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..

39 Commits
3.1.1 ... 1.5.0

Author SHA1 Message Date
Min RK
bf73e6f7b7 fix 1.5.0 link in changelog 2021-11-04 13:56:40 +01:00
Min RK
e2631b302a import commands from setuptools
importing build_py from distutils breaks in setuptools 58
2021-11-04 13:55:41 +01:00
Min RK
0d89241c9f release 1.5.0 2021-11-04 13:22:53 +01:00
Min RK
5ac9e7f73a Merge branch 'fix-set-cookie' into 1.4.x
Prepare to release 1.5.0

Fixes GHSA-cw7p-q79f-m2v7
2021-11-04 13:21:52 +01:00
Min RK
9672b534ec changelog for 1.5.0 2021-11-03 16:16:48 +01:00
Min RK
254365716d jupyterlab: don't use $JUPYTERHUB_API_TOKEN in PageConfig.token 2021-11-03 15:52:00 +01:00
Min RK
dcac8c4efe Revert "store tokens passed via url or header, not only url."
This reverts commit 53c3201c17.

Only tokens in URLs should be persisted in cookies.
Tokens in headers should not have any effect on cookies.
2021-11-03 15:52:00 +01:00
Min RK
0611169dea Merge pull request #3677 from minrk/doc-requirements-1x
1.4.x: update doc requirements
2021-11-02 10:38:16 +01:00
Min RK
a432fa3bb6 Merge pull request #3676 from manics/1-use_legacy_stopped_server_status_code
use_legacy_stopped_server_status_code: use 1.* language
2021-11-02 10:35:09 +01:00
Min RK
44141ae025 1.4.x: update doc requirements
pin down docutils, unpin autodoc-traits
2021-11-02 10:35:04 +01:00
Simon Li
04ae25d2c2 use_legacy_stopped_server_status_code: use 1.* language
Also fixes the JupyterHub 2.0 default: will be False not True
2021-11-01 22:14:59 +00:00
Min RK
69a1e97fbe Merge pull request #3639 from yuvipanda/404-1.4.x
Backport #3636 to 1.4.x
2021-10-06 16:08:00 +02:00
YuviPanda
eb0c6514af Set use_legacy_stopped_server_status_code to True for 1.4.x 2021-10-06 17:25:10 +05:30
YuviPanda
d03fc8c531 Update tests that were looking for 503s 2021-10-05 20:19:59 +05:30
YuviPanda
1c8dce533b Preserve older 503 behavior behind a flag 2021-10-05 20:19:59 +05:30
YuviPanda
bbfbc47bb3 Use 424 rather than 404 to indicate non-running server
404 is also used to identify that a particular resource
(like a kernel or terminal) is not present, maybe because
it is deleted. That comes from the notebook server, while
here we are responding from JupyterHub. Saying that the
user server they are trying to request the resource (kernel, etc)
from does not exist seems right.
2021-10-05 20:19:59 +05:30
YuviPanda
be6ec28dab Fail suspected API requests with 404, not 503
Non-running user servers making requests is a fairly
common occurance - user servers get culled while their
browser tabs are left open. So we now have a background level
of 503s responses on the hub *all* the time, making it
very difficult to detect *real* 503s, which should ideally
be closely monitored and alerted on.

I *think* 404 is a more appropriate response, as the resource
(API) being requested is no longer present.
2021-10-05 20:19:59 +05:30
Min RK
bd3a215c9e Merge pull request #3580 from meeseeksmachine/auto-backport-of-pr-3552-on-1.4.x
Backport PR #3552 on branch 1.4.x (Add expiration date dropdown to Token page)
2021-08-23 12:08:56 +02:00
Min RK
3783a1bc6c Backport PR #3552: Add expiration date dropdown to Token page 2021-08-23 09:42:15 +00:00
Min RK
7b0f29b340 Merge pull request #3579 from meeseeksmachine/auto-backport-of-pr-3488-on-1.4.x
Backport PR #3488 on branch 1.4.x (Support auto login when used as a OAuth2 provider)
2021-08-23 10:32:15 +02:00
Min RK
f63e810dfe Backport PR #3488: Support auto login when used as a OAuth2 provider 2021-08-20 08:30:04 +00:00
Min RK
909b3ad4d7 Merge pull request #3538 from consideRatio/pr/release-1.4.2
Release 1.4.2
2021-07-16 10:57:54 +00:00
Erik Sundell
114493be9b release 1.4.2 2021-07-15 16:57:54 +02:00
Erik Sundell
4c0ac5ba91 changelog for 1.4.2 2021-07-15 16:57:52 +02:00
Erik Sundell
52793d65bd Backport PR #3531: Fix regression where external services api_token became required
Issue background

Registering an external service means it won't be run as a process by JupyterHub or similar as I understand it, and such external services may be registered only to get a /services/<service_name> route registered with JupyterHub's configured proxy rather than to actually use an api_token and speak with JupyterHub.

In the past, it was okay for a external service without an api_token to be registered, but not it isn't. This PR fixes that.

The situation when I run into this is when I register grafana as an external service like this (but in reality via a z2jh config with slightly different syntax).

```python
c.JupyterHub.services = [
    {
        "name": "grafana",
        "url": "http://grafana",
    }
]
```

JupyterHub has a [documentation about Services](https://jupyterhub.readthedocs.io/en/stable/reference/services.html properties-of-a-service), where one can see that the default value of api_token is None.

    Issue details

This is an error me and  GeorgianaElena have run into using JupyterHub 1.4.1, but I'm not sure at what point the regression was introduced besides it was around in 1.4.1.

I wrote some notes tracking this issue down. This is the summary I wrote.

```
    This test was made to reproduce an error like this:

        ValueError: Tokens must be at least 8 characters, got ''

    The error had the following stack trace in 1.4.1:

        jupyterhub/app.py:2213: in init_api_tokens
            await self._add_tokens(self.service_tokens, kind='service')
        jupyterhub/app.py:2182: in _add_tokens
            obj.new_api_token(
        jupyterhub/orm.py:424: in new_api_token
            return APIToken.new(token=token, service=self, **kwargs)
        jupyterhub/orm.py:699: in new
            cls.check_token(db, token)

    This test also make _add_tokens receive a token_dict that is buggy:

        {"": "external_2"}

    It turned out that whatever passes token_dict to _add_tokens failed to
    ignore service's api_tokens that were None, and instead passes them as blank
    strings.

    It turned out that init_api_tokens was passing self.service_tokens, and that
    self.service_tokens had been populated with blank string tokens for external
    services registered with JupyterHub.
```

Signed-off-by: Erik Sundell <erik.i.sundell@gmail.com>
2021-07-15 10:16:18 +02:00
passer
320e1924a7 Backport PR #3521: Fix contributor documentation's link
Clicking the contributor documentation's link [https://jupyter.readthedocs.io/en/latest/contributor/content-contributor.html](https://jupyter.readthedocs.io/en/latest/contributor/content-contributor.html) will get an error

This link needs to be replaced with [https://jupyter.readthedocs.io/en/latest/contributing/content-contributor.html](https://jupyter.readthedocs.io/en/latest/contributing/content-contributor.html)

Signed-off-by: Erik Sundell <erik.i.sundell@gmail.com>
2021-07-15 10:16:16 +02:00
Min RK
2c90715c8d Backport PR #3510: bump autodoc-traits
for sphinx compatibility fix, to get docs building again

Signed-off-by: Erik Sundell <erik.i.sundell@gmail.com>
2021-07-15 10:16:13 +02:00
David Brochart
c99bb32e12 Backport PR #3494: Fix typo
Signed-off-by: Erik Sundell <erik.i.sundell@gmail.com>
2021-07-15 10:16:11 +02:00
Igor Beliakov
fee4ee23c0 Backport PR #3484: Bug: save_bearer_token (provider.py) passes a float value to the expires_at field (int)
**Environment**

* image: k8s-hub (`jupyterhub/k8s-hub:0.11.1`);
* `authenticator_class: dummy`;
* db: cocroachdb (`sqlalchemy-cocroachdb`).

**Description:**

`save_bearer_token` method (`provider.py`) passes a float value to the `expires_at` field (int).

