
config-examples
@@ -29,4 +26,3 @@ what happens under-the-hood when you deploy and configure your JupyterHub.
config-proxy
config-sudo
config-reference
oauth

View File

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

View File

@@ -54,7 +54,7 @@ class MyProxy(Proxy):
"""Stop the proxy"""
```
These methods **may** be coroutines.
These methods **may** be coroutines.
`c.Proxy.should_start` is a configurable flag that determines whether the
Hub should call these methods when the Hub itself starts and stops.
@@ -103,7 +103,7 @@ route to be proxied, such as `/user/name/`. A routespec will:
When adding a route, JupyterHub may pass a JSON-serializable dict as a `data`
argument that should be attached to the proxy route. When that route is
retrieved, the `data` argument should be returned as well. If your proxy
retrieved, the `data` argument should be returned as well. If your proxy
implementation doesn't support storing data attached to routes, then your
Python wrapper may have to handle storing the `data` piece itself, e.g in a
simple file or database.
@@ -136,7 +136,7 @@ async def delete_route(self, routespec):
### Retrieving routes
For retrieval, you only _need_ to implement a single method that retrieves all
For retrieval, you only *need* to implement a single method that retrieves all
routes. The return value for this function should be a dictionary, keyed by
`routespect`, of dicts whose keys are the same three arguments passed to
`add_route` (`routespec`, `target`, `data`)
@@ -220,11 +220,3 @@ previously required.
Additionally, configurable attributes for your proxy will
appear in jupyterhub help output and auto-generated configuration files
via `jupyterhub --generate-config`.
### Index of proxies
A list of the proxies that are currently available for JupyterHub (that we know about).
1. [`jupyterhub/configurable-http-proxy`](https://github.com/jupyterhub/configurable-http-proxy) The default proxy which uses node-http-proxy
2. [`jupyterhub/traefik-proxy`](https://github.com/jupyterhub/traefik-proxy) The proxy which configures traefik proxy server for jupyterhub
3. [`AbdealiJK/configurable-http-proxy`](https://github.com/AbdealiJK/configurable-http-proxy) A pure python implementation of the configurable-http-proxy

View File

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

View File

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

View File

@@ -1,5 +1,3 @@
(rest-api)=
# Using JupyterHub's REST API
This section will give you information on:
@@ -19,7 +17,6 @@ such as:
- adding or removing users
- stopping or starting single user notebook servers
- authenticating services
- communicating with an individual Jupyter server's REST API
A [REST](https://en.wikipedia.org/wiki/Representational_state_transfer)
API provides a standard way for users to get and send information to the
@@ -30,7 +27,8 @@ Hub.
To send requests using JupyterHub API, you must pass an API token with
the request.
The preferred way of generating an API token is:
As of [version 0.6.0](../changelog.md), the preferred way of
generating an API token is:
```bash
openssl rand -hex 32
@@ -50,34 +48,33 @@ jupyterhub token <username>
This command generates a random string to use as a token and registers
it for the given user with the Hub's database.
In [version 0.8.0](../changelog.md), a token request page for
In [version 0.8.0](../changelog.md), a TOKEN request page for
generating an API token is available from the JupyterHub user interface:
![Request API token page](../images/token-request.png)
![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)
## Assigning permissions to a token
## Add API tokens to the config file
Prior to JupyterHub 2.0, there were two levels of permissions:
**This is deprecated. We are in no rush to remove this feature,
but please consider if service tokens are right for you.**
1. user, and
2. admin
You may also add a dictionary of API tokens and usernames to the hub's
configuration file, `jupyterhub_config.py` (note that
the **key** is the 'secret-token' while the **value** is the 'username'):
where a token would always have full permissions to do whatever its owner could do.
In JupyterHub 2.0,
specific permissions are now defined as 'scopes',
and can be assigned both at the user/service level,
and at the individual token level.
This allows e.g. a user with full admin permissions to request a token with limited permissions.
```python
c.JupyterHub.api_tokens = {
'secret-token': 'username',
}
```
### Updating to admin services
The `api_tokens` configuration has been softly deprecated since the introduction of services.
We have no plans to remove it,
but deployments are encouraged to use service configuration instead.
but users are encouraged to use service configuration instead.
If you have been using `api_tokens` to create an admin user
and a token for that user to perform some automations,
@@ -91,39 +88,19 @@ c.JupyterHub.api_tokens = {
}
```
This can be updated to create a service, with the following configuration:
This can be updated to create an admin service, with the following configuration:
```python
c.JupyterHub.services = [
{
# give the token a name
"name": "service-admin",
"name": "service-token",
"admin": True,
"api_token": "secret-token",
# "admin": True, # if using JupyterHub 1.x
},
]
# roles are new in JupyterHub 2.0
# prior to 2.0, only 'admin': True or False
# was available
c.JupyterHub.load_roles = [
{
"name": "service-role",
"scopes": [
# specify the permissions the token should have
"admin:users",
],
"services": [
# assign the service the above permissions
"service-admin",
],
}
]
```
The token will have the permissions listed in the role
(see [scopes][] for a list of available permissions),
The token will have the same admin permissions,
but there will no longer be a user account created to house it.
The main noticeable difference is that there will be no notebook server associated with the account
and the service will not show up in the various user list pages and APIs.
@@ -135,7 +112,7 @@ Authorization header.
### Use requests
Using the popular Python [requests](https://docs.python-requests.org)
Using the popular Python [requests](http://docs.python-requests.org/en/master/)
library, here's example code to make an API request for the users of a JupyterHub
deployment. An API GET request is made, and the request sends an API token for
authorization. The response contains information about the users:
@@ -147,9 +124,9 @@ api_url = 'http://127.0.0.1:8081/hub/api'
r = requests.get(api_url + '/users',
headers={
'Authorization': f'token {token}',
}
)
'Authorization': 'token %s' % token,
}
)
r.raise_for_status()
users = r.json()
@@ -167,95 +144,19 @@ data = {'name': 'mygroup', 'users': ['user1', 'user2']}
r = requests.post(api_url + '/groups/formgrade-data301/users',
headers={
'Authorization': f'token {token}',
},
json=data,
'Authorization': 'token %s' % token,
},
json=data
)
r.raise_for_status()
r.json()
```
The same API token can also authorize access to the [Jupyter Notebook REST API][]
provided by notebook servers managed by JupyterHub if it has the necessary `access:users:servers` scope:
provided by notebook servers managed by JupyterHub if one of the following is true:
(api-pagination)=
## Paginating API requests
```{versionadded} 2.0
```
Pagination is available through the `offset` and `limit` query parameters on
list endpoints, which can be used to return ideally sized windows of results.
Here's example code demonstrating pagination on the `GET /users`
endpoint to fetch the first 20 records.
```python
import os
import requests
api_url = 'http://127.0.0.1:8081/hub/api'
r = requests.get(
api_url + '/users?offset=0&limit=20',
headers={
"Accept": "application/jupyterhub-pagination+json",
"Authorization": f"token {token}",
},
)
r.raise_for_status()
r.json()
```
For backward-compatibility, the default structure of list responses is unchanged.
However, this lacks pagination information (e.g. is there a next page),
so if you have enough users that they won't fit in the first response,
it is a good idea to opt-in to the new paginated list format.
There is a new schema for list responses which include pagination information.
You can request this by including the header:
```
Accept: application/jupyterhub-pagination+json
```
with your request, in which case a response will look like:
```python
{
"items": [
{
"name": "username",
"kind": "user",
...
},
],
"_pagination": {
"offset": 0,
"limit": 20,
"total": 50,
"next": {
"offset": 20,
"limit": 20,
"url": "http://127.0.0.1:8081/hub/api/users?limit=20&offset=20"
}
}
}
```
where the list results (same as pre-2.0) will be in `items`,
and pagination info will be in `_pagination`.
The `next` field will include the offset, limit, and URL for requesting the next page.
`next` will be `null` if there is no next page.
Pagination is governed by two configuration options:
- `JupyterHub.api_page_default_limit` - the page size, if `limit` is unspecified in the request
and the new pagination API is requested
(default: 50)
- `JupyterHub.api_page_max_limit` - the maximum page size a request can ask for (default: 200)
Pagination is enabled on the `GET /users`, `GET /groups`, and `GET /proxy` REST endpoints.
1. The token is for the same user as the owner of the notebook
2. The token is tied to an admin user or service **and** `c.JupyterHub.admin_access` is set to `True`
## Enabling users to spawn multiple named-servers via the API
@@ -268,7 +169,7 @@ curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/us
```
With the named-server functionality, it's now possible to launch more than one
specifically named servers against a given user. This could be used, for instance,
specifically named servers against a given user. This could be used, for instance,
to launch each server based on a different image.
First you must enable named-servers by including the following setting in the `jupyterhub_config.py` file.
@@ -286,7 +187,6 @@ hub:
```
With that setting in place, a new named-server is activated like this:
```bash
curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/users/<user>/servers/<serverA>"
curl -X POST -H "Authorization: token <token>" "http://127.0.0.1:8081/hub/api/users/<user>/servers/<serverB>"
@@ -301,10 +201,15 @@ will need to be able to handle the case of multiple servers per user and ensure
uniqueness of names, particularly if servers are spawned via docker containers
or kubernetes pods.