A user can create a notebook, it gets successfully scheduled, and then, once the pod is up and ready, the user is unable to enter the notebook, because jupyterhub cannot save a token. In logs, we can see the following:

```
[I 2021-05-29 14:45:04.302 JupyterHub log:181] 302 GET /hub/api/oauth2/authorize?client_id=jupyterhub-user-user2&redirect_uri=%2Fuser%2Fuser2%2Foauth_callback&response_type=code&state=[secret] -> /user/user2/oauth_callback?code=[secret]&state=[secret] (user2 40.113.125.116) 73.98ms
[E 2021-05-29 14:45:04.424 JupyterHub web:1789] Uncaught exception POST /hub/api/oauth2/token (10.42.80.10)
    HTTPServerRequest(protocol='http', host='hub:8081', method='POST', uri='/hub/api/oauth2/token', version='HTTP/1.1', remote_ip='10.42.80.10')
    Traceback (most recent call last):
      File "/usr/local/lib/python3.8/dist-packages/tornado/web.py", line 1702, in _execute
        result = method(*self.path_args, **self.path_kwargs)
      File "/usr/local/lib/python3.8/dist-packages/jupyterhub/apihandlers/auth.py", line 324, in post
        headers, body, status = self.oauth_provider.create_token_response(
      File "/usr/local/lib/python3.8/dist-packages/oauthlib/oauth2/rfc6749/endpoints/base.py", line 116, in wrapper
        return f(endpoint, uri, *args, **kwargs)
      File "/usr/local/lib/python3.8/dist-packages/oauthlib/oauth2/rfc6749/endpoints/token.py", line 118, in create_token_response
        return grant_type_handler.create_token_response(
      File "/usr/local/lib/python3.8/dist-packages/oauthlib/oauth2/rfc6749/grant_types/authorization_code.py", line 313, in create_token_response
        self.request_validator.save_token(token, request)
      File "/usr/local/lib/python3.8/dist-packages/jupyterhub/oauth/provider.py", line 281, in save_token
        return self.save_bearer_token(token, request, *args, **kwargs)
      File "/usr/local/lib/python3.8/dist-packages/jupyterhub/oauth/provider.py", line 354, in save_bearer_token
        self.db.commit()
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/orm/session.py", line 1042, in commit
        self.transaction.commit()
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/orm/session.py", line 504, in commit
        self._prepare_impl()
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/orm/session.py", line 483, in _prepare_impl
        self.session.flush()
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/orm/session.py", line 2536, in flush
        self._flush(objects)
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/orm/session.py", line 2678, in _flush
        transaction.rollback(_capture_exception=True)
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/util/langhelpers.py", line 68, in __exit__
        compat.raise_(
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/util/compat.py", line 182, in raise_
        raise exception
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/orm/session.py", line 2638, in _flush
        flush_context.execute()
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/orm/unitofwork.py", line 422, in execute
        rec.execute(self)
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/orm/unitofwork.py", line 586, in execute
        persistence.save_obj(
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/orm/persistence.py", line 239, in save_obj
        _emit_insert_statements(
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/orm/persistence.py", line 1135, in _emit_insert_statements
        result = cached_connections[connection].execute(
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/engine/base.py", line 1011, in execute
        return meth(self, multiparams, params)
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/sql/elements.py", line 298, in _execute_on_connection
        return connection._execute_clauseelement(self, multiparams, params)
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/engine/base.py", line 1124, in _execute_clauseelement
        ret = self._execute_context(
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/engine/base.py", line 1316, in _execute_context
        self._handle_dbapi_exception(
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/engine/base.py", line 1510, in _handle_dbapi_exception
        util.raise_(
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/util/compat.py", line 182, in raise_
        raise exception
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/engine/base.py", line 1276, in _execute_context
        self.dialect.do_execute(
      File "/usr/local/lib/python3.8/dist-packages/sqlalchemy/engine/default.py", line 593, in do_execute
        cursor.execute(statement, parameters)
    sqlalchemy.exc.ProgrammingError: (psycopg2.errors.DatatypeMismatch) value type decimal doesn't match type int of column "expires_at"
    HINT:  you will need to rewrite or cast the expression

    [SQL: INSERT INTO oauth_access_tokens (client_id, grant_type, expires_at, refresh_token, refresh_expires_at, user_id, session_id, hashed, prefix, created, last_activity) VALUES (%(client_id)s, %(grant_type)s, %(expires_at)s, %(refresh_token)s, %(refresh_expires_at)s, %(user_id)s, %(session_id)s, %(hashed)s, %(prefix)s, %(created)s, %(last_activity)s) RETURNING oauth_access_tokens.id]
    [parameters: {'client_id': 'jupyterhub-user-user2', 'grant_type': 'authorization_code', 'expires_at': 1622303104.418992, 'refresh_token': 'FVJ8S4is0367LlEMnxIiEIoTOeoxhf', 'refresh_expires_at': None, 'user_id': 662636890939424770, 'session_id': '4e041a2bfcb34a34a00033a281bc1236', 'hashed': 'sha512:1:3b18deae37fbf50a:03df035736960af14e19196e1d13fd74f55c21f17405119f80e75817ff37c7567fab089a3d40b97a57f94b54065ee56f7260895352516b9facb989d656f05be8', 'prefix': 't11z', 'created': datetime.datetime(2021, 5, 29, 14, 45, 4, 421305), 'last_activity': None}]
    (Background on this error at: http://sqlalche.me/e/13/f405)

[W 2021-05-29 14:45:04.430 JupyterHub base:110] Rolling back session due to database error (psycopg2.errors.DatatypeMismatch) value type decimal doesn't match type int of column "expires_at"
    HINT:  you will need to rewrite or cast the expression

    [SQL: INSERT INTO oauth_access_tokens (client_id, grant_type, expires_at, refresh_token, refresh_expires_at, user_id, session_id, hashed, prefix, created, last_activity) VALUES (%(client_id)s, %(grant_type)s, %(expires_at)s, %(refresh_token)s, %(refresh_expires_at)s, %(user_id)s, %(session_id)s, %(hashed)s, %(prefix)s, %(created)s, %(last_activity)s) RETURNING oauth_access_tokens.id]
    [parameters: {'client_id': 'jupyterhub-user-user2', 'grant_type': 'authorization_code', 'expires_at': 1622303104.418992, 'refresh_token': 'FVJ8S4is0367LlEMnxIiEIoTOeoxhf', 'refresh_expires_at': None, 'user_id': 662636890939424770, 'session_id': '4e041a2bfcb34a34a00033a281bc1236', 'hashed': 'sha512:1:3b18deae37fbf50a:03df035736960af14e19196e1d13fd74f55c21f17405119f80e75817ff37c7567fab089a3d40b97a57f94b54065ee56f7260895352516b9facb989d656f05be8', 'prefix': 't11z', 'created': datetime.datetime(2021, 5, 29, 14, 45, 4, 421305), 'last_activity': None}]
    (Background on this error at: http://sqlalche.me/e/13/f405)
[E 2021-05-29 14:45:04.443 JupyterHub log:173] {
      "Host": "hub:8081",
      "User-Agent": "python-requests/2.25.1",
      "Accept-Encoding": "gzip, deflate",
      "Accept": "*/*",
      "Connection": "keep-alive",
      "Content-Type": "application/x-www-form-urlencoded",
      "Authorization": "token [secret]",
      "Content-Length": "190"
    }
[E 2021-05-29 14:45:04.444 JupyterHub log:181] 500 POST /hub/api/oauth2/token (user2 10.42.80.10) 63.28ms
```

Everything went well, when I changed:
`expires_at=orm.OAuthAccessToken.now() + token['expires_in'],`
to:
`expires_at=int(orm.OAuthAccessToken.now() + token['expires_in']),`
That's what this PR is about.

As a sidenote, `black` formatter adjusted the `orm_client = orm.OAuthClient(identifier=client_id,)` line, but I guess it should be fine. Please, feel free to revert this change if needed.

(Upd): added the missing `int` conversion.

Signed-off-by: Erik Sundell <erik.i.sundell@gmail.com>
2021-07-15 10:16:08 +02:00
Min RK
2c8b29b6bb Merge pull request #3467 from minrk/1.4.x
Prepare for 1.4.1
2021-05-12 17:16:58 +02:00
Min RK
a53178a92b Backport PR #3462: prepare to rename default branch to main
- update references to default branch name in docs, workflows
- use HEAD in github urls, which always works regardless of default branch name
- fix petstore URLs since the old petstore links seem to have stopped working

to merge, in order:

- [x] approve this PR
- [x] rename the default branch to main in settings
- [x] merge this PR

Related tangent: I've been using [this git default-branch](https://github.com/minrk/git-stuff/blob/main/bin/git-default-branch) to help with my aliases and friends working with repos with different branch names.

Signed-off-by: Min RK <benjaminrk@gmail.com>
2021-05-12 15:51:06 +02:00
Min RK
e032cda638 release 1.4.1 2021-05-12 15:45:27 +02:00
Min RK
40820b3489 changelog for 1.4.1 2021-05-12 15:42:21 +02:00
Min RK
80f4454371 Backport PR #3457: ci: fix typo in environment variable
When i setup the release workflow i made a typo in an environment variable so signing into Docker Hub now fails.

Observed in https://github.com/jupyterhub/jupyterhub/pull/3456 issuecomment-832923798.

Signed-off-by: Min RK <benjaminrk@gmail.com>
2021-05-12 15:36:39 +02:00
Erik Sundell
4d0005b0b7 Backport PR #3454: define Spawner.delete_forever on base Spawner
...where I thought it already was! Instead of on the test class.

and fix the logic for when it is called a bit:

- call on *all* Spawners, not just the default
- call on named server deletion when remove=True

closes  3451, finishes  3337

Signed-off-by: Min RK <benjaminrk@gmail.com>
2021-05-12 15:36:36 +02:00
Erik Sundell
86761ff0d4 Backport PR #3456: avoid re-using asyncio.Locks across event loops
should never occur in real applications where only one loop is run, but may occur in tests if the Proxy object lives longer than the loop that is running when it's created (imported?).

I *suspect* this is the source of our intermittent test failures with:

> got Future <Future pending> attached to a different loop

But since they are intermittent, it's hard to be sure, even if this PR passes.

The issue: we were allocating an asyncio.Lock(), which in turn grabs a handle on the current event loop, at *method definition time* in the decorator, instead of *call time*.

The solution: allocate the method at call time *and* double-check to ensure we never use a lock across event loops by storing the locks per-loop.

This should change nothing for 'real' hub instances, where only one loop is ever running, only tests where we start and stop loops a bunch.

Signed-off-by: Min RK <benjaminrk@gmail.com>
2021-05-12 15:36:34 +02:00
Min RK
32a2a3031c Backport PR #3437: patch base handlers from both jupyter_server and notebook
and clarify warning when a base handler isn't patched that auth is still being applied

- reorganize patch steps into functions for easier re-use
- patch notebook and jupyter_server handlers if they are already imported
- run patch after initialize to ensure extensions have done their importing before we check what's present
- apply class-level patch even when instance-level patch is happening to avoid triggering patch on every request

This change isn't as big as it looks, because it's mostly moving some re-used code to a couple of functions.

closes https://github.com/jupyter-server/jupyter_server/issues/488

Signed-off-by: Min RK <benjaminrk@gmail.com>
2021-05-12 15:36:31 +02:00
Min RK
16352496da Backport PR #3452: Fix documentation
Signed-off-by: Min RK <benjaminrk@gmail.com>
2021-05-12 15:36:28 +02:00
Min RK
2259f57772 Backport PR #3436: ci: github workflow security, pin action to sha etc
Pin references to github actions we rely on in workflows with jobs that reference GitHub secrets that could get exposed.

Signed-off-by: Min RK <benjaminrk@gmail.com>
2021-05-12 15:36:26 +02:00
273 changed files with 5705 additions and 30043 deletions

View File

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

View File

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

View File

@@ -1,49 +1,27 @@
# 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.
#
# Build releases and (on tags) publish to PyPI
name: Release
# always build releases (to make sure wheel-building works)
# but only publish to PyPI on tags
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:
pull_request:
jobs:
build-release:
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
- uses: actions/checkout@v2
- uses: actions/setup-python@v2
with:
python-version: "3.9"
python-version: 3.8
- uses: actions/setup-node@v3
- uses: actions/setup-node@v1
with:
node-version: "14"
- name: install build requirements
- name: install build package
run: |
npm install -g yarn
pip install --upgrade pip
pip install build
pip freeze
@@ -53,21 +31,28 @@ jobs:
python -m build --sdist --wheel .
ls -l dist
- name: verify sdist
- name: verify wheel
run: |
./ci/check_sdist.py dist/jupyterhub-*.tar.gz
- name: verify data-files are installed where they are found
run: |
pip install dist/*.whl
./ci/check_installed_data.py
- name: verify sdist can be installed without npm/yarn
run: |
docker run --rm -v $PWD/dist:/dist:ro docker.io/library/python:3.9-slim-bullseye bash -c 'pip install /dist/jupyterhub-*.tar.gz'
cd dist
pip install ./*.whl
# verify data-files are installed where they are found
cat <<EOF | python
import os
from jupyterhub._data import DATA_FILES_PATH
print(f"DATA_FILES_PATH={DATA_FILES_PATH}")
assert os.path.exists(DATA_FILES_PATH), DATA_FILES_PATH
for subpath in (
"templates/page.html",
"static/css/style.min.css",
"static/components/jquery/dist/jquery.js",
):
path = os.path.join(DATA_FILES_PATH, subpath)
assert os.path.exists(path), path
print("OK")
EOF
# ref: https://github.com/actions/upload-artifact#readme
- uses: actions/upload-artifact@v3
- uses: actions/upload-artifact@v2
with:
name: jupyterhub-${{ github.sha }}
path: "dist/*"
@@ -102,16 +87,17 @@ jobs:
echo "REGISTRY=localhost:5000/" >> $GITHUB_ENV
fi
- uses: actions/checkout@v3
- uses: actions/checkout@v2
# Setup docker to build for multiple platforms, see:
# https://github.com/docker/build-push-action/tree/v2.4.0#usage
# https://github.com/docker/build-push-action/blob/v2.4.0/docs/advanced/multi-platform.md
- name: Set up QEMU (for docker buildx)
uses: docker/setup-qemu-action@e81a89b1732b9c48d79cd809d8d81d79c4647a18 # associated tag: v1.0.2
uses: docker/setup-qemu-action@25f0500ff22e406f7191a2a8ba8cda16901ca018 # associated tag: v1.0.2
- name: Set up Docker Buildx (for multi-arch builds)
uses: docker/setup-buildx-action@8c0edbc76e98fa90f69d9a2c020dcb50019dc325
uses: docker/setup-buildx-action@2a4b53665e15ce7d7049afb11ff1f70ff1610609 # associated tag: v1.1.2
with:
# Allows pushing to registry on localhost:5000
driver-opts: network=host
@@ -130,8 +116,6 @@ jobs:
run: |
docker login -u "${{ 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
@@ -141,15 +125,14 @@ jobs:
# 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
uses: jupyterhub/action-major-minor-tag-calculator@v1
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/jupyterhub:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub
uses: docker/build-push-action@c56af957549030174b10d6867f20e78cfd7debc5
uses: docker/build-push-action@e1b7f96249f2e4c8e4ac1519b9608c0d48944a1f # associated tag: v2.4.0
with:
context: .
platforms: linux/amd64,linux/arm64
@@ -158,19 +141,18 @@ jobs:
# array into a comma separated list of tags
tags: ${{ join(fromJson(steps.jupyterhubtags.outputs.tags)) }}
# image: jupyterhub/jupyterhub-onbuild
#
# jupyterhub-onbuild
- name: Get list of jupyterhub-onbuild tags
id: onbuildtags
uses: jupyterhub/action-major-minor-tag-calculator@v2
uses: jupyterhub/action-major-minor-tag-calculator@v1
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/jupyterhub-onbuild:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub-onbuild:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub-onbuild
uses: docker/build-push-action@c56af957549030174b10d6867f20e78cfd7debc5
uses: docker/build-push-action@e1b7f96249f2e4c8e4ac1519b9608c0d48944a1f # associated tag: v2.4.0
with:
build-args: |
BASE_IMAGE=${{ fromJson(steps.jupyterhubtags.outputs.tags)[0] }}
@@ -179,19 +161,18 @@ jobs:
push: true
tags: ${{ join(fromJson(steps.onbuildtags.outputs.tags)) }}
# image: jupyterhub/jupyterhub-demo
#
# jupyterhub-demo
- name: Get list of jupyterhub-demo tags
id: demotags
uses: jupyterhub/action-major-minor-tag-calculator@v2
uses: jupyterhub/action-major-minor-tag-calculator@v1
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/jupyterhub-demo:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/jupyterhub-demo:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub-demo
uses: docker/build-push-action@c56af957549030174b10d6867f20e78cfd7debc5
uses: docker/build-push-action@e1b7f96249f2e4c8e4ac1519b9608c0d48944a1f # associated tag: v2.4.0
with:
build-args: |
BASE_IMAGE=${{ fromJson(steps.onbuildtags.outputs.tags)[0] }}
@@ -202,24 +183,3 @@ jobs:
platforms: linux/amd64
push: true
tags: ${{ join(fromJson(steps.demotags.outputs.tags)) }}
# image: jupyterhub/singleuser
#
- name: Get list of jupyterhub/singleuser tags
id: singleusertags
uses: jupyterhub/action-major-minor-tag-calculator@v2
with:
githubToken: ${{ secrets.GITHUB_TOKEN }}
prefix: "${{ env.REGISTRY }}jupyterhub/singleuser:"
defaultTag: "${{ env.REGISTRY }}jupyterhub/singleuser:noref"
branchRegex: ^\w[\w-.]*$
- name: Build and push jupyterhub/singleuser
uses: docker/build-push-action@c56af957549030174b10d6867f20e78cfd7debc5
with:
build-args: |
JUPYTERHUB_VERSION=${{ github.ref_type == 'tag' && github.ref_name || format('git:{0}', github.sha) }}
context: singleuser
platforms: linux/amd64,linux/arm64
push: true
tags: ${{ join(fromJson(steps.singleusertags.outputs.tags)) }}

View File

@@ -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"

View File

@@ -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@v1.3.2
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
- name: pytest docs/
run: |
pytest docs/

View File

@@ -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

View File

@@ -1,44 +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
# 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"
SQLALCHEMY_WARN_20: "1"
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: ${{ failure() }}
run: |
echo "You can install pre-commit hooks to automatically run formatting"
echo "on each commit with:"
echo " pre-commit install"
echo "or you can run by hand on staged files with"
echo " pre-commit run"
echo "or after-the-fact on already committed files with"
echo " pre-commit run --all-files"
# Run "pytest jupyterhub/tests" in various configurations
pytest:
runs-on: ubuntu-20.04
timeout-minutes: 15
timeout-minutes: 10
strategy:
# Keep running even if one variation of the job fail
@@ -57,9 +73,9 @@ jobs:
# Tests everything when JupyterHub works against a dedicated mysql or
# postgresql server.
#
# legacy_notebook:
# jupyter_server:
# Tests everything when the user instances are started with
# the legacy notebook server instead of jupyter_server.
# jupyter_server instead of notebook.
#
# ssl:
# Tests everything using internal SSL connections instead of
@@ -67,30 +83,25 @@ jobs:
#
# main_dependencies:
# Tests everything when the we use the latest available dependencies
# from: traitlets.
# from: ipytraitlets.
#
# NOTE: Since only the value of these parameters are presented in the
# GitHub UI when the workflow run, we avoid using true/false as
# values by instead duplicating the name to signal true.
# Python versions available at:
# https://github.com/actions/python-versions/blob/HEAD/versions-manifest.json
include:
- python: "3.7"
- python: "3.6"
oldest_dependencies: oldest_dependencies
legacy_notebook: legacy_notebook
- python: "3.8"
legacy_notebook: legacy_notebook
- python: "3.9"
db: mysql
- python: "3.10"
db: postgres
- python: "3.11"
- python: "3.6"
subdomain: subdomain
- python: "3.11"
- python: "3.7"
db: mysql
- python: "3.7"
ssl: ssl
- python: "3.11"
selenium: selenium
- python: "3.11"
- python: "3.8"
db: postgres
- python: "3.8"
jupyter_server: jupyter_server
- python: "3.9"
main_dependencies: main_dependencies
steps:
@@ -118,30 +129,29 @@ jobs:
if [ "${{ 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: ${{ matrix.python }}
- name: Install Python dependencies
run: |
pip install --upgrade pip
pip install -e ".[test]"
pip install --upgrade . -r dev-requirements.txt
if [ "${{ matrix.oldest_dependencies }}" != "" ]; then
# take any dependencies in requirements.txt such as tornado>=5.0
@@ -153,11 +163,10 @@ jobs:
if [ "${{ matrix.main_dependencies }}" != "" ]; then
pip install git+https://github.com/ipython/traitlets#egg=traitlets --force
pip install --upgrade --pre sqlalchemy
fi
if [ "${{ matrix.legacy_notebook }}" != "" ]; then
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
@@ -193,47 +202,33 @@ 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 "$(which psql)" ]]; then
sudo apt-get update
sudo apt-get install -y postgresql-client
fi
DB=postgres bash ci/docker-db.sh
DB=postgres bash ci/init-db.sh
fi
- name: Setup Firefox
if: matrix.selenium
uses: browser-actions/setup-firefox@latest
with:
firefox-version: latest
- name: Setup Geckodriver
if: matrix.selenium
uses: browser-actions/setup-geckodriver@latest
- name: Configure selenium tests
if: matrix.selenium
run: echo "PYTEST_ADDOPTS=$PYTEST_ADDOPTS -m selenium" >> "${GITHUB_ENV}"
- name: Run pytest
# FIXME: --color=yes explicitly set because:
# https://github.com/actions/runner/issues/241
run: |
pytest --maxfail=2 --cov=jupyterhub jupyterhub/tests
- uses: codecov/codecov-action@v3
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
timeout-minutes: 10
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v2
- name: build images
run: |

5
.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
@@ -32,5 +29,3 @@ htmlcov
pip-wheel-metadata
docs/source/reference/metrics.rst
oldest-requirements.txt
jupyterhub-proxy.pid
examples/server-api/service-token

View File

@@ -1,61 +1,24 @@
# 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: v3.2.2
- repo: https://github.com/asottile/reorder_python_imports
rev: v1.9.0
hooks:
- id: pyupgrade
args:
- --py36-plus
# Autoformat: Python code
- repo: https://github.com/PyCQA/autoflake
rev: v2.0.0
hooks:
- id: autoflake
# args ref: https://github.com/PyCQA/autoflake#advanced-usage
args:
- --in-place
# Autoformat: Python code
- repo: https://github.com/pycqa/isort
rev: 5.10.1
hooks:
- id: isort
# Autoformat: Python code
- id: reorder-python-imports
- repo: https://github.com/psf/black
rev: 22.10.0
rev: 20.8b1
hooks:
- id: black
# Autoformat: markdown, yaml, javascript (see the file .prettierignore)
- repo: https://github.com/pre-commit/mirrors-prettier
rev: v3.0.0-alpha.4
rev: v2.2.1
hooks:
- id: prettier
# Autoformat and linting, misc. details
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.4.0
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: "6.0.0"
- repo: https://gitlab.com/pycqa/flake8
rev: "3.8.4"
hooks:
- id: flake8
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v3.4.0
hooks:
- id: end-of-file-fixer
- id: check-case-conflict
- id: check-executables-have-shebangs
- id: requirements-txt-fixer