## Learn more about the API
You can see the full [JupyterHub REST API][] for details.
You can see the full [JupyterHub REST API][] for details. This REST API Spec can
be viewed in a more [interactive style on swagger's petstore][].
Both resources contain the same information and differ only in its display.
Note: The Swagger specification is being renamed the [OpenAPI Initiative][].
[openapi initiative]: https://www.openapis.org/
[jupyterhub rest api]: ./rest-api
[jupyter notebook rest api]: https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/jupyter/notebook/HEAD/notebook/services/api/api.yaml
[interactive style on swagger's petstore]: http://petstore.swagger.io/?url=https://raw.githubusercontent.com/jupyterhub/jupyterhub/master/docs/rest-api.yml#!/default
[OpenAPI Initiative]: https://www.openapis.org/
[JupyterHub REST API]: ./rest-api
[Jupyter Notebook REST API]: http://petstore.swagger.io/?url=https://raw.githubusercontent.com/jupyter/notebook/master/notebook/services/api/api.yaml

View File

@@ -1,26 +1,28 @@
# Running proxy separately from the hub
## Background
The thing which users directly connect to is the proxy, by default
`configurable-http-proxy`. The proxy either redirects users to the
`configurable-http-proxy`. The proxy either redirects users to the
hub (for login and managing servers), or to their own single-user
servers. Thus, as long as the proxy stays running, access to existing
servers. Thus, as long as the proxy stays running, access to existing
servers continues, even if the hub itself restarts or goes down.
When you first configure the hub, you may not even realize this
because the proxy is automatically managed by the hub. This is great
because the proxy is automatically managed by the hub. This is great
for getting started and even most use, but everytime you restart the
hub, all user connections also get restarted. But it's also simple to
hub, all user connections also get restarted. But it's also simple to
run the proxy as a service separate from the hub, so that you are free
to reconfigure the hub while only interrupting users who are currently
actively starting the hub.
The default JupyterHub proxy is
[configurable-http-proxy](https://github.com/jupyterhub/configurable-http-proxy),
and that page has some docs. If you are using a different proxy, such
and that page has some docs. If you are using a different proxy, such
as Traefik, these instructions are probably not relevant to you.
## Configuration options
`c.JupyterHub.cleanup_servers = False` should be set, which tells the
@@ -35,20 +37,24 @@ it yourself).
token for authenticating communication with the proxy.
`c.ConfigurableHTTPProxy.api_url = 'http://localhost:8001'` should be
set to the URL which the hub uses to connect _to the proxy's API_.
set to the URL which the hub uses to connect *to the proxy's API*.
## Proxy configuration
You need to configure a service to start the proxy. An example
command line for this is `configurable-http-proxy --ip=127.0.0.1 --port=8000 --api-ip=127.0.0.1 --api-port=8001 --default-target=http://localhost:8081 --error-target=http://localhost:8081/hub/error`. (Details for how to
You need to configure a service to start the proxy. An example
command line for this is `configurable-http-proxy --ip=127.0.0.1
--port=8000 --api-ip=127.0.0.1 --api-port=8001
--default-target=http://localhost:8081
--error-target=http://localhost:8081/hub/error`. (Details for how to
do this is out of scope for this tutorial - for example it might be a
systemd service on within another docker cotainer). The proxy has no
systemd service on within another docker cotainer). The proxy has no
configuration files, all configuration is via the command line and
environment variables.
`--api-ip` and `--api-port` (which tells the proxy where to listen) should match the hub's `ConfigurableHTTPProxy.api_url`.
`--ip`, `-port`, and other options configure the _user_ connections to the proxy.
`--ip`, `-port`, and other options configure the *user* connections to the proxy.
`--default-target` and `--error-target` should point to the hub, and used when users navigate to the proxy originally.
@@ -57,16 +63,18 @@ match the token given to `c.ConfigurableHTTPProxy.auth_token`.
You should check the [configurable-http-proxy
options](https://github.com/jupyterhub/configurable-http-proxy) to see
what other options are needed, for example SSL options. Note that
what other options are needed, for example SSL options. Note that
these are configured in the hub if the hub is starting the proxy - you
need to move the options to here.
## Docker image
You can use [jupyterhub configurable-http-proxy docker
image](https://hub.docker.com/r/jupyterhub/configurable-http-proxy/)
to run the proxy.
## See also
- [jupyterhub configurable-http-proxy](https://github.com/jupyterhub/configurable-http-proxy)
* [jupyterhub configurable-http-proxy](https://github.com/jupyterhub/configurable-http-proxy)

View File

@@ -1,369 +0,0 @@
# Starting servers with the JupyterHub API
JupyterHub's [REST API][] allows launching servers on behalf of users
without ever interacting with the JupyterHub UI.
This allows you to build services launching Jupyter-based services for users
without relying on the JupyterHub UI at all,
enabling a variety of user/launch/lifecycle patterns not natively supported by JupyterHub,
without needing to develop all the server management features of JupyterHub Spawners and/or Authenticators.
[BinderHub][] is an example of such an application.
[binderhub]: https://binderhub.readthedocs.io
[rest api]: ../reference/rest.md
This document provides an example of working with the JupyterHub API to
manage servers for users.
In particular, we will cover how to:
1. [check status of servers](checking)
2. [start servers](starting)
3. [wait for servers to be ready](waiting)
4. [communicate with servers](communicating)
5. [stop servers](stopping)
(checking)=
## Checking server status
Requesting information about a user includes a `servers` field,
which is a dictionary.
```
GET /hub/api/users/:username
```
**Required scope: `read:servers`**
```json
{
"admin": false,
"groups": [],
"pending": null,
"server": null,
"name": "test-1",
"kind": "user",
"last_activity": "2021-08-03T18:12:46.026411Z",
"created": "2021-08-03T18:09:59.767600Z",
"roles": ["user"],
"servers": {}
}
```
If the `servers` dict is empty, the user has no running servers.
The keys of the `servers` dict are server names as strings.
Many JupyterHub deployments only use the 'default' server,
which has the empty string `''` for a name.
In this case, the servers dict will always have either zero or one elements.
This is the servers dict when the user's default server is fully running and ready:
```json
"servers": {
"": {
"name": "",
"last_activity": "2021-08-03T18:48:35.934000Z",
"started": "2021-08-03T18:48:29.093885Z",
"pending": null,
"ready": true,
"url": "/user/test-1/",
"user_options": {},
"progress_url": "/hub/api/users/test-1/server/progress"
}
}
```
Key properties of a server:
name
: the server's name. Always the same as the key in `servers`
ready
: boolean. If true, the server can be expected to respond to requests at `url`.
pending
: `null` or a string indicating a transitional state (such as `start` or `stop`).
Will always be `null` if `ready` is true,
and will always be a string if `ready` is false.
url
: The server's url (just the path, e.g. `/users/:name/:servername/`)
where the server can be accessed if `ready` is true.
progress_url
: The API url path (starting with `/hub/api`)
where the progress API can be used to wait for the server to be ready.
See below for more details on the progress API.
last_activity
: ISO8601 timestamp indicating when activity was last observed on the server
started
: ISO801 timestamp indicating when the server was last started
We've seen the `servers` model with no servers and with one `ready` server.
Here is what it looks like immediately after requesting a server launch,
while the server is not ready yet:
```json
"servers": {
"": {
"name": "",
"last_activity": "2021-08-03T18:48:29.093885Z",
"started": "2021-08-03T18:48:29.093885Z",
"pending": "spawn",
"ready": false,
"url": "/user/test-1/",
"user_options": {},
"progress_url": "/hub/api/users/test-1/server/progress"
}
}
```
Note that `ready` is false and `pending` is `spawn`.
This means that the server is not ready
(attempting to access it may not work)
because it isn't finished spawning yet.
We'll get more into that below in [waiting for a server][].
[waiting for a server]: waiting
(starting)=
## Starting servers
To start a server, make the request
```
POST /hub/api/users/:username/servers/[:servername]
```
**Required scope: `servers`**
(omit servername for the default server)
Assuming the request was valid,
there are two possible responses:
201 Created
: This status code means the launch completed and the server is ready.
It should be available at the server's URL immediately.
202 Accepted
: This is the more likely response,
and means that the server has begun launching,
but isn't immediately ready.
The server has `pending: 'spawn'` at this point.
_Aside: how quickly JupyterHub responds with `202 Accepted` is governed by the `slow_spawn_timeout` tornado setting._
(waiting)=
## Waiting for a server
If you are starting a server via the API,
there's a good change you want to know when it's ready.
There are two ways to do with:
1. {ref}`Polling the server model <polling>`
2. the {ref}`progress API <progress>`
(polling)=
### Polling the server model
The simplest way to check if a server is ready
is to request the user model.
If:
1. the server name is in the user's `servers` model, and
2. `servers['servername']['ready']` is true
A Python example, checking if a server is ready:
```python
def server_ready(hub_url, user, server_name="", token):
r = requests.get(
f"{hub_url}/hub/api/users/{user}/servers/{server_name}",
headers={"Authorization": f"token {token}"},
)
r.raise_for_status()
user_model = r.json()
servers = user_model.get("servers", {})
if server_name not in servers:
return False
server = servers[server_name]
if server['ready']:
print(f"Server {user}/{server_name} ready at {server['url']}")
return True
else:
print(f"Server {user}/{server_name} not ready, pending {server['pending']}")
return False
```
You can keep making this check until `ready` is true.
(progress)=
### Progress API
The most _efficient_ way to wait for a server to start is the progress API.