View File

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

View File

@@ -1,25 +0,0 @@
# Configuration on how ReadTheDocs (RTD) builds our documentation
# ref: https://readthedocs.org/projects/jupyterhub/
# ref: https://docs.readthedocs.io/en/stable/config-file/v2.html
#
version: 2
sphinx:
configuration: docs/source/conf.py
build:
os: ubuntu-20.04
tools:
nodejs: "16"
python: "3.9"
python:
install:
- requirements: docs/requirements.txt
formats:
# Adding htmlzip enables a Downloads section in the rendered website's RTD
# menu where the html build can be downloaded. This doesn't require any
# additional configuration in docs/source/conf.py.
#
- htmlzip

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

@@ -6,9 +6,134 @@ you can follow the [Jupyter contributor guide](https://jupyter.readthedocs.io/en
Make sure to also follow [Project Jupyter's Code of Conduct](https://github.com/jupyter/governance/blob/HEAD/conduct/code_of_conduct.md)
for a friendly and welcoming collaborative environment.
Please see our documentation on
## Setting up a development environment
- [Setting up a development install](https://jupyterhub.readthedocs.io/en/latest/contributing/setup.html)
- [Testing JupyterHub and linting code](https://jupyterhub.readthedocs.io/en/latest/contributing/tests.html)
<!--
https://jupyterhub.readthedocs.io/en/stable/contributing/setup.html
contains a lot of the same information. Should we merge the docs and
just have this page link to that one?
-->
If you need some help, feel free to ask on [Gitter](https://gitter.im/jupyterhub/jupyterhub) or [Discourse](https://discourse.jupyter.org/).
JupyterHub requires Python >= 3.5 and nodejs.
As a Python project, a development install of JupyterHub follows standard practices for the basics (steps 1-2).
1. clone the repo
```bash
git clone https://github.com/jupyterhub/jupyterhub
```
2. do a development install with pip
```bash
cd jupyterhub
python3 -m pip install --editable .
```
3. install the development requirements,
which include things like testing tools
```bash
python3 -m pip install -r dev-requirements.txt
```
4. install configurable-http-proxy with npm:
```bash
npm install -g configurable-http-proxy
```
5. set up pre-commit hooks for automatic code formatting, etc.
```bash
pre-commit install
```
You can also invoke the pre-commit hook manually at any time with
```bash
pre-commit run
```
## Contributing
JupyterHub has adopted automatic code formatting so you shouldn't
need to worry too much about your code style.
As long as your code is valid,
the pre-commit hook should take care of how it should look.
You can invoke the pre-commit hook by hand at any time with:
```bash
pre-commit run
```
which should run any autoformatting on your code
and tell you about any errors it couldn't fix automatically.
You may also install [black integration](https://github.com/psf/black#editor-integration)
into your text editor to format code automatically.
If you have already committed files before setting up the pre-commit
hook with `pre-commit install`, you can fix everything up using
`pre-commit run --all-files`. You need to make the fixing commit
yourself after that.
## Testing
It's a good idea to write tests to exercise any new features,
or that trigger any bugs that you have fixed to catch regressions.
You can run the tests with:
```bash
pytest -v
```
in the repo directory. If you want to just run certain tests,
check out the [pytest docs](https://pytest.readthedocs.io/en/latest/usage.html)
for how pytest can be called.
For instance, to test only spawner-related things in the REST API:
```bash
pytest -v -k spawn jupyterhub/tests/test_api.py
```
The tests live in `jupyterhub/tests` and are organized roughly into:
1. `test_api.py` tests the REST API
2. `test_pages.py` tests loading the HTML pages
and other collections of tests for different components.
When writing a new test, there should usually be a test of
similar functionality already written and related tests should
be added nearby.
The fixtures live in `jupyterhub/tests/conftest.py`. There are
fixtures that can be used for JupyterHub components, such as:
- `app`: an instance of JupyterHub with mocked parts
- `auth_state_enabled`: enables persisting auth_state (like authentication tokens)
- `db`: a sqlite in-memory DB session
- `io_loop`: a Tornado event loop
- `event_loop`: a new asyncio event loop
- `user`: creates a new temporary user
- `admin_user`: creates a new temporary admin user
- single user servers
- `cleanup_after`: allows cleanup of single user servers between tests
- mocked service
- `MockServiceSpawner`: a spawner that mocks services for testing with a short poll interval
- `mockservice`: mocked service with no external service url
- `mockservice_url`: mocked service with a url to test external services
And fixtures to add functionality or spawning behavior:
- `admin_access`: grants admin access
- `no_patience`: sets slow-spawning timeouts to zero
- `slow_spawn`: enables the SlowSpawner (a spawner that takes a few seconds to start)
- `never_spawn`: enables the NeverSpawner (a spawner that will never start)
- `bad_spawn`: enables the BadSpawner (a spawner that fails immediately)
- `slow_bad_spawn`: enables the SlowBadSpawner (a spawner that fails after a short delay)
To read more about fixtures check out the
[pytest docs](https://docs.pytest.org/en/latest/fixture.html)
for how to use the existing fixtures, and how to create new ones.
When in doubt, feel free to [ask](https://gitter.im/jupyterhub/jupyterhub).

View File

@@ -21,7 +21,7 @@
# your jupyterhub_config.py will be added automatically
# from your docker directory.
ARG BASE_IMAGE=ubuntu:22.04
ARG BASE_IMAGE=ubuntu:focal-20200729
FROM $BASE_IMAGE AS builder
USER root
@@ -35,14 +35,12 @@ RUN apt-get update \
python3-dev \
python3-pip \
python3-pycurl \
python3-venv \
nodejs \
npm \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
RUN python3 -m pip install --upgrade setuptools pip build wheel
RUN npm install --global yarn
RUN python3 -m pip install --upgrade setuptools pip wheel
# copy everything except whats in .dockerignore, its a
# compromise between needing to rebuild and maintaining
@@ -52,7 +50,7 @@ WORKDIR /src/jupyterhub
# Build client component packages (they will be copied into ./share and
# packaged with the built wheel.)
RUN python3 -m build --wheel
RUN python3 setup.py bdist_wheel
RUN python3 -m pip wheel --wheel-dir wheelhouse dist/*.whl

View File

@@ -8,7 +8,6 @@ include *requirements.txt
include Dockerfile
graft onbuild
graft jsx
graft jupyterhub
graft scripts
graft share
@@ -19,10 +18,6 @@ graft ci
graft docs
prune docs/node_modules
# Intermediate javascript files
prune jsx/node_modules
prune jsx/build
# prune some large unused files from components
prune share/jupyterhub/static/components/bootstrap/dist/css
exclude share/jupyterhub/static/components/bootstrap/dist/fonts/*.svg

View File

@@ -6,37 +6,27 @@
**[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)
[![DockerHub build status](https://img.shields.io/docker/build/jupyterhub/jupyterhub?logo=docker&label=build)](https://hub.docker.com/r/jupyterhub/jupyterhub/tags)
[![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/main/graph/badge.svg)](https://codecov.io/gh/jupyterhub/jupyterhub)
[![GitHub](https://img.shields.io/badge/issue_tracking-github-blue?logo=github)](https://github.com/jupyterhub/jupyterhub/issues)
[![Discourse](https://img.shields.io/badge/help_forum-discourse-blue?logo=discourse)](https://discourse.jupyter.org/c/jupyterhub)
[![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 +40,36 @@ 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](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/jupyter/jupyterhub/HEAD/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
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 +85,12 @@ 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`
@@ -105,10 +102,10 @@ npm install -g configurable-http-proxy
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
@@ -117,9 +114,10 @@ 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
_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
@@ -190,7 +188,7 @@ this a good choice for **testing JupyterHub on your desktop or laptop**.
If you want to run docker on a computer that has a public IP then you should
(as in MUST) **secure it with ssl** by adding ssl options to your docker
configuration or by using an ssl enabled proxy.
configuration or by using a ssl enabled proxy.
[Mounting volumes](https://docs.docker.com/engine/admin/volumes/volumes/) will
allow you to **store data outside the docker image (host system) so it will be persistent**, even when you start
@@ -230,17 +228,18 @@ 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](https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/jupyter/jupyterhub/HEAD/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

@@ -1,55 +0,0 @@
# How to make a release
`jupyterhub` is a package available on [PyPI][] and [conda-forge][].
These are instructions on how to make a release.
## Pre-requisites
- Push rights to [jupyterhub/jupyterhub][]
- Push rights to [conda-forge/jupyterhub-feedstock][]
## Steps to make a release
1. Create a PR updating `docs/source/changelog.md` with [github-activity][] and
continue only when its merged.
```shell
pip install github-activity
github-activity --heading-level=3 jupyterhub/jupyterhub
```
1. Checkout main and make sure it is up to date.
```shell
git checkout main
git fetch origin main
git reset --hard origin/main
```
1. Update the version, make commits, and push a git tag with `tbump`.
```shell
pip install tbump
tbump --dry-run ${VERSION}
tbump ${VERSION}
```
Following this, the [CI system][] will build and publish a release.
1. Reset the version back to dev, e.g. `2.1.0.dev` after releasing `2.0.0`
```shell
tbump --no-tag ${NEXT_VERSION}.dev
```
1. Following the release to PyPI, an automated PR should arrive to
[conda-forge/jupyterhub-feedstock][] with instructions.
[pypi]: https://pypi.org/project/jupyterhub/
[conda-forge]: https://anaconda.org/conda-forge/jupyterhub
[jupyterhub/jupyterhub]: https://github.com/jupyterhub/jupyterhub
[conda-forge/jupyterhub-feedstock]: https://github.com/conda-forge/jupyterhub-feedstock
[github-activity]: https://github.com/executablebooks/github-activity
[ci system]: https://github.com/jupyterhub/jupyterhub/actions/workflows/release.yml

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

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

View File

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

View File

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

View File

@@ -19,9 +19,8 @@ else
fi
# Configure a set of databases in the database server for upgrade tests
# this list must be in sync with versions in test_db.py:test_upgrade
set -x
for SUFFIX in '' _upgrade_110 _upgrade_122 _upgrade_130 _upgrade_150 _upgrade_211; do
for SUFFIX in '' _upgrade_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

20
dev-requirements.txt Normal file
View File

@@ -0,0 +1,20 @@
-r requirements.txt
# temporary pin of attrs for jsonschema 0.3.0a1
# seems to be a pip bug
attrs>=17.4.0
beautifulsoup4
codecov
coverage
cryptography
html5lib # needed for beautifulsoup
mock
notebook
pre-commit
pytest>=3.3
pytest-asyncio
pytest-cov
requests-mock
# blacklist urllib3 releases affected by https://github.com/urllib3/urllib3/issues/1683
# I *think* this should only affect testing, not production
urllib3!=1.25.4,!=1.25.5
virtualenv

View File

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

View File

@@ -4,11 +4,6 @@ from jupyterhub._data import DATA_FILES_PATH
print(f"DATA_FILES_PATH={DATA_FILES_PATH}")
for sub_path in (
"templates",
"static/components",
"static/css/style.min.css",
"static/js/admin-react.js",
):
for sub_path in ("templates", "static/components", "static/css/style.min.css"):
path = os.path.join(DATA_FILES_PATH, sub_path)
assert os.path.exists(path), path

View File

@@ -53,20 +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
# If the pre-requisites for the html target is updated, also update the Read The
# Docs section in docs/source/conf.py.
#
html: metrics scopes
html: rest-api metrics
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
@echo
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."