The progress URL is available in the server model under `progress_url`,
and has the form `/hub/api/users/:user/servers/:servername/progress`.
_the default server progress can be accessed at `:user/servers//progress` or `:user/server/progress`_
```
GET /hub/api/users/:user/servers/:servername/progress
```
**Required scope: `read:servers`**
This is an [EventStream][] API.
In an event stream, messages are _streamed_ and delivered on lines of the form:
```
data: {"progress": 10, "message": "...", ...}
```
where the line after `data:` contains a JSON-serialized dictionary.
Lines that do not start with `data:` should be ignored.
[eventstream]: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#examples
progress events have the form:
```python
{
"progress": 0-100,
"message": "",
"ready": True, # or False
}
```
progress
: integer, 0-100
message
: string message describing progress stages
ready
: present and true only for the last event when the server is ready
url
: only present if `ready` is true; will be the server's url
the progress API can be used even with fully ready servers.
If the server is ready,
there will only be one event that looks like:
```json
{
"progress": 100,
"ready": true,
"message": "Server ready at /user/test-1/",
"html_message": "Server ready at <a href=\"/user/test-1/\">/user/test-1/</a>",
"url": "/user/test-1/"
}
```
where `ready` and `url` are the same as in the server model (`ready` will always be true).
A typical complete stream from the event-stream API:
```
data: {"progress": 0, "message": "Server requested"}
data: {"progress": 50, "message": "Spawning server..."}
data: {"progress": 100, "ready": true, "message": "Server ready at /user/test-user/", "html_message": "Server ready at <a href=\"/user/test-user/\">/user/test-user/</a>", "url": "/user/test-user/"}
```
Here is a Python example for consuming an event stream:
```{literalinclude} ../../../examples/server-api/start-stop-server.py
:language: python
:pyobject: event_stream
```
(stopping)=
## Stopping servers
Servers can be stopped with a DELETE request:
```
DELETE /hub/api/users/:user/servers/[:servername]
```
**Required scope: `servers`**
Like start, delete may not complete immediately.
The DELETE request has two possible response codes:
204 Deleted
: This status code means the delete completed and the server is fully stopped.
It will now be absent from the user `servers` model.
202 Accepted
: Like start, `202` means your request was accepted,
but is not yet complete.
The server has `pending: 'stop'` at this point.
Unlike start, there is no progress API for stop.
To wait for stop to finish, you must poll the user model
and wait for the server to disappear from the user `servers` model.
```{literalinclude} ../../../examples/server-api/start-stop-server.py
:language: python
:pyobject: stop_server
```
(communicating)=
## Communicating with servers
JupyterHub tokens with the the `access:servers` scope
can be used to communicate with servers themselves.
This can be the same token you used to launch your service.
```{note}
Access scopes are new in JupyterHub 2.0.
To access servers in JupyterHub 1.x,
a token must be owned by the same user as the server,
*or* be an admin token if admin_access is enabled.
```
The URL returned from a server model is the url path suffix,
e.g. `/user/:name/` to append to the jupyterhub base URL.
For instance, `{hub_url}{server_url}`,
where `hub_url` would be e.g. `http://127.0.0.1:8000` by default,
and `server_url` `/user/myname`,
for a full url of `http://127.0.0.1:8000/user/myname`.
## Python example
The JupyterHub repo includes a complete example in {file}`examples/server-api`
tying all this together.
To summarize the steps:
1. get user info from `/user/:name`
2. the server model includes a `ready` state to tell you if it's ready
3. if it's not ready, you can follow up with `progress_url` to wait for it
4. if it is ready, you can use the `url` field to link directly to the running server
The example demonstrates starting and stopping servers via the JupyterHub API,
including waiting for them to start via the progress API,
as well as waiting for them to stop via polling the user model.
```{literalinclude} ../../../examples/server-api/start-stop-server.py
:language: python
:start-at: def event_stream
:end-before: def main
```

View File

@@ -1,5 +1,17 @@
# Services
With version 0.7, JupyterHub adds support for **Services**.
This section provides the following information about Services:
- [Definition of a Service](#definition-of-a-service)
- [Properties of a Service](#properties-of-a-service)
- [Hub-Managed Services](#hub-managed-services)
- [Launching a Hub-Managed Service](#launching-a-hub-managed-service)
- [Externally-Managed Services](#externally-managed-services)
- [Writing your own Services](#writing-your-own-services)
- [Hub Authentication and Services](#hub-authentication-and-services)
## Definition of a Service
When working with JupyterHub, a **Service** is defined as a process that interacts
@@ -33,16 +45,17 @@ A Service may have the following properties:
- `url: str (default - None)` - The URL where the service is/should be. If a
url is specified for where the Service runs its own web server,
the service will be added to the proxy at `/services/:name`
- `api_token: str (default - None)` - For Externally-Managed Services you need to specify
- `api_token: str (default - None)` - For Externally-Managed Services you need to specify
an API token to perform API requests to the Hub
- `display: bool (default - True)` - When set to true, display a link to the
service's URL under the 'Services' dropdown in user's hub home page.
If a service is also to be managed by the Hub, it has a few extra options:
- `command: (str/Popen list)` - Command for JupyterHub to spawn the service. - Only use this if the service should be a subprocess. - If command is not specified, the Service is assumed to be managed
externally. - If a command is specified for launching the Service, the Service will
be started and managed by the Hub.
- `command: (str/Popen list`) - Command for JupyterHub to spawn the service.
- Only use this if the service should be a subprocess.
- If command is not specified, the Service is assumed to be managed
externally.
- If a command is specified for launching the Service, the Service will
be started and managed by the Hub.
- `environment: dict` - additional environment variables for the Service.
- `user: str` - the name of a system user to manage the Service. If
unspecified, run as the same user as the Hub.
@@ -76,21 +89,11 @@ Hub-Managed Service would include:
This example would be configured as follows in `jupyterhub_config.py`:
```python
c.JupyterHub.load_roles = [
{
"name": "idle-culler",
"scopes": [
"read:users:activity", # read user last_activity
"servers", # start and stop servers
# 'admin:users' # needed if culling idle users as well
]
}
]
c.JupyterHub.services = [
{
'name': 'idle-culler',
'command': [sys.executable, '-m', 'jupyterhub_idle_culler', '--timeout=3600']
'name': 'cull-idle',
'admin': True,
'command': [sys.executable, '/path/to/cull-idle.py', '--timeout']
}
]
```
@@ -100,14 +103,12 @@ parameters, which describe the environment needed to start the Service process:
- `environment: dict` - additional environment variables for the Service.
- `user: str` - name of the user to run the server if different from the Hub.
Requires Hub to be root.
Requires Hub to be root.
- `cwd: path` directory in which to run the Service, if different from the
Hub directory.
Hub directory.
The Hub will pass the following environment variables to launch the Service:
(service-env)=
```bash
JUPYTERHUB_SERVICE_NAME: The name of the service
JUPYTERHUB_API_TOKEN: API token assigned to the service
@@ -116,24 +117,21 @@ JUPYTERHUB_BASE_URL: Base URL of the Hub (https://mydomain[:port]/)
JUPYTERHUB_SERVICE_PREFIX: URL path prefix of this service (/services/:service-name/)
JUPYTERHUB_SERVICE_URL: Local URL where the service is expected to be listening.
Only for proxied web services.
JUPYTERHUB_OAUTH_SCOPES: JSON-serialized list of scopes to use for allowing access to the service
(deprecated in 3.0, use JUPYTERHUB_OAUTH_ACCESS_SCOPES).
JUPYTERHUB_OAUTH_ACCESS_SCOPES: JSON-serialized list of scopes to use for allowing access to the service (new in 3.0).
JUPYTERHUB_OAUTH_CLIENT_ALLOWED_SCOPES: JSON-serialized list of scopes that can be requested by the oauth client on behalf of users (new in 3.0).
```
For the previous 'cull idle' Service example, these environment variables
would be passed to the Service when the Hub starts the 'cull idle' Service:
```bash
JUPYTERHUB_SERVICE_NAME: 'idle-culler'
JUPYTERHUB_SERVICE_NAME: 'cull-idle'
JUPYTERHUB_API_TOKEN: API token assigned to the service
JUPYTERHUB_API_URL: http://127.0.0.1:8080/hub/api
JUPYTERHUB_BASE_URL: https://mydomain[:port]
JUPYTERHUB_SERVICE_PREFIX: /services/idle-culler/
JUPYTERHUB_SERVICE_PREFIX: /services/cull-idle/
```
See the GitHub repo for additional information about the [jupyterhub_idle_culler][].
See the JupyterHub GitHub repo for additional information about the
[`cull-idle` example](https://github.com/jupyterhub/jupyterhub/tree/master/examples/cull-idle).
## Externally-Managed Services
@@ -192,45 +190,27 @@ extra slash you might get unexpected behavior. For example if your service has a
## Hub Authentication and Services
JupyterHub provides some utilities for using the Hub's authentication
mechanism to govern access to your service.
JupyterHub 0.7 introduces some utilities for using the Hub's authentication
mechanism to govern access to your service. When a user logs into JupyterHub,
the Hub sets a **cookie (`jupyterhub-services`)**. The service can use this
cookie to authenticate requests.
Requests to all JupyterHub services are made with OAuth tokens.
These can either be requests with a token in the `Authorization` header,
or url parameter `?token=...`,
or browser requests which must complete the OAuth authorization code flow,
which results in a token that should be persisted for future requests
(persistence is up to the service,
but an encrypted cookie confined to the service path is appropriate,
and provided by default).
:::{versionchanged} 2.0
The shared `jupyterhub-services` cookie is removed.
OAuth must be used to authenticate browser requests with services.
:::
JupyterHub includes a reference implementation of Hub authentication that