View File

@@ -1,4 +1,5 @@
import os
from os.path import join
from pytablewriter import RstSimpleTableWriter
from pytablewriter.style import Style

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

@@ -1,21 +1,11 @@
# We install the jupyterhub package to help autodoc-traits inspect it and
# generate documentation.
#
# FIXME: If there is a way for this requirements.txt file to pass a flag that
# the build system can intercept to not build the javascript artifacts,
# then do so so. That would mean that installing the documentation can
# avoid needing node/npm installed.
#
--editable .
-r ../requirements.txt
alabaster_jupyterhub
autodoc-traits
myst-parser
pre-commit
docutils<0.18
pydata-sphinx-theme
pytablewriter>=0.56
ruamel.yaml
sphinx>=4
recommonmark>=0.6
sphinx>=1.7
sphinx-copybutton
sphinx-jsonschema
sphinxext-opengraph
sphinxext-rediraffe

893
docs/rest-api.yml Normal file
View File

@@ -0,0 +1,893 @@
# see me at: https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/jupyterhub/jupyterhub/HEAD/docs/rest-api.yml#/default
swagger: "2.0"
info:
title: JupyterHub
description: The REST API for JupyterHub
version: 1.5.0
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
parameters:
- name: state
in: query
required: false
type: string
enum: ["inactive", "active", "ready"]
description: |
Return only users who have servers in the given state.
If unspecified, return all users.
active: all users with any active servers (ready OR pending)
ready: all users who have any ready servers (running, not pending)
inactive: all users who have *no* active servers (complement of active)
Added in JupyterHub 1.3
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 if 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.

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height: 4rem !important;
}
/* hide redundant funky-formatted swagger-ui version */
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display: none !important;
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# Capacity planning
General capacity planning advice for JupyterHub is hard to give,
because it depends almost entirely on what your users are doing,
and what JupyterHub users do varies _wildly_ in terms of resource consumption.
**There is no single answer to "I have X users, what resources do I need?" or "How many users can I support with this machine?"**
Here are three _typical_ Jupyter use patterns that require vastly different resources:
- **Learning**: negligible resources because computation is mostly idle,
e.g. students learning programming for the first time
- **Production code**: very intense, sustained load, e.g. training machine learning models
- **Bursting**: _mostly_ idle, but needs a lot of resources for short periods of time
(interactive research often looks like this)
But just because there's no single answer doesn't mean we can't help.
So we have gathered here some useful information to help you make your decisions
about what resources you need based on how your users work,
including the relative invariants in terms of resources that JupyterHub itself needs.
## JupyterHub infrastructure
JupyterHub consists of a few components that are always running.
These take up very little resources,
especially relative to the resources consumed by users when you have more than a few.
As an example, an instance of mybinder.org (running JupyterHub 1.5.0),
running with typically ~100-150 users has:
| Component | CPU (mean/peak) | Memory (mean/peak) |
| --------- | --------------- | ------------------ |
| Hub | 4% / 13% | (230 MB / 260 MB) |
| Proxy | 6% / 13% | (47 MB / 65 MB) |
So it would be pretty generous to allocate ~25% of one CPU core
and ~500MB of RAM to overall JupyterHub infrastructure.
The rest is going to be up to your users.
Per-user overhead from JupyterHub is typically negligible
up to at least a few hundred concurrent active users.
```[figure} ../images/mybinder-hub-components-cpu-memory.png
JupyterHub component resource usage for mybinder.org.
```
## Factors to consider
### Static vs elastic resources
A big factor in planning resources is:
**how much does it cost to change your mind?**
If you are using a single shared machine with local storage,
migrating to a new one because it turns out your users don't fit might be very costly.
You will have to get a new machine, set it up, and maybe even migrate user data.
On the other hand, if you are using ephemeral resources,
such as node pools in Kubernetes,
changing resource types costs close to nothing
because nodes can automatically be added or removed as needed.
Take that cost into account when you are picking how much memory or cpu to allocate to users.
Static resources (like [the-littlest-jupyterhub][]) provide for more **stable, predictable costs**,
but elastic resources (like [zero-to-jupyterhub][]) tend to provide **lower overall costs**
(especially when deployed with monitoring allowing cost optimizations over time),
but which are **less predictable**.
[the-littlest-jupyterhub]: https://the-littlest-jupyterhub.readthedocs.io
[zero-to-jupyterhub]: https://zero-to-jupyterhub.readthedocs.io
(limits-requests)=
### Limit vs Request for resources
Many scheduling tools like Kubernetes have two separate ways of allocating resources to users.
A **Request** or **Reservation** describes how much resources are _set aside_ for each user.
Often, this doesn't have any practical effect other than deciding when a given machine is considered 'full'.
If you are using expandable resources like an autoscaling Kubernetes cluster,
a new node must be launched and added to the pool if you 'request' more resources than fit on currently running nodes (a cluster **scale-up event**).
If you are running on a single VM, this describes how many users you can run at the same time, full stop.
A **Limit**, on the other hand, enforces a limit to how much resources any given user can consume.
For more information on what happens when users try to exceed their limits, see [](oversubscription).
In the strictest, safest case, you can have these two numbers be the same.
That means that each user is _limited_ to fit within the resources allocated to it.
This avoids **[oversubscription](oversubscription)** of resources (allowing use of more than you have available),
at the expense (in a literal, this-costs-money sense) of reserving lots of usually-idle capacity.
However, you often find that a small fraction of users use more resources than others.
In this case you may give users limits that _go beyond the amount of resources requested_.
This is called **oversubscribing** the resources available to users.
Having a gap between the request and the limit means you can fit a number of _typical_ users on a node (based on the request),
but still limit how much a runaway user can gobble up for themselves.
(oversubscription)=
### Oversubscribed CPU is okay, running out of memory is bad
An important consideration when assigning resources to users is: **What happens when users need more than I've given them?**
A good summary to keep in mind:
> When tasks don't get enough CPU, things are slow.
> When they don't get enough memory, things are broken.
This means it's **very important that users have enough memory**,
but much less important that they always have exclusive access to all the CPU they can use.
This relates to [Limits and Requests](limits-requests),
because these are the consequences of your limits and/or requests not matching what users actually try to use.
A table of mismatched resource allocation situations and their consequences:
| issue | consequence |
| -------------------------------------------------------- | ------------------------------------------------------------------------------------- |
| Requests too high | Unnecessarily high cost and/or low capacity. |
| CPU limit too low | Poor performance experienced by users |
| CPU oversubscribed (too-low request + too-high limit) | Poor performance across the system; may crash, if severe |
| Memory limit too low | Servers killed by Out-of-Memory Killer (OOM); lost work for users |
| Memory oversubscribed (too-low request + too-high limit) | System memory exhaustion - all kinds of hangs and crashes and weird errors. Very bad. |
Note that the 'oversubscribed' problem case is where the request is lower than _typical_ usage,
meaning that the total reserved resources isn't enough for the total _actual_ consumption.
This doesn't mean that _all_ your users exceed the request,
just that the _limit_ gives enough room for the _average_ user to exceed the request.
All of these considerations are important _per node_.
Larger nodes means more users per node, and therefore more users to average over.
It also means more chances for multiple outliers on the same node.
### Example case for oversubscribing memory
Take for example, this system and sampling of user behavior:
- System memory = 8G
- memory request = 1G, limit = 3G
- typical 'heavy' user: 2G
- typical 'light' user: 0.5G
This will assign 8 users to those 8G of RAM (remember: only requests are used for deciding when a machine is 'full').
As long as the total of 8 users _actual_ usage is under 8G, everything is fine.
But the _limit_ allows a total of 24G to be used,
which would be a mess if everyone used their full limit.
But _not_ everyone uses the full limit, which is the point!
This pattern is fine if 1/8 of your users are 'heavy' because _typical_ usage will be ~0.7G,
and your total usage will be ~5G (`1 × 2 + 7 × 0.5 = 5.5`).
But if _50%_ of your users are 'heavy' you have a problem because that means your users will be trying to use 10G (`4 × 2 + 4 × 0.5 = 10`),
which you don't have.
You can make guesses at these numbers, but the only _real_ way to get them is to measure (see [](measuring)).
### CPU:memory ratio
Most of the time, you'll find that only one resource is the limiting factor for your users.
Most often it's memory, but for certain tasks, it could be CPU (or even GPUs).
Many cloud deployments have just one or a few fixed ratios of cpu to memory
(e.g. 'general purpose', 'high memory', and 'high cpu').
Setting your secondary resource allocation according to this ratio
after selecting the more important limit results in a balanced resource allocation.
For instance, some of Google Cloud's ratios are:
| node type | GB RAM / CPU core |
| ----------- | ----------------- |
| n2-highmem | 8 |
| n2-standard | 4 |
| n2-highcpu | 1 |
(idleness)=
### Idleness
Jupyter being an interactive tool means people tend to spend a lot more time reading and thinking than actually running resource-intensive code.
This significantly affects how much _cpu_ resources a typical active user needs,
but often does not significantly affect the _memory_.
Ways to think about this:
- More idle users means unused CPU.
This generally means setting your CPU _limit_ higher than your CPU _request_.
- What do your users do when they _are_ running code?
Is it typically single-threaded local computation in a notebook?
If so, there's little reason to set a limit higher than 1 CPU core.
- Do typical computations take a long time, or just a few seconds?
Longer typical computations means it's more likely for users to be trying to use the CPU at the same moment,
suggesting a higher _request_.
- Even with idle users, parallel computation adds up quickly - one user fully loading 4 cores and 3 using almost nothing still averages to more than a full CPU core per user.
- Long-running intense computations suggest higher requests.
Again, using mybinder.org as an example—we run around 100 users on 8-core nodes,
and still see fairly _low_ overall CPU usage on each user node.
The limit here is actually Kubernetes' pods per node, not memory _or_ CPU.
This is likely a extreme case, as many Binder users come from clicking links on webpages
without any actual intention of running code.
```[figure} ../images/mybinder-load5.png
mybinder.org node CPU usage is low with 50-150 users sharing just 8 cores
```
### Concurrent users and culling idle servers
Related to [][idleness], all of these resource consumptions and limits are calculated based on **concurrently active users**,
not total users.
You might have 10,000 users of your JupyterHub deployment, but only 100 of them running at any given time.
That 100 is the main number you need to use for your capacity planning.
JupyterHub costs scale very little based on the number of _total_ users,
up to a point.
There are two important definitions for **active user**:
- Are they _actually_ there (i.e. a human interacting with Jupyter, or running code that might be )
- Is their server running (this is where resource reservations and limits are actually applied)
Connecting those two definitions (how long are servers running if their humans aren't using them) is an important area of deployment configuration, usually implemented via the [JupyterHub idle culler service][idle-culler].
[idle-culler]: https://github.com/jupyterhub/jupyterhub-idle-culler
There are a lot of considerations when it comes to culling idle users that will depend:
- How much does it save me to shut down user servers? (e.g. keeping an elastic cluster small, or keeping a fixed-size deployment available to active users)
- How much does it cost my users to have their servers shut down? (e.g. lost work if shutdown prematurely)
- How easy do I want it to be for users to keep their servers running? (e.g. Do they want to run unattended simulations overnight? Do you want them to?)
Like many other things in this guide, there are many correct answers leading to different configuration choices.
For more detail on culling configuration and considerations, consult the [JupyterHub idle culler documentation][idle-culler].
## More tips
### Start strict and generous, then measure
A good tip, in general, is to give your users as much resources as you can afford that you think they _might_ use.
Then, use resource usage metrics like prometheus to analyze what your users _actually_ need,
and tune accordingly.
Remember: **Limits affect your user experience and stability. Requests mostly affect your costs**.
For example, a sensible starting point (lacking any other information) might be:
```yaml
request:
cpu: 0.5
mem: 2G
limit:
cpu: 1
mem: 2G
```
(more memory if significant computations are likely - machine learning models, data analysis, etc.)
Some actions
- If you see out-of-memory killer events, increase the limit (or talk to your users!)
- If you see typical memory well below your limit, reduce the request (but not the limit)
- If _nobody_ uses that much memory, reduce your limit
- If CPU is your limiting scheduling factor and your CPUs are mostly idle,
reduce the cpu request (maybe even to 0!).
- If CPU usage continues to be low, increase the limit to 2 or 4 to allow bursts of parallel execution.
(measuring)=
### Measuring user resource consumption
It is _highly_ recommended to deploy monitoring services such as [Prometheus][]
and [Grafana][] to get a view of your users' resource usage.
This is the only way to truly know what your users need.
JupyterHub has some experimental [grafana dashboards][] you can use as a starting point,
to keep an eye on your resource usage.
Here are some sample charts from (again from mybinder.org),
showing >90% of users using less than 10% CPU and 200MB,
but a few outliers near the limit of 1 CPU and 2GB of RAM.
This is the kind of information you can use to tune your requests and limits.
![Snapshot from JupyterHub's Grafana dashboards on mybinder.org](../images/mybinder-user-resources.png)
[prometheus]: https://prometheus.io
[grafana]: https://grafana.com
[grafana dashboards]: https://github.com/jupyterhub/grafana-dashboards
### Measuring costs
Measuring costs may be as important as measuring your users activity.
If you are using a cloud provider, you can often use cost thresholds and quotas to instruct them to notify you if your costs are too high,
e.g. "Have AWS send me an email if I hit X spending trajectory on week 3 of the month."
You can then use this information to tune your resources based on what you can afford.
You can mix this information with user resource consumption to figure out if you have a problem,
e.g. "my users really do need X resources, but I can only afford to give them 80% of X."
This information may prove useful when asking your budget-approving folks for more funds.
### Additional resources
There are lots of other resources for cost and capacity planning that may be specific to JupyterHub and/or your cloud provider.
Here are some useful links to other resources
- [Zero to JupyterHub](https://zero-to-jupyterhub.readthedocs.io) documentation on
- [projecting costs](https://zero-to-jupyterhub.readthedocs.io/en/latest/administrator/cost.html)
- [configuring user resources](https://zero-to-jupyterhub.readthedocs.io/en/latest/jupyterhub/customizing/user-resources.html)
- Cloud platform cost calculators:
- [Google Cloud](https://cloud.google.com/products/calculator/)
- [Amazon AWS](https://calculator.s3.amazonaws.com)
- [Microsoft Azure](https://azure.microsoft.com/en-us/pricing/calculator/)

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 intends to describe some common
log messages, what they mean and what are the most common causes that generated them, as well as some possible ways to fix them.
## Failing suspected API request to not-running server
### Example
Your logs might be littered with lines that look scary
```
[W 2022-03-10 17:25:19.774 JupyterHub base:1349] Failing suspected API request to not-running server: /hub/user/<user-name>/api/metrics/v1
```
### Cause
This likely means that the user's server has stopped running but they
still have a browser tab open. For example, you might have 3 tabs open and you shut
the server down via one.
Another possible reason could be that you closed your laptop and the server was culled for inactivity, then reopened the laptop!
However, the client-side code (JupyterLab, Classic Notebook, etc) doesn't interpret the shut-down server and continues to make some API requests.
JupyterHub's architecture means that the proxy routes all requests that
don't go to a running user server to the hub process itself. The hub
process then explicitly returns a failure response, so the client knows
that the server is not running anymore. This is used by JupyterLab to
inform the user that the server is not running anymore, and provide an option
to restart it.
Most commonly, you'll see this in reference to the `/api/metrics/v1`
URL, used by [jupyter-resource-usage](https://github.com/jupyter-server/jupyter-resource-usage).
### Actions you can take
This log message is benign, and there is usually no action for you to take.
## JupyterHub Singleuser Version mismatch
### Example
```
jupyterhub version 1.5.0 != jupyterhub-singleuser version 1.3.0. This could cause failure to authenticate and result in redirect loops!
```
### Cause
JupyterHub requires the `jupyterhub` python package installed inside the image or
environment, the user server starts in. This message indicates that the version of
the `jupyterhub` package installed inside the user image or environment is not
the same as the JupyterHub server's version itself. This is not necessarily always a
problem - some version drift is mostly acceptable, and the only two known cases of
breakage are across the 0.7 and 2.0 version releases. In those cases, issues pop
up immediately after upgrading your version of JupyterHub, so **always check the JupyterHub
changelog before upgrading!**. The primary problems this _could_ cause are:
1. Infinite redirect loops after the user server starts
2. Missing expected environment variables in the user server once it starts
3. Failure for the started user server to authenticate with the JupyterHub server -
note that this is _not_ the same as _user authentication_ failing!
However, for the most part, unless you are seeing these specific issues, the log
message should be counted as a warning to get the `jupyterhub` package versions
aligned, rather than as an indicator of an existing problem.
### Actions you can take
Upgrade the version of the `jupyterhub` package in your user environment or image
so that it matches the version of JupyterHub running your JupyterHub server! If you
are using the [zero-to-jupyterhub](https://z2jh.jupyter.org) helm chart, you can find the appropriate
version of the `jupyterhub` package to install in your user image [here](https://jupyterhub.github.io/helm-chart/)

View File

@@ -1,3 +1,5 @@
.. _admin/upgrading:
====================
Upgrading JupyterHub
====================
@@ -6,34 +8,34 @@ JupyterHub offers easy upgrade pathways between minor versions. This
document describes how to do these upgrades.
If you are using :ref:`a JupyterHub distribution <index/distributions>`, you
should consult the distribution's documentation on how to upgrade. This documentation is
for those who have set up their JupyterHub without using a distribution.
should consult the distribution's documentation on how to upgrade. This
document is if you have set up your own JupyterHub without using a
distribution.
This documentation is lengthy because it is quite detailed. Most likely, upgrading
It is long because is pretty detailed! Most likely, upgrading
JupyterHub is painless, quick and with minimal user interruption.
The steps are discussed in detail, so if you get stuck at any step you can always refer to this guide.
Read the Changelog
==================
The `changelog <../changelog.md>`_ contains information on what has
changed with the new JupyterHub release and any deprecation warnings.
The `changelog <../changelog.html>`_ contains information on what has
changed with the new JupyterHub release, and any deprecation warnings.
Read these notes to familiarize yourself with the coming changes. There
might be new releases of the authenticators & spawners you use, so
might be new releases of authenticators & spawners you are using, so
read the changelogs for those too!
Notify your users
=================
If you use the default configuration where ``configurable-http-proxy``
If you are using the default configuration where ``configurable-http-proxy``
is managed by JupyterHub, your users will see service disruption during
the upgrade process. You should notify them, and pick a time to do the
upgrade where they will be least disrupted.
If you use a different proxy or run ``configurable-http-proxy``
If you are using a different proxy, or running ``configurable-http-proxy``
independent of JupyterHub, your users will be able to continue using notebook
servers they had already launched, but will not be able to launch new servers or sign in.
servers they had already launched, but will not be able to launch new servers
nor sign in.
Backup database & config
@@ -41,37 +43,37 @@ Backup database & config
Before doing an upgrade, it is critical to back up:
#. Your JupyterHub database (SQLite by default, or MySQL / Postgres if you used those).
If you use SQLite (the default), you should backup the ``jupyterhub.sqlite`` file.
#. Your JupyterHub database (sqlite by default, or MySQL / Postgres
if you used those). If you are using sqlite (the default), you
should backup the ``jupyterhub.sqlite`` file.
#. Your ``jupyterhub_config.py`` file.
#. Your users' home directories. This is unlikely to be affected directly by
a JupyterHub upgrade, but we recommend a backup since user data is critical.
#. Your user's home directories. This is unlikely to be affected directly by
a JupyterHub upgrade, but we recommend a backup since user data is very
critical.
Shut down JupyterHub
====================
Shutdown JupyterHub
===================
Shut down the JupyterHub process. This would vary depending on how you
have set up JupyterHub to run. It is most likely using a process
Shutdown the JupyterHub process. This would vary depending on how you
have set up JupyterHub to run. Most likely, it is using a process
supervisor of some sort (``systemd`` or ``supervisord`` or even ``docker``).
Use the supervisor-specific command to stop the JupyterHub process.
Use the supervisor specific command to stop the JupyterHub process.
Upgrade JupyterHub packages
===========================
There are two environments where the ``jupyterhub`` package is installed:
#. The *hub environment*: where the JupyterHub server process
#. The *hub environment*, which is where the JupyterHub server process
runs. This is started with the ``jupyterhub`` command, and is what
people generally think of as JupyterHub.
#. The *notebook user environments*: where the user notebook
#. The *notebook user environments*. This is where the user notebook
servers are launched from, and is probably custom to your own
installation. This could be just one environment (different from the
hub environment) that is shared by all users, one environment
per user, or the same environment as the hub environment. The hub
per user, or same environment as the hub environment. The hub
launched the ``jupyterhub-singleuser`` command in this environment,
which in turn starts the notebook server.
@@ -92,8 +94,10 @@ with:
conda install -c conda-forge jupyterhub==<version>
Where ``<version>`` is the version of JupyterHub you are upgrading to.
You should also check for new releases of the authenticator & spawner you
are using. You might wish to upgrade those packages, too, along with JupyterHub
are using. You might wish to upgrade those packages too along with JupyterHub,
or upgrade them separately.
Upgrade JupyterHub database
@@ -107,7 +111,7 @@ database. From the hub environment, in the same directory as your
jupyterhub upgrade-db
This should find the location of your database, and run the necessary upgrades
This should find the location of your database, and run necessary upgrades
for it.
SQLite database disadvantages
@@ -116,11 +120,11 @@ SQLite database disadvantages
SQLite has some disadvantages when it comes to upgrading JupyterHub. These
are:
- ``upgrade-db`` may not work, and you may need to delete your database
- ``upgrade-db`` may not work, and you may need delete your database
and start with a fresh one.
- ``downgrade-db`` **will not** work if you want to rollback to an
earlier version, so backup the ``jupyterhub.sqlite`` file before
upgrading.
upgrading
What happens if I delete my database?
-------------------------------------
@@ -135,10 +139,10 @@ resides only in the Hub database includes:
If the following conditions are true, you should be fine clearing the
Hub database and starting over:
- users specified in the config file, or login using an external
- users specified in config file, or login using an external
authentication provider (Google, GitHub, LDAP, etc)
- user servers are stopped during the upgrade
- don't mind causing users to log in again after the upgrade
- user servers are stopped during upgrade
- don't mind causing users to login again after upgrade
Start JupyterHub
================
@@ -146,7 +150,7 @@ Start JupyterHub
Once the database upgrade is completed, start the ``jupyterhub``
process again.
#. Log in and start the server to make sure things work as
#. Log-in and start the server to make sure things work as
expected.
#. Check the logs for any errors or deprecation warnings. You
might have to update your ``jupyterhub_config.py`` file to