JupyterHub ships with a reference implementation of Hub authentication that
can be used by services. You may go beyond this reference implementation and
create custom hub-authenticating clients and services. We describe the process
below.
The reference, or base, implementation is the {class}`.HubAuth` class,
which implements the API requests to the Hub that resolve a token to a User model.
There are two levels of authentication with the Hub:
- {class}`.HubAuth` - the most basic authentication,
for services that should only accept API requests authorized with a token.
- {class}`.HubOAuth` - For services that should use oauth to authenticate with the Hub.
This should be used for any service that serves pages that should be visited with a browser.
The reference, or base, implementation is the [`HubAuth`][HubAuth] class,
which implements the requests to the Hub.
To use HubAuth, you must set the `.api_token`, either programmatically when constructing the class,
or via the `JUPYTERHUB_API_TOKEN` environment variable.
Most of the logic for authentication implementation is found in the
{meth}`.HubAuth.user_for_token` methods,
which makes a request of the Hub, and returns:
Most of the logic for authentication implementation is found in the
[`HubAuth.user_for_cookie`][HubAuth.user_for_cookie]
and in the
[`HubAuth.user_for_token`][HubAuth.user_for_token]
methods, which makes a request of the Hub, and returns:
- None, if no user could be identified, or
- a dict of the following form:
@@ -239,9 +219,7 @@ which makes a request of the Hub, and returns:
{
"name": "username",
"groups": ["list", "of", "groups"],
"scopes": [
"access:users:servers!server=username/",
],
"admin": False, # or True
}
```
@@ -251,45 +229,79 @@ action.
HubAuth also caches the Hub's response for a number of seconds,
configurable by the `cookie_cache_max_age` setting (default: five minutes).
If your service would like to make further requests _on behalf of users_,
it should use the token issued by this OAuth process.
If you are using tornado,
you can access the token authenticating the current request with {meth}`.HubAuth.get_token`.
:::{versionchanged} 2.2
{meth}`.HubAuth.get_token` adds support for retrieving
tokens stored in tornado cookies after completion of OAuth.
Previously, it only retrieved tokens from URL parameters or the Authorization header.
Passing `get_token(handler, in_cookie=False)` preserves this behavior.
:::
### Flask Example
For example, you have a Flask service that returns information about a user.
JupyterHub's HubAuth class can be used to authenticate requests to the Flask
service. See the `service-whoami-flask` example in the
[JupyterHub GitHub repo](https://github.com/jupyterhub/jupyterhub/tree/HEAD/examples/service-whoami-flask)
[JupyterHub GitHub repo](https://github.com/jupyterhub/jupyterhub/tree/master/examples/service-whoami-flask)
for more details.
```{literalinclude} ../../../examples/service-whoami-flask/whoami-flask.py
:language: python
```python
from functools import wraps
import json
import os
from urllib.parse import quote
from flask import Flask, redirect, request, Response
from jupyterhub.services.auth import HubAuth
prefix = os.environ.get('JUPYTERHUB_SERVICE_PREFIX', '/')
auth = HubAuth(
api_token=os.environ['JUPYTERHUB_API_TOKEN'],
cache_max_age=60,
)
app = Flask(__name__)
def authenticated(f):
"""Decorator for authenticating with the Hub"""
@wraps(f)
def decorated(*args, **kwargs):
cookie = request.cookies.get(auth.cookie_name)
token = request.headers.get(auth.auth_header_name)
if cookie:
user = auth.user_for_cookie(cookie)
elif token:
user = auth.user_for_token(token)
else:
user = None
if user:
return f(user, *args, **kwargs)
else:
# redirect to login url on failed auth
return redirect(auth.login_url + '?next=%s' % quote(request.path))
return decorated
@app.route(prefix)
@authenticated
def whoami(user):
return Response(
json.dumps(user, indent=1, sort_keys=True),
mimetype='application/json',
)
```
### Authenticating tornado services with JupyterHub
Since most Jupyter services are written with tornado,
we include a mixin class, [`HubOAuthenticated`][huboauthenticated],
we include a mixin class, [`HubAuthenticated`][HubAuthenticated],
for quickly authenticating your own tornado services with JupyterHub.
Tornado's {py:func}`~.tornado.web.authenticated` decorator calls a Handler's {py:meth}`~.tornado.web.RequestHandler.get_current_user`
method to identify the user. Mixing in {class}`.HubAuthenticated` defines
{meth}`~.HubAuthenticated.get_current_user` to use HubAuth. If you want to configure the HubAuth
instance beyond the default, you'll want to define an {py:meth}`~.tornado.web.RequestHandler.initialize` method,
Tornado's `@web.authenticated` method calls a Handler's `.get_current_user`
method to identify the user. Mixing in `HubAuthenticated` defines
`get_current_user` to use HubAuth. If you want to configure the HubAuth
instance beyond the default, you'll want to define an `initialize` method,
such as:
```python
class MyHandler(HubOAuthenticated, web.RequestHandler):
class MyHandler(HubAuthenticated, web.RequestHandler):
hub_users = {'inara', 'mal'}
def initialize(self, hub_auth):
self.hub_auth = hub_auth
@@ -299,97 +311,68 @@ class MyHandler(HubOAuthenticated, web.RequestHandler):
...
```
The HubAuth class will automatically load the desired configuration from the Service
[environment variables](service-env).
:::{versionchanged} 2.0
The HubAuth will automatically load the desired configuration from the Service
environment variables.
Access scopes are used to govern access to services.
Prior to 2.0,
sets of users and groups could be used to grant access
by defining `.hub_groups` or `.hub_users` on the authenticated handler.
These are ignored if the 2.0 `.hub_scopes` is defined.
:::
If you want to limit user access, you can specify allowed users through either the
`.hub_users` attribute or `.hub_groups`. These are sets that check against the
username and user group list, respectively. If a user matches neither the user
list nor the group list, they will not be allowed access. If both are left
undefined, then any user will be allowed.
:::{seealso}
{meth}`.HubAuth.check_scopes`
:::
### Implementing your own Authentication with JupyterHub
If you don't want to use the reference implementation
(e.g. you find the implementation a poor fit for your Flask app),
you can implement authentication via the Hub yourself.
JupyterHub is a standard OAuth2 provider,
so you can use any OAuth 2 client implementation appropriate for your toolkit.
See the [FastAPI example][] for an example of using JupyterHub as an OAuth provider with [FastAPI][],
without using any code imported from JupyterHub.
On completion of OAuth, you will have an access token for JupyterHub,
which can be used to identify the user and the permissions (scopes)
the user has authorized for your service.
You will only get to this stage if the user has the required `access:services!service=$service-name` scope.
To retrieve the user model for the token, make a request to `GET /hub/api/user` with the token in the Authorization header.
For example, using flask:
```{literalinclude} ../../../examples/service-whoami-flask/whoami-flask.py
:language: python
```
We recommend looking at the [`HubOAuth`][huboauth] class implementation for reference,
We recommend looking at the [`HubAuth`][HubAuth] class implementation for reference,
and taking note of the following process:
1. retrieve the token from the request.
2. Make an API request `GET /hub/api/user`,
with the token in the `Authorization` header.
1. retrieve the cookie `jupyterhub-services` from the request.
2. Make an API request `GET /hub/api/authorizations/cookie/jupyterhub-services/cookie-value`,
where cookie-value is the url-encoded value of the `jupyterhub-services` cookie.
This request must be authenticated with a Hub API token in the `Authorization` header,
for example using the `api_token` from your [external service's configuration](#externally-managed-services).
For example, with [requests][]:
For example, with [requests][]:
```python
r = requests.get(
"http://127.0.0.1:8081/hub/api/user",
headers = {
'Authorization' : f'token {api_token}',
},
)
r.raise_for_status()
user = r.json()
```
```python
r = requests.get(
'/'.join((["http://127.0.0.1:8081/hub/api",
"authorizations/cookie/jupyterhub-services",
quote(encrypted_cookie, safe=''),
]),
headers = {
'Authorization' : 'token %s' % api_token,
},
)
r.raise_for_status()
user = r.json()
```
3. On success, the reply will be a JSON model describing the user:
```python
```json
{
"name": "inara",
# groups may be omitted, depending on permissions
"groups": ["serenity", "guild"],
# scopes is new in JupyterHub 2.0
"scopes": [
"access:services",
"read:users:name",
"read:users!user=inara",
"..."
]
}
```
The `scopes` field can be used to manage access.
Note: a user will have access to a service to complete oauth access to the service for the first time.
Individual permissions may be revoked at any later point without revoking the token,
in which case the `scopes` field in this model should be checked on each access.
The default required scopes for access are available from `hub_auth.oauth_scopes` or `$JUPYTERHUB_OAUTH_ACCESS_SCOPES`.
An example of using an Externally-Managed Service and authentication is
in [nbviewer README][nbviewer example] section on securing the notebook viewer,
and an example of its configuration is found [here](https://github.com/jupyter/nbviewer/blob/ed942b10a52b6259099e2dd687930871dc8aac22/nbviewer/providers/base.py#L95).
nbviewer can also be run as a Hub-Managed Service as described [nbviewer README][nbviewer example]
section on securing the notebook viewer.
[requests]: http://docs.python-requests.org/en/master/
[services_auth]: ../api/services.auth.html
[HubAuth]: ../api/services.auth.html#jupyterhub.services.auth.HubAuth
[HubAuth.user_for_cookie]: ../api/services.auth.html#jupyterhub.services.auth.HubAuth.user_for_cookie
[HubAuth.user_for_token]: ../api/services.auth.html#jupyterhub.services.auth.HubAuth.user_for_token
[HubAuthenticated]: ../api/services.auth.html#jupyterhub.services.auth.HubAuthenticated
[nbviewer example]: https://github.com/jupyter/nbviewer#securing-the-notebook-viewer
[fastapi example]: https://github.com/jupyterhub/jupyterhub/tree/HEAD/examples/service-fastapi
[fastapi]: https://fastapi.tiangolo.com
[jupyterhub_idle_culler]: https://github.com/jupyterhub/jupyterhub-idle-culler

View File

@@ -8,17 +8,18 @@ and a custom Spawner needs to be able to take three actions:
- poll whether the process is still running
- stop the process
## Examples
Custom Spawners for JupyterHub can be found on the [JupyterHub wiki](https://github.com/jupyterhub/jupyterhub/wiki/Spawners).
Some examples include:
- [DockerSpawner](https://github.com/jupyterhub/dockerspawner) for spawning user servers in Docker containers
- `dockerspawner.DockerSpawner` for spawning identical Docker containers for
* `dockerspawner.DockerSpawner` for spawning identical Docker containers for
each users
- `dockerspawner.SystemUserSpawner` for spawning Docker containers with an
* `dockerspawner.SystemUserSpawner` for spawning Docker containers with an
environment and home directory for each users
- both `DockerSpawner` and `SystemUserSpawner` also work with Docker Swarm for
* both `DockerSpawner` and `SystemUserSpawner` also work with Docker Swarm for
launching containers on remote machines
- [SudoSpawner](https://github.com/jupyterhub/sudospawner) enables JupyterHub to
run without being root, by spawning an intermediate process via `sudo`
@@ -29,6 +30,7 @@ Some examples include:
- [SSHSpawner](https://github.com/NERSC/sshspawner) to spawn notebooks
on a remote server using SSH
## Spawner control methods
### Spawner.start
@@ -37,13 +39,14 @@ Some examples include:
Information about the user can be retrieved from `self.user`,
an object encapsulating the user's name, authentication, and server info.
The return value of `Spawner.start` should be the `(ip, port)` of the running server,
or a full URL as a string.
The return value of `Spawner.start` should be the (ip, port) of the running server.
**NOTE:** When writing coroutines, *never* `yield` in between a database change and a commit.
Most `Spawner.start` functions will look similar to this example:
```python
async def start(self):
def start(self):
self.ip = '127.0.0.1'
self.port = random_port()
# get environment variables,
@@ -55,10 +58,8 @@ async def start(self):
cmd.extend(self.cmd)
cmd.extend(self.get_args())
await self._actually_start_server_somehow(cmd, env)
# url may not match self.ip:self.port, but it could!
url = self._get_connectable_url()
return url
yield self._actually_start_server_somehow(cmd, env)
return (self.ip, self.port)
```
When `Spawner.start` returns, the single-user server process should actually be running,
@@ -66,58 +67,6 @@ not just requested. JupyterHub can handle `Spawner.start` being very slow
(such as PBS-style batch queues, or instantiating whole AWS instances)
via relaxing the `Spawner.start_timeout` config value.
#### Note on IPs and ports
`Spawner.ip` and `Spawner.port` attributes set the _bind_ url,
which the single-user server should listen on
(passed to the single-user process via the `JUPYTERHUB_SERVICE_URL` environment variable).
The _return_ value is the ip and port (or full url) the Hub should _connect to_.
These are not necessarily the same, and usually won't be in any Spawner that works with remote resources or containers.
The default for Spawner.ip, and Spawner.port is `127.0.0.1:{random}`,
which is appropriate for Spawners that launch local processes,
where everything is on localhost and each server needs its own port.
For remote or container Spawners, it will often make sense to use a different value,
such as `ip = '0.0.0.0'` and a fixed port, e.g. `8888`.
The defaults can be changed in the class,
preserving configuration with traitlets:
```python
from traitlets import default
from jupyterhub.spawner import Spawner
class MySpawner(Spawner):
@default("ip")
def _default_ip(self):
return '0.0.0.0'
@default("port")
def _default_port(self):
return 8888
async def start(self):
env = self.get_env()
cmd = []
# get jupyterhub command to run,
# typically ['jupyterhub-singleuser']
cmd.extend(self.cmd)
cmd.extend(self.get_args())
remote_server_info = await self._actually_start_server_somehow(cmd, env)
url = self.get_public_url_from(remote_server_info)
return url
```
#### Exception handling
When `Spawner.start` raises an Exception, a message can be passed on to the user via the exception via a `.jupyterhub_html_message` or `.jupyterhub_message` attribute.
When the Exception has a `.jupyterhub_html_message` attribute, it will be rendered as HTML to the user.
Alternatively `.jupyterhub_message` is rendered as unformatted text.
If both attributes are not present, the Exception will be shown to the user as unformatted text.
### Spawner.poll
`Spawner.poll` should check if the spawner is still running.
@@ -131,6 +80,7 @@ to check if the local process is still running. On Windows, it uses `psutil.pid_
`Spawner.stop` should stop the process. It must be a tornado coroutine, which should return when the process has finished exiting.
## Spawner state
JupyterHub should be able to stop and restart without tearing down
@@ -162,6 +112,7 @@ def clear_state(self):
self.pid = 0
```
## Spawner options form
(new in 0.4)
@@ -178,7 +129,7 @@ If the `Spawner.options_form` is defined, when a user tries to start their serve
If `Spawner.options_form` is undefined, the user's server is spawned directly, and no spawn page is rendered.
See [this example](https://github.com/jupyterhub/jupyterhub/blob/HEAD/examples/spawn-form/jupyterhub_config.py) for a form that allows custom CLI args for the local spawner.
See [this example](https://github.com/jupyterhub/jupyterhub/blob/master/examples/spawn-form/jupyterhub_config.py) for a form that allows custom CLI args for the local spawner.
### `Spawner.options_from_form`
@@ -219,7 +170,8 @@ which would return:
When `Spawner.start` is called, this dictionary is accessible as `self.user_options`.
[spawner]: https://github.com/jupyterhub/jupyterhub/blob/HEAD/jupyterhub/spawner.py
[Spawner]: https://github.com/jupyterhub/jupyterhub/blob/master/jupyterhub/spawner.py
## Writing a custom spawner
@@ -260,75 +212,6 @@ Additionally, configurable attributes for your spawner will
appear in jupyterhub help output and auto-generated configuration files
via `jupyterhub --generate-config`.
## Environment variables and command-line arguments
Spawners mainly do one thing: launch a command in an environment.
The command-line is constructed from user configuration:
- Spawner.cmd (default: `['jupterhub-singleuser']`)
- Spawner.args (cli args to pass to the cmd, default: empty)
where the configuration:
```python
c.Spawner.cmd = ["my-singleuser-wrapper"]
c.Spawner.args = ["--debug", "--flag"]
```
would result in spawning the command:
```bash
my-singleuser-wrapper --debug --flag
```
The `Spawner.get_args()` method is how Spawner.args is accessed,
and can be used by Spawners to customize/extend user-provided arguments.
Prior to 2.0, JupyterHub unconditionally added certain options _if specified_ to the command-line,
such as `--ip={Spawner.ip}` and `--port={Spawner.port}`.
These have now all been moved to environment variables,
and from JupyterHub 2.0,
the command-line launched by JupyterHub is fully specified by overridable configuration `Spawner.cmd + Spawner.args`.
Most process configuration is passed via environment variables.
Additional variables can be specified via the `Spawner.environment` configuration.
The process environment is returned by `Spawner.get_env`, which specifies the following environment variables:
- JUPYTERHUB*SERVICE_URL - the \_bind* url where the server should launch its http server (`http://127.0.0.1:12345`).
This includes Spawner.ip and Spawner.port; _new in 2.0, prior to 2.0 ip,port were on the command-line and only if specified_
- JUPYTERHUB_SERVICE_PREFIX - the URL prefix the service will run on (e.g. `/user/name/`)
- JUPYTERHUB_USER - the JupyterHub user's username
- JUPYTERHUB_SERVER_NAME - the server's name, if using named servers (default server has an empty name)
- JUPYTERHUB_API_URL - the full url for the JupyterHub API (http://17.0.0.1:8001/hub/api)
- JUPYTERHUB_BASE_URL - the base url of the whole jupyterhub deployment, i.e. the bit before `hub/` or `user/`,
as set by c.JupyterHub.base_url (default: `/`)
- JUPYTERHUB_API_TOKEN - the API token the server can use to make requests to the Hub.
This is also the OAuth client secret.
- JUPYTERHUB_CLIENT_ID - the OAuth client ID for authenticating visitors.
- JUPYTERHUB_OAUTH_CALLBACK_URL - the callback URL to use in oauth, typically `/user/:name/oauth_callback`
- JUPYTERHUB_OAUTH_ACCESS_SCOPES - the scopes required to access the server (called JUPYTERHUB_OAUTH_SCOPES prior to 3.0)
- JUPYTERHUB_OAUTH_CLIENT_ALLOWED_SCOPES - the scopes the service is allowed to request.
If no scopes are requested explicitly, these scopes will be requested.
Optional environment variables, depending on configuration:
- JUPYTERHUB*SSL*[KEYFILE|CERTFILE|CLIENT_CI] - SSL configuration, when internal_ssl is enabled
- JUPYTERHUB_ROOT_DIR - the root directory of the server (notebook directory), when Spawner.notebook_dir is defined (new in 2.0)
- JUPYTERHUB_DEFAULT_URL - the default URL for the server (for redirects from /user/:name/),
if Spawner.default_url is defined
(new in 2.0, previously passed via cli)
- JUPYTERHUB_DEBUG=1 - generic debug flag, sets maximum log level when Spawner.debug is True
(new in 2.0, previously passed via cli)
- JUPYTERHUB_DISABLE_USER_CONFIG=1 - disable loading user config,
sets maximum log level when Spawner.debug is True (new in 2.0,
previously passed via cli)
- JUPYTERHUB*[MEM|CPU]*[LIMIT_GUARANTEE] - the values of cpu and memory limits and guarantees.
These are not expected to be enforced by the process,
but are made available as a hint,
e.g. for resource monitoring extensions.
## Spawners, resource limits, and guarantees (Optional)
@@ -341,9 +224,10 @@ support for them**. For example, LocalProcessSpawner, the default
spawner, does not support limits and guarantees. One of the spawners
that supports limits and guarantees is the `systemdspawner`.
### Memory Limits & Guarantees
`c.Spawner.mem_limit`: A **limit** specifies the _maximum amount of memory_
`c.Spawner.mem_limit`: A **limit** specifies the *maximum amount of memory*
that may be allocated, though there is no promise that the maximum amount will
be available. In supported spawners, you can set `c.Spawner.mem_limit` to
limit the total amount of memory that a single-user notebook server can
@@ -351,8 +235,8 @@ allocate. Attempting to use more memory than this limit will cause errors. The
single-user notebook server can discover its own memory limit by looking at
the environment variable `MEM_LIMIT`, which is specified in absolute bytes.
`c.Spawner.mem_guarantee`: Sometimes, a **guarantee** of a _minimum amount of
memory_ is desirable. In this case, you can set `c.Spawner.mem_guarantee` to
`c.Spawner.mem_guarantee`: Sometimes, a **guarantee** of a *minimum amount of
memory* is desirable. In this case, you can set `c.Spawner.mem_guarantee` to
to provide a guarantee that at minimum this much memory will always be
available for the single-user notebook server to use. The environment variable
`MEM_GUARANTEE` will also be set in the single-user notebook server.
@@ -387,7 +271,7 @@ utilize these certs, there are two methods of interest on the base `Spawner`
class: `.create_certs` and `.move_certs`.
The first method, `.create_certs` will sign a key-cert pair using an internally
trusted authority for notebooks. During this process, `.create_certs` can
trusted authority for notebooks. During this process, `.create_certs` can
apply `ip` and `dns` name information to the cert via an `alt_names` `kwarg`.
This is used for certificate authentication (verification). Without proper
verification, the `Notebook` will be unable to communicate with the `Hub` and

View File

@@ -1,8 +1,8 @@
# Working with templates and UI
The pages of the JupyterHub application are generated from
[Jinja](http://jinja.pocoo.org/) templates. These allow the header, for
example, to be defined once and incorporated into all pages. By providing
[Jinja](http://jinja.pocoo.org/) templates. These allow the header, for
example, to be defined once and incorporated into all pages. By providing
your own templates, you can have complete control over JupyterHub's
appearance.
@@ -10,7 +10,7 @@ appearance.
JupyterHub will look for custom templates in all of the paths in the
`JupyterHub.template_paths` configuration option, falling back on the
[default templates](https://github.com/jupyterhub/jupyterhub/tree/HEAD/share/jupyterhub/templates)
[default templates](https://github.com/jupyterhub/jupyterhub/tree/master/share/jupyterhub/templates)
if no custom template with that name is found. This fallback
behavior is new in version 0.9; previous versions searched only those paths
explicitly included in `template_paths`. You may override as many
@@ -20,8 +20,8 @@ or as few templates as you desire.
Jinja provides a mechanism to [extend templates](http://jinja.pocoo.org/docs/2.10/templates/#template-inheritance).
A base template can define a `block`, and child templates can replace or
supplement the material in the block. The
[JupyterHub templates](https://github.com/jupyterhub/jupyterhub/tree/HEAD/share/jupyterhub/templates)
supplement the material in the block. The
[JupyterHub templates](https://github.com/jupyterhub/jupyterhub/tree/master/share/jupyterhub/templates)
make extensive use of blocks, which allows you to customize parts of the
interface easily.
@@ -32,8 +32,8 @@ In general, a child template can extend a base template, `page.html`, by beginni
```
This works, unless you are trying to extend the default template for the same
file name. Starting in version 0.9, you may refer to the base file with a
`templates/` prefix. Thus, if you are writing a custom `page.html`, start the
file name. Starting in version 0.9, you may refer to the base file with a
`templates/` prefix. Thus, if you are writing a custom `page.html`, start the
file with this block:
```html
@@ -41,7 +41,7 @@ file with this block:
```
By defining `block`s with same name as in the base template, child templates
can replace those sections with custom content. The content from the base
can replace those sections with custom content. The content from the base
template can be included with the `{{ super() }}` directive.
### Example
@@ -52,7 +52,10 @@ text about the server starting up, place this content in a file named
`JupyterHub.template_paths` configuration option.
```html
{% extends "templates/spawn_pending.html" %} {% block message %} {{ super() }}
{% extends "templates/spawn_pending.html" %}
{% block message %}
{{ super() }}
<p>Patience is a virtue.</p>
{% endblock %}
```
@@ -66,8 +69,9 @@ To add announcements to be displayed on a page, you have two options:
### Announcement Configuration Variables
If you set the configuration variable `JupyterHub.template_vars = {'announcement': 'some_text'}`, the given `some_text` will be placed on
the top of all pages. The more specific variables
If you set the configuration variable `JupyterHub.template_vars =
{'announcement': 'some_text'}`, the given `some_text` will be placed on
the top of all pages. The more specific variables
`announcement_login`, `announcement_spawn`, `announcement_home`, and
`announcement_logout` are more specific and only show on their
respective pages (overriding the global `announcement` variable).
@@ -75,12 +79,13 @@ Note that changing these variables require a restart, unlike direct
template extension.
You can get the same effect by extending templates, which allows you
to update the messages without restarting. Set
to update the messages without restarting. Set
`c.JupyterHub.template_paths` as mentioned above, and then create a
template (for example, `login.html`) with:
```html
{% extends "templates/login.html" %} {% set announcement = 'some message' %}
{% extends "templates/login.html" %}
{% set announcement = 'some message' %}
```
Extending `page.html` puts the message on all pages, but note that