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) <https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/jupyterhub/jupyterhub/HEAD/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

@@ -1,70 +1,71 @@
# Configuration file for Sphinx to build our documentation to HTML.
# -*- coding: utf-8 -*-
#
# Configuration reference: https://www.sphinx-doc.org/en/master/usage/configuration.html
#
import contextlib
import datetime
import io
import os
import subprocess
import sys
from docutils import nodes
from sphinx.directives.other import SphinxDirective
# Set paths
sys.path.insert(0, os.path.abspath('.'))
# -- General configuration ------------------------------------------------
# Minimal Sphinx version
needs_sphinx = '1.4'
# Sphinx extension modules
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.intersphinx',
'sphinx.ext.napoleon',
'autodoc_traits',
'sphinx_copybutton',
'sphinx-jsonschema',
'recommonmark',
]
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'JupyterHub'
copyright = u'2016, Project Jupyter team'
author = u'Project Jupyter team'
# Autopopulate version
from os.path import dirname
docs = dirname(dirname(__file__))
root = dirname(docs)
sys.path.insert(0, root)
import jupyterhub
from jupyterhub.app import JupyterHub
# -- Project information -----------------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
#
project = "JupyterHub"
author = "Project Jupyter Contributors"
copyright = f"{datetime.date.today().year}, {author}"
version = "%i.%i" % jupyterhub.version_info[:2]
# The short X.Y version.
version = '%i.%i' % jupyterhub.version_info[:2]
# The full version, including alpha/beta/rc tags.
release = jupyterhub.__version__
language = None
exclude_patterns = []
pygments_style = 'sphinx'
todo_include_todos = False
# -- General Sphinx configuration --------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
#
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.intersphinx",
"sphinx.ext.napoleon",
"autodoc_traits",
"sphinx_copybutton",
"sphinx-jsonschema",
"sphinxext.opengraph",
"sphinxext.rediraffe",
"myst_parser",
]
root_doc = "index"
source_suffix = [".md", ".rst"]
# default_role let's use use `foo` instead of ``foo`` in rST
default_role = "literal"
# Set the default role so we can use `foo` instead of ``foo``
default_role = 'literal'
# -- Source -------------------------------------------------------------
# -- MyST configuration ------------------------------------------------------
# ref: https://myst-parser.readthedocs.io/en/latest/configuration.html
#
myst_heading_anchors = 2
myst_enable_extensions = [
"colon_fence",
"deflist",
]
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
# -- Custom directives to generate documentation -----------------------------
# ref: https://myst-parser.readthedocs.io/en/latest/syntax/roles-and-directives.html
#
# We define custom directives to help us generate documentation using Python on
# demand when referenced from our documentation files.
#
# Create a temp instance of JupyterHub for use by two separate directive classes
# to get the output from using the "--generate-config" and "--help-all" CLI
# flags respectively.
#
# create a temp instance of JupyterHub just to get the output of the generate-config
# and help --all commands.
jupyterhub_app = JupyterHub()
@@ -81,8 +82,8 @@ class ConfigDirective(SphinxDirective):
# The generated configuration file for this version
generated_config = jupyterhub_app.generate_config_file()
# post-process output
home_dir = os.environ["HOME"]
generated_config = generated_config.replace(home_dir, "$HOME", 1)
home_dir = os.environ['HOME']
generated_config = generated_config.replace(home_dir, '$HOME', 1)
par = nodes.literal_block(text=generated_config)
return [par]
@@ -98,118 +99,135 @@ class HelpAllDirective(SphinxDirective):
def run(self):
# The output of the help command for this version
buffer = io.StringIO()
with contextlib.redirect_stdout(buffer):
jupyterhub_app.print_help("--help-all")
buffer = StringIO()
with redirect_stdout(buffer):
jupyterhub_app.print_help('--help-all')
all_help = buffer.getvalue()
# post-process output
home_dir = os.environ["HOME"]
all_help = all_help.replace(home_dir, "$HOME", 1)
home_dir = os.environ['HOME']
all_help = all_help.replace(home_dir, '$HOME', 1)
par = nodes.literal_block(text=all_help)
return [par]
def setup(app):
app.add_css_file("custom.css")
app.add_directive("jupyterhub-generate-config", ConfigDirective)
app.add_directive("jupyterhub-help-all", HelpAllDirective)
app.add_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)
# -- Read The Docs -----------------------------------------------------------
#
# Since RTD runs sphinx-build directly without running "make html", we run the
# pre-requisite steps for "make html" from here if needed.
#
if os.environ.get("READTHEDOCS"):
docs = os.path.dirname(os.path.dirname(__file__))
subprocess.check_call(["make", "metrics", "scopes"], cwd=docs)
source_suffix = ['.rst', '.md']
# source_encoding = 'utf-8-sig'
# -- Options for HTML output ----------------------------------------------
# The theme to use for HTML and HTML Help pages.
html_theme = 'pydata_sphinx_theme'
html_logo = '_static/images/logo/logo.png'
html_favicon = '_static/images/logo/favicon.ico'
# Paths that contain custom static files (such as style sheets)
html_static_path = ['_static']
htmlhelp_basename = 'JupyterHubdoc'
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# 'papersize': 'letterpaper',
# 'pointsize': '10pt',
# 'preamble': '',
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(
master_doc,
'JupyterHub.tex',
u'JupyterHub Documentation',
u'Project Jupyter team',
'manual',
)
]
# latex_logo = None
# latex_use_parts = False
# latex_show_pagerefs = False
# latex_show_urls = False
# latex_appendices = []
# latex_domain_indices = True
# -- Spell checking ----------------------------------------------------------
# ref: https://sphinxcontrib-spelling.readthedocs.io/en/latest/customize.html#configuration-options
#
# The "sphinxcontrib.spelling" extension is optionally enabled if its available.
#
# -- manual page output -------------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [(master_doc, 'jupyterhub', u'JupyterHub Documentation', [author], 1)]
# man_show_urls = False
# -- Texinfo output -----------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(
master_doc,
'JupyterHub',
u'JupyterHub Documentation',
author,
'JupyterHub',
'One line description of project.',
'Miscellaneous',
)
]
# texinfo_appendices = []
# texinfo_domain_indices = True
# texinfo_show_urls = 'footnote'
# texinfo_no_detailmenu = False
# -- Epub output --------------------------------------------------------
# Bibliographic Dublin Core info.
epub_title = project
epub_author = author
epub_publisher = author
epub_copyright = copyright
# A list of files that should not be packed into the epub file.
epub_exclude_files = ['search.html']
# -- Intersphinx ----------------------------------------------------------
intersphinx_mapping = {'https://docs.python.org/3/': None}
# -- Read The Docs --------------------------------------------------------
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
if on_rtd:
# readthedocs.org uses their theme by default, so no need to specify it
# build both metrics and rest-api, since RTD doesn't run make
from subprocess import check_call as sh
sh(['make', 'metrics', 'rest-api'], cwd=docs)
# -- Spell checking -------------------------------------------------------
try:
import sphinxcontrib.spelling # noqa
import sphinxcontrib.spelling
except ImportError:
pass
else:
extensions.append("sphinxcontrib.spelling")
spelling_word_list_filename = "spelling_wordlist.txt"
# -- Options for HTML output -------------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
#
html_logo = "_static/images/logo/logo.png"
html_favicon = "_static/images/logo/favicon.ico"
html_static_path = ["_static"]
html_theme = "pydata_sphinx_theme"
html_theme_options = {
"icon_links": [
{
"name": "GitHub",
"url": "https://github.com/jupyterhub/jupyterhub",
"icon": "fab fa-github-square",
},
{
"name": "Discourse",
"url": "https://discourse.jupyter.org/c/jupyterhub/10",
"icon": "fab fa-discourse",
},
],
"use_edit_page_button": True,
"navbar_align": "left",
}
html_context = {
"github_user": "jupyterhub",
"github_repo": "jupyterhub",
"github_version": "main",
"doc_path": "docs/source",
}
# -- Options for linkcheck builder -------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-the-linkcheck-builder
#
linkcheck_ignore = [
r"(.*)github\.com(.*)#", # javascript based anchors
r"(.*)/#%21(.*)/(.*)", # /#!forum/jupyter - encoded anchor edge case
r"https://github.com/[^/]*$", # too many github usernames / searches in changelog
"https://github.com/jupyterhub/jupyterhub/pull/", # too many PRs in changelog
"https://github.com/jupyterhub/jupyterhub/compare/", # too many comparisons in changelog
]
linkcheck_anchors_ignore = [
"/#!",
"/#%21",
]
# -- Intersphinx -------------------------------------------------------------
# ref: https://www.sphinx-doc.org/en/master/usage/extensions/intersphinx.html#configuration
#
intersphinx_mapping = {
"python": ("https://docs.python.org/3/", None),
"tornado": ("https://www.tornadoweb.org/en/stable/", None),
}
# -- Options for the opengraph extension -------------------------------------
# ref: https://github.com/wpilibsuite/sphinxext-opengraph#options
#
# ogp_site_url is set automatically by RTD
ogp_image = "_static/logo.png"
ogp_use_first_image = True
# -- Options for the rediraffe extension -------------------------------------
# ref: https://github.com/wpilibsuite/sphinxext-rediraffe#readme
#
# This extensions help us relocated content without breaking links. If a
# document is moved internally, a redirect like should be configured below to
# help us not break links.
#
rediraffe_branch = "main"
rediraffe_redirects = {
# "old-file": "new-folder/new-file-name",
}
spelling_word_list_filename = 'spelling_wordlist.txt'