View File

@@ -11,6 +11,8 @@ All authenticated handlers redirect to `/hub/login` to login users
prior to being redirected back to the originating page.
The returned request should preserve all query parameters.
## `/`
The top-level request is always a simple redirect to `/hub/`,
@@ -59,7 +61,7 @@ for starting and stopping the user's server.
If named servers are enabled, there will be some additional
tools for management of named servers.
_Version added: 1.0_ named server UI is new in 1.0.
*Version added: 1.0* named server UI is new in 1.0.
## `/hub/login`
@@ -109,7 +111,7 @@ not the Hub.
The username is the first part and, if using named servers,
the server name is the second part.
If the user's server is _not_ running, this will be redirected to `/hub/user/:username/...`
If the user's server is *not* running, this will be redirected to `/hub/user/:username/...`
## `/hub/user/:username[/:servername]`
@@ -121,8 +123,8 @@ Handling this URL is the most complicated condition in JupyterHub,
because there can be many states:
1. server is not active
a. user matches
b. user doesn't match
a. user matches
b. user doesn't match
2. server is ready
3. server is pending, but not ready
@@ -144,7 +146,7 @@ without additional user action (i.e. clicking the link on the page)
![Visiting a URL for a server that's not running](../images/not-running.png)
_Version changed: 1.0_
*Version changed: 1.0*
Prior to 1.0, this URL itself was responsible for spawning servers,
and served the progress page if it was pending,
@@ -163,7 +165,7 @@ indicating how to spawn the server.
This is meant to help applications such as JupyterLab
that are connected to a server that has stopped.
_Version changed: 1.0_
*Version changed: 1.0*
JupyterHub 0.9 failed these API requests with status 404,
but 1.0 uses 503.
@@ -205,12 +207,12 @@ and a POST request will trigger the actual spawn and redirect.
![The spawn form](../images/spawn-form.png)
_Version added: 1.0_
*Version added: 1.0*
1.0 adds the ability to specify username and servername.
Prior to 1.0, only `/hub/spawn` was recognized for the default server.
_Version changed: 1.0_
*Version changed: 1.0*
Prior to 1.0, this page redirected back to `/hub/user/:username`,
which was responsible for triggering spawn and rendering progress, etc.
@@ -219,7 +221,7 @@ which was responsible for triggering spawn and rendering progress, etc.
![The spawn pending page](../images/spawn-pending.png)
_Version added: 1.0_ this URL is new in JupyterHub 1.0.
*Version added: 1.0* this URL is new in JupyterHub 1.0.
This page renders the progress view for the given spawn request.
Once the server is ready,

View File

@@ -12,17 +12,17 @@ works.
## Semi-trusted and untrusted users
JupyterHub is designed to be a _simple multi-user server for modestly sized
groups_ of **semi-trusted** users. While the design reflects serving semi-trusted
JupyterHub is designed to be a *simple multi-user server for modestly sized
groups* of **semi-trusted** users. While the design reflects serving semi-trusted
users, JupyterHub is not necessarily unsuitable for serving **untrusted** users.
Using JupyterHub with **untrusted** users does mean more work by the
administrator. Much care is required to secure a Hub, with extra caution on
protecting users from each other as the Hub is serving untrusted users.
One aspect of JupyterHub's _design simplicity_ for **semi-trusted** users is that
the Hub and single-user servers are placed in a _single domain_, behind a
[_proxy_][configurable-http-proxy]. If the Hub is serving untrusted
One aspect of JupyterHub's *design simplicity* for **semi-trusted** users is that
the Hub and single-user servers are placed in a *single domain*, behind a
[*proxy*][configurable-http-proxy]. If the Hub is serving untrusted
users, many of the web's cross-site protections are not applied between
single-user servers and the Hub, or between single-user servers and each
other, since browsers see the whole thing (proxy, Hub, and single user
@@ -40,7 +40,7 @@ server.
To protect all users from each other, JupyterHub administrators must
ensure that:
- A user **does not have permission** to modify their single-user notebook server,
* A user **does not have permission** to modify their single-user notebook server,
including:
- A user **may not** install new packages in the Python environment that runs
their single-user server.
@@ -49,11 +49,11 @@ ensure that:
directory that precedes the directory containing `jupyterhub-singleuser`.
- A user may not modify environment variables (e.g. PATH, PYTHONPATH) for
their single-user server.
- A user **may not** modify the configuration of the notebook server
* A user **may not** modify the configuration of the notebook server
(the `~/.jupyter` or `JUPYTER_CONFIG_DIR` directory).
If any additional services are run on the same domain as the Hub, the services
**must never** display user-authored HTML that is neither _sanitized_ nor _sandboxed_
**must never** display user-authored HTML that is neither *sanitized* nor *sandboxed*
(e.g. IFramed) to any user that lacks authentication as the author of a file.
## Mitigate security issues
@@ -85,7 +85,7 @@ admin must enforce.
### Prevent spawners from evaluating shell configuration files
For most Spawners, `PATH` is not something users can influence, but care should
be taken to ensure that the Spawner does _not_ evaluate shell configuration
be taken to ensure that the Spawner does *not* evaluate shell configuration
files prior to launching the server.
### Isolate packages using virtualenv
@@ -125,12 +125,13 @@ versions up to date.
A handy website for testing your deployment is
[Qualsys' SSL analyzer tool](https://www.ssllabs.com/ssltest/analyze.html).
[configurable-http-proxy]: https://github.com/jupyterhub/configurable-http-proxy
## Vulnerability reporting
If you believe youve found a security vulnerability in JupyterHub, or any
Jupyter project, please report it to
[security@ipython.org](mailto:security@ipython.org). If you prefer to encrypt
[security@ipython.org](mailto:security@iypthon.org). If you prefer to encrypt
your security reports, you can use [this PGP public
key](https://jupyter-notebook.readthedocs.io/en/stable/_downloads/ipython_security.asc).

View File

@@ -4,20 +4,17 @@ When troubleshooting, you may see unexpected behaviors or receive an error
message. This section provide links for identifying the cause of the
problem and how to resolve it.
[_Behavior_](#behavior)
[*Behavior*](#behavior)
- JupyterHub proxy fails to start
- sudospawner fails to run
- What is the default behavior when none of the lists (admin, allowed,
allowed groups) are set?
- JupyterHub Docker container not accessible at localhost
[_Errors_](#errors)
[*Errors*](#errors)
- 500 error after spawning my single-user server
[_How do I...?_](#how-do-i)
[*How do I...?*](#how-do-i)
- Use a chained SSL certificate
- Install JupyterHub without a network connection
- I want access to the whole filesystem, but still default users to their home directory
@@ -28,7 +25,7 @@ problem and how to resolve it.
- Toree integration with HDFS rack awareness script
- Where do I find Docker images and Dockerfiles related to JupyterHub?
[_Troubleshooting commands_](#troubleshooting-commands)
[*Troubleshooting commands*](#troubleshooting-commands)
## Behavior
@@ -37,8 +34,8 @@ problem and how to resolve it.
If you have tried to start the JupyterHub proxy and it fails to start:
- check if the JupyterHub IP configuration setting is
`c.JupyterHub.ip = '*'`; if it is, try `c.JupyterHub.ip = ''`
- Try starting with `jupyterhub --ip=0.0.0.0`
``c.JupyterHub.ip = '*'``; if it is, try ``c.JupyterHub.ip = ''``
- Try starting with ``jupyterhub --ip=0.0.0.0``
**Note**: If this occurs on Ubuntu/Debian, check that the you are using a
recent version of node. Some versions of Ubuntu/Debian come with a version
@@ -69,13 +66,13 @@ things like inspect other users' servers, or modify the user list at runtime).
### JupyterHub Docker container not accessible at localhost
Even though the command to start your Docker container exposes port 8000
(`docker run -p 8000:8000 -d --name jupyterhub jupyterhub/jupyterhub jupyterhub`),
it is possible that the IP address itself is not accessible/visible. As a result
when you try http://localhost:8000 in your browser, you are unable to connect
even though the container is running properly. One workaround is to explicitly
tell Jupyterhub to start at `0.0.0.0` which is visible to everyone. Try this
command:
Even though the command to start your Docker container exposes port 8000
(`docker run -p 8000:8000 -d --name jupyterhub jupyterhub/jupyterhub jupyterhub`),
it is possible that the IP address itself is not accessible/visible. As a result
when you try http://localhost:8000 in your browser, you are unable to connect
even though the container is running properly. One workaround is to explicitly
tell Jupyterhub to start at `0.0.0.0` which is visible to everyone. Try this
command:
`docker run -p 8000:8000 -d --name jupyterhub jupyterhub/jupyterhub jupyterhub --ip 0.0.0.0 --port 8000`
### How can I kill ports from JupyterHub managed services that have been orphaned?
@@ -111,7 +108,7 @@ sudo MY_ENV=abc123 \
### How can I view the logs for JupyterHub or the user's Notebook servers when using the DockerSpawner?
Use `docker logs <container>` where `<container>` is the container name defined within `docker-compose.yml`. For example, to view the logs of the JupyterHub container use:
Use `docker logs <container>` where `<container>` is the container name defined within `docker-compose.yml`. For example, to view the logs of the JupyterHub container use:
docker logs hub
@@ -135,11 +132,11 @@ There are two likely reasons for this:
1. The single-user server cannot connect to the Hub's API (networking
configuration problems)
2. The single-user server cannot _authenticate_ its requests (invalid token)
2. The single-user server cannot *authenticate* its requests (invalid token)
#### Symptoms
The main symptom is a failure to load _any_ page served by the single-user
The main symptom is a failure to load *any* page served by the single-user
server, met with a 500 error. This is typically the first page at `/user/<your_name>`
after logging in or clicking "Start my server". When a single-user notebook server
receives a request, the notebook server makes an API request to the Hub to
@@ -201,15 +198,15 @@ your server again.
##### Proxy settings (403 GET)
When your whole JupyterHub sits behind a organization proxy (_not_ a reverse proxy like NGINX as part of your setup and _not_ the configurable-http-proxy) the environment variables `HTTP_PROXY`, `HTTPS_PROXY`, `http_proxy` and `https_proxy` might be set. This confuses the jupyterhub-singleuser servers: When connecting to the Hub for authorization they connect via the proxy instead of directly connecting to the Hub on localhost. The proxy might deny the request (403 GET). This results in the singleuser server thinking it has a wrong auth token. To circumvent this you should add `<hub_url>,<hub_ip>,localhost,127.0.0.1` to the environment variables `NO_PROXY` and `no_proxy`.
When your whole JupyterHub sits behind a organization proxy (*not* a reverse proxy like NGINX as part of your setup and *not* the configurable-http-proxy) the environment variables `HTTP_PROXY`, `HTTPS_PROXY`, `http_proxy` and `https_proxy` might be set. This confuses the jupyterhub-singleuser servers: When connecting to the Hub for authorization they connect via the proxy instead of directly connecting to the Hub on localhost. The proxy might deny the request (403 GET). This results in the singleuser server thinking it has a wrong auth token. To circumvent this you should add `<hub_url>,<hub_ip>,localhost,127.0.0.1` to the environment variables `NO_PROXY` and `no_proxy`.
### Launching Jupyter Notebooks to run as an externally managed JupyterHub service with the `jupyterhub-singleuser` command returns a `JUPYTERHUB_API_TOKEN` error
[JupyterHub services](https://jupyterhub.readthedocs.io/en/stable/reference/services.html) allow processes to interact with JupyterHub's REST API. Example use-cases include:
- **Secure Testing**: provide a canonical Jupyter Notebook for testing production data to reduce the number of entry points into production systems.
- **Grading Assignments**: provide access to shared Jupyter Notebooks that may be used for management tasks such grading assignments.
- **Private Dashboards**: share dashboards with certain group members.
* **Secure Testing**: provide a canonical Jupyter Notebook for testing production data to reduce the number of entry points into production systems.
* **Grading Assignments**: provide access to shared Jupyter Notebooks that may be used for management tasks such grading assignments.
* **Private Dashboards**: share dashboards with certain group members.
If possible, try to run the Jupyter Notebook as an externally managed service with one of the provided [jupyter/docker-stacks](https://github.com/jupyter/docker-stacks).
@@ -234,7 +231,7 @@ With a docker container, pass in the environment variable with the run command:
-e JUPYTERHUB_API_TOKEN=my_secret_token \
jupyter/datascience-notebook:latest
[This example](https://github.com/jupyterhub/jupyterhub/tree/HEAD/examples/service-notebook/external) demonstrates how to combine the use of the `jupyterhub-singleuser` environment variables when launching a Notebook as an externally managed service.
[This example](https://github.com/jupyterhub/jupyterhub/tree/master/examples/service-notebook/external) demonstrates how to combine the use of the `jupyterhub-singleuser` environment variables when launching a Notebook as an externally managed service.
## How do I...?
@@ -253,6 +250,7 @@ You would then set in your `jupyterhub_config.py` file the `ssl_key` and
c.JupyterHub.ssl_cert = your_host-chained.crt
c.JupyterHub.ssl_key = your_host.key
#### Example
Your certificate provider gives you the following files: `example_host.crt`,
@@ -275,7 +273,7 @@ where `ssl_cert` is example-chained.crt and ssl_key to your private key.
Then restart JupyterHub.
See also {ref}`ssl-encryption`.
See also [JupyterHub SSL encryption](./getting-started/security-basics.html#ssl-encryption).
### Install JupyterHub without a network connection
@@ -371,7 +369,7 @@ a JupyterHub deployment. The commands are:
- System and deployment information
```bash
jupyter troubleshoot
jupyter troubleshooting
```
- Kernel information
@@ -404,8 +402,8 @@ SyntaxError: Missing parentheses in call to 'print'
In order to resolve this issue, there are two potential options.
1. Update HDFS core-site.xml, so the parameter "net.topology.script.file.name" points to a custom
script (e.g. /etc/hadoop/conf/custom_topology_script.py). Copy the original script and change the first line point
to a python two installation (e.g. /usr/bin/python).
script (e.g. /etc/hadoop/conf/custom_topology_script.py). Copy the original script and change the first line point
to a python two installation (e.g. /usr/bin/python).
2. In spark-env.sh add a Python 2 installation to your path (e.g. export PATH=/opt/anaconda2/bin:$PATH).
### Where do I find Docker images and Dockerfiles related to JupyterHub?

View File

@@ -1,46 +0,0 @@
import sys
from pathlib import Path
from subprocess import run
from ruamel.yaml import YAML
yaml = YAML(typ="safe")
here = Path(__file__).absolute().parent
root = here.parent
def test_rest_api_version_is_updated():
"""Checks that the version in JupyterHub's REST API definition file
(rest-api.yml) is matching the JupyterHub version."""
version_py = root.joinpath("jupyterhub", "_version.py")
rest_api_yaml = root.joinpath("docs", "source", "_static", "rest-api.yml")
ns = {}
with version_py.open() as f:
exec(f.read(), {}, ns)
jupyterhub_version = ns["__version__"]
with rest_api_yaml.open() as f:
rest_api = yaml.load(f)
rest_api_version = rest_api["info"]["version"]
assert jupyterhub_version == rest_api_version
def test_rest_api_rbac_scope_descriptions_are_updated():
"""Checks that the RBAC scope descriptions in JupyterHub's REST API
definition file (rest-api.yml) as can be updated by generate-scope-table.py
matches what is committed."""
run([sys.executable, "source/rbac/generate-scope-table.py"], cwd=here, check=True)
run(
[
"git",
"--no-pager",
"diff",
"--color=always",
"--exit-code",
str(here.joinpath("source", "_static", "rest-api.yml")),
],
cwd=here,
check=True,
)

View File

@@ -1,31 +0,0 @@
"""sample jupyterhub config file for testing
configures jupyterhub with dummyauthenticator and simplespawner
to enable testing without administrative privileges.
"""
c = get_config() # noqa
c.Application.log_level = 'DEBUG'
import os
from oauthenticator.azuread import AzureAdOAuthenticator
c.JupyterHub.authenticator_class = AzureAdOAuthenticator
c.AzureAdOAuthenticator.client_id = os.getenv("AAD_CLIENT_ID")
c.AzureAdOAuthenticator.client_secret = os.getenv("AAD_CLIENT_SECRET")
c.AzureAdOAuthenticator.oauth_callback_url = os.getenv("AAD_CALLBACK_URL")
c.AzureAdOAuthenticator.tenant_id = os.getenv("AAD_TENANT_ID")
c.AzureAdOAuthenticator.username_claim = "email"
c.AzureAdOAuthenticator.authorize_url = os.getenv("AAD_AUTHORIZE_URL")
c.AzureAdOAuthenticator.token_url = os.getenv("AAD_TOKEN_URL")
c.Authenticator.manage_groups = True
c.Authenticator.refresh_pre_spawn = True
# Optionally set a global password that all users must use
# c.DummyAuthenticator.password = "your_password"
from jupyterhub.spawner import SimpleLocalProcessSpawner
c.JupyterHub.spawner_class = SimpleLocalProcessSpawner

View File

@@ -1,2 +0,0 @@
oauthenticator
pyjwt

View File

@@ -1,34 +1,34 @@
# Bootstrapping your users
Before spawning a notebook to the user, it could be useful to
Before spawning a notebook to the user, it could be useful to
do some preparation work in a bootstrapping process.
Common use cases are:
_Providing writeable storage for LDAP users_
*Providing writeable storage for LDAP users*
Your Jupyterhub is configured to use the LDAPAuthenticator and DockerSpawer.
- The user has no file directory on the host since your are using LDAP.
- When a user has no directory and DockerSpawner wants to mount a volume,
the spawner will use docker to create a directory.
Since the docker daemon is running as root, the generated directory for the volume
mount will not be writeable by the `jovyan` user inside of the container.
For the directory to be useful to the user, the permissions on the directory
need to be modified for the user to have write access.
* The user has no file directory on the host since your are using LDAP.
* When a user has no directory and DockerSpawner wants to mount a volume,
the spawner will use docker to create a directory.
Since the docker daemon is running as root, the generated directory for the volume
mount will not be writeable by the `jovyan` user inside of the container.
For the directory to be useful to the user, the permissions on the directory
need to be modified for the user to have write access.
_Prepopulating Content_
*Prepopulating Content*
Another use would be to copy initial content, such as tutorial files or reference
material, into the user's space when a notebook server is newly spawned.
material, into the user's space when a notebook server is newly spawned.
You can define your own bootstrap process by implementing a `pre_spawn_hook` on any spawner.
The Spawner itself is passed as parameter to your hook and you can easily get the contextual information out of the spawning process.
The Spawner itself is passed as parameter to your hook and you can easily get the contextual information out of the spawning process.
Similarly, there may be cases where you would like to clean up after a spawner stops.
You may implement a `post_stop_hook` that is always executed after the spawner stops.
If you implement a hook, make sure that it is _idempotent_. It will be executed every time
If you implement a hook, make sure that it is *idempotent*. It will be executed every time
a notebook server is spawned to the user. That means you should somehow
ensure that things which should run only once are not running again and again.
For example, before you create a directory, check if it exists.
@@ -41,13 +41,13 @@ Create a directory for the user, if none exists
```python
# in jupyterhub_config.py
# in jupyterhub_config.py
import os
def create_dir_hook(spawner):
username = spawner.user.name # get the username
volume_path = os.path.join('/volumes/jupyterhub', username)
if not os.path.exists(volume_path):
# create a directory with umask 0755
# create a directory with umask 0755
# hub and container user must have the same UID to be writeable
# still readable by other users on the system
os.mkdir(volume_path, 0o755)
@@ -83,17 +83,17 @@ in a new file in `/etc/sudoers.d`, or simply in `/etc/sudoers`.
All new home directories will be created from `/etc/skel`, so make sure to place any custom homedir-contents in there.
### Example #3 - Run a shell script
### Example #3 - Run a shell script
You can specify a plain ole' shell script (or any other executable) to be run
You can specify a plain ole' shell script (or any other executable) to be run
by the bootstrap process.
For example, you can execute a shell script and as first parameter pass the name
For example, you can execute a shell script and as first parameter pass the name
of the user:
```python
# in jupyterhub_config.py
# in jupyterhub_config.py
from subprocess import check_call
import os
def my_script_hook(spawner):
@@ -106,7 +106,7 @@ c.Spawner.pre_spawn_hook = my_script_hook
```
Here's an example on what you could do in your shell script. See also
Here's an example on what you could do in your shell script. See also
`/examples/bootstrap-script/`
```bash
@@ -126,7 +126,7 @@ fi
# This example script will do the following:
# - create one directory for the user $USER in a BASE_DIRECTORY (see below)
# - create a "tutorials" directory within and download and unzip
# - create a "tutorials" directory within and download and unzip
# the PythonDataScienceHandbook from GitHub
# Start the Bootstrap Process
@@ -148,9 +148,9 @@ else
echo "...initial content loading for user ..."
mkdir $USER_DIRECTORY/tutorials
cd $USER_DIRECTORY/tutorials
wget https://github.com/jakevdp/PythonDataScienceHandbook/archive/HEAD.zip
unzip -o HEAD.zip
rm HEAD.zip
wget https://github.com/jakevdp/PythonDataScienceHandbook/archive/master.zip
unzip -o master.zip
rm master.zip
fi
exit 0

View File

@@ -40,9 +40,9 @@ else
echo "...initial content loading for user ..."
mkdir $USER_DIRECTORY/tutorials
cd $USER_DIRECTORY/tutorials
wget https://github.com/jakevdp/PythonDataScienceHandbook/archive/HEAD.zip
unzip -o HEAD.zip
rm HEAD.zip
wget https://github.com/jakevdp/PythonDataScienceHandbook/archive/master.zip
unzip -o master.zip
rm master.zip
fi
exit 0

View File

@@ -10,7 +10,7 @@ from jupyter_client.localinterfaces import public_ips
def create_dir_hook(spawner):
"""Create directory"""
""" Create directory """
username = spawner.user.name # get the username
volume_path = os.path.join('/volumes/jupyterhub', username)
if not os.path.exists(volume_path):
@@ -20,7 +20,7 @@ def create_dir_hook(spawner):
def clean_dir_hook(spawner):
"""Delete directory"""
""" Delete directory """
username = spawner.user.name # get the username
temp_path = os.path.join('/volumes/jupyterhub', username, 'temp')
if os.path.exists(temp_path) and os.path.isdir(temp_path):

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