View File

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

View File

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

View File

@@ -5,7 +5,7 @@ Contributing Documentation
==========================
Documentation is often more important than code. This page helps
you get set up on how to contribute to JupyterHub's documentation.
you get set up on how to contribute documentation to JupyterHub.
Building documentation locally
==============================
@@ -18,7 +18,7 @@ stored under the ``docs/source`` directory) and converts it into various
formats for people to read. To make sure the documentation you write or
change renders correctly, it is good practice to test it locally.
#. Make sure you have successfully completed :ref:`contributing/setup`.
#. Make sure you have successfuly completed :ref:`contributing/setup`.
#. Install the packages required to build the docs.
@@ -27,7 +27,7 @@ change renders correctly, it is good practice to test it locally.
python3 -m pip install -r docs/requirements.txt
#. Build the html version of the docs. This is the most commonly used
output format, so verifying it renders correctly is usually good
output format, so verifying it renders as you should is usually good
enough.
.. code-block:: bash
@@ -44,14 +44,8 @@ change renders correctly, it is good practice to test it locally.
.. tip::
**On Windows**, you can open a file from the terminal with ``start <path-to-file>``.
**On macOS**, you can do the same with ``open <path-to-file>``.
**On Linux**, you can do the same with ``xdg-open <path-to-file>``.
After opening index.html in your browser you can just refresh the page whenever
you rebuild the docs via ``make html``
On macOS, you can open a file from the terminal with ``open <path-to-file>``.
On Linux, you can do the same with ``xdg-open <path-to-file>``.
.. _contributing/docs/conventions:

View File

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

View File

@@ -7,7 +7,7 @@ Setting up a development install
System requirements
===================
JupyterHub can only run on macOS or Linux operating systems. If you are
JupyterHub can only run on MacOS or Linux operating systems. If you are
using Windows, we recommend using `VirtualBox <https://virtualbox.org>`_
or a similar system to run `Ubuntu Linux <https://ubuntu.com>`_ for
development.
@@ -15,27 +15,25 @@ development.
Install Python
--------------
JupyterHub is written in the `Python <https://python.org>`_ programming language and
requires you have at least version 3.6 installed locally. If you havent
JupyterHub is written in the `Python <https://python.org>`_ programming language, and
requires you have at least version 3.5 installed locally. If you havent
installed Python before, the recommended way to install it is to use
`Miniconda <https://conda.io/miniconda.html>`_. Remember to get the Python 3 version,
`miniconda <https://conda.io/miniconda.html>`_. Remember to get the Python 3 version,
and **not** the Python 2 version!
Install nodejs
--------------
`NodeJS 12+ <https://nodejs.org/en/>`_ is required for building some JavaScript components.
``configurable-http-proxy``, the default proxy implementation for JupyterHub, is written in Javascript.
If you have not installed NodeJS before, we recommend installing it in the ``miniconda`` environment you set up for Python.
You can do so with ``conda install nodejs``.
Many in the Jupyter community use [``nvm``](https://github.com/nvm-sh/nvm) to
managing node dependencies.
``configurable-http-proxy``, the default proxy implementation for
JupyterHub, is written in Javascript to run on `NodeJS
<https://nodejs.org/en/>`_. If you have not installed nodejs before, we
recommend installing it in the ``miniconda`` environment you set up for
Python. You can do so with ``conda install nodejs``.
Install git
-----------
JupyterHub uses `Git <https://git-scm.com>`_ & `GitHub <https://github.com>`_
JupyterHub uses `git <https://git-scm.com>`_ & `GitHub <https://github.com>`_
for development & collaboration. You need to `install git
<https://git-scm.com/book/en/v2/Getting-Started-Installing-Git>`_ to work on
JupyterHub. We also recommend getting a free account on GitHub.com.
@@ -43,11 +41,13 @@ JupyterHub. We also recommend getting a free account on GitHub.com.
Setting up a development install
================================
When developing JupyterHub, you would need to make changes and be able to instantly view the results of the changes. To achieve that, a developer install is required.
When developing JupyterHub, you need to make changes to the code & see
their effects quickly. You need to do a developer install to make that
happen.
.. note:: This guide does not attempt to dictate *how* development
environments should be isolated since that is a personal preference and can
be achieved in many ways, for example, `tox`, `conda`, `docker`, etc. See this
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.
@@ -66,7 +66,7 @@ When developing JupyterHub, you would need to make changes and be able to instan
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
@@ -74,51 +74,53 @@ When developing JupyterHub, you would need to make changes and be able to instan
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``.
``sudo`` may be required to perform a system-wide install.
If you do not have access to sudo, you may instead run the following commands:
If you get an error that says ``Error: EACCES: permission denied``,
you might need to prefix the command with ``sudo``. If you do not
have access to sudo, you may instead run the following commands:
.. code:: bash
npm install configurable-http-proxy yarn
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:
4. Install the python packages required for JupyterHub development.
.. code:: bash
conda install configurable-http-proxy yarn
python3 -m pip install -r dev-requirements.txt
python3 -m pip install -r requirements.txt
4. Install an editable version of JupyterHub and its requirements for
development and testing. This lets you edit JupyterHub code in a text editor
& restart the JupyterHub process to see your code changes immediately.
.. code:: bash
python3 -m pip install --editable ".[test]"
5. Set up a database.
5. Setup a database.
The default database engine is ``sqlite`` so if you are just trying
to get up and running quickly for local development that should be
available via `Python <https://docs.python.org/3.5/library/sqlite3.html>`__.
available via `python <https://docs.python.org/3.5/library/sqlite3.html>`__.
See :doc:`/reference/database` for details on other supported databases.
6. You are now ready to start JupyterHub!
6. Install the development version of JupyterHub. This lets you edit
JupyterHub code in a text editor & restart the JupyterHub process to
see your code changes immediately.
.. code:: bash
python3 -m pip install --editable .
7. You are now ready to start JupyterHub!
.. code:: bash
jupyterhub
7. You can access JupyterHub from your browser at
8. You can access JupyterHub from your browser at
``http://localhost:8000`` now.
Happy developing!
@@ -126,12 +128,12 @@ Happy developing!
Using DummyAuthenticator & SimpleLocalProcessSpawner
====================================================
To simplify testing of JupyterHub, it is helpful to use
To simplify testing of JupyterHub, its helpful to use
:class:`~jupyterhub.auth.DummyAuthenticator` instead of the default JupyterHub
authenticator and SimpleLocalProcessSpawner instead of the default spawner.
There is a sample configuration file that does this in
``testing/jupyterhub_config.py``. To launch JupyterHub with this
``testing/jupyterhub_config.py``. To launch jupyterhub with this
configuration:
.. code:: bash
@@ -147,14 +149,14 @@ JupyterHub as.
DummyAuthenticator allows you to log in with any username & password,
while SimpleLocalProcessSpawner allows you to start servers without having to
create a Unix user for each JupyterHub user. Together, these make it
create a unix user for each JupyterHub user. Together, these make it
much easier to test JupyterHub.
Tip: If you are working on parts of JupyterHub that are common to all
authenticators & spawners, we recommend using both DummyAuthenticator &
SimpleLocalProcessSpawner. If you are working on just authenticator-related
SimpleLocalProcessSpawner. If you are working on just authenticator related
parts, use only SimpleLocalProcessSpawner. Similarly, if you are working on
just spawner-related parts, use only DummyAuthenticator.
just spawner related parts, use only DummyAuthenticator.
Troubleshooting
===============
@@ -184,4 +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

@@ -1,24 +1,25 @@
.. _contributing/tests:
===================================
Testing JupyterHub and linting code
===================================
==================
Testing JupyterHub
==================
Unit testing helps to validate that JupyterHub works the way we think it does,
Unit test help validate that JupyterHub works the way we think it does,
and continues to do so when changes occur. They also help communicate
precisely what we expect our code to do.
JupyterHub uses `pytest <https://pytest.org>`_ for all the tests. You
can find them under the `jupyterhub/tests <https://github.com/jupyterhub/jupyterhub/tree/main/jupyterhub/tests>`_ directory in the git repository.
JupyterHub uses `pytest <https://pytest.org>`_ for all our tests. You
can find them under ``jupyterhub/tests`` directory in the git repository.
Running the tests
==================
#. Make sure you have completed :ref:`contributing/setup`.
Once you are done, you would be able to run ``jupyterhub`` from the command line and access it from your web browser.
This ensures that the dev environment is properly set up for tests to run.
#. Make sure you have completed :ref:`contributing/setup`. You should be able
to start ``jupyterhub`` from the commandline & access it from your
web browser. This ensures that the dev environment is properly set
up for tests to run.
#. You can run all tests in JupyterHub
#. You can run all tests in JupyterHub
.. code-block:: bash
@@ -26,7 +27,7 @@ Running the tests
This should display progress as it runs all the tests, printing
information about any test failures as they occur.
If you wish to confirm test coverage the run tests with the `--cov` flag:
.. code-block:: bash
@@ -53,51 +54,9 @@ Running the tests
you would run:
.. code-block:: bash
pytest -v jupyterhub/tests/test_api.py::test_shutdown
For more details, refer to the `pytest usage documentation <https://pytest.readthedocs.io/en/latest/usage.html>`_.
Test organisation
=================
The tests live in ``jupyterhub/tests`` and are organized roughly into:
#. ``test_api.py`` tests the REST API
#. ``test_pages.py`` tests loading the HTML pages
and other collections of tests for different components.
When writing a new test, there should usually be a test of
similar functionality already written and related tests should
be added nearby.
The fixtures live in ``jupyterhub/tests/conftest.py``. There are
fixtures that can be used for JupyterHub components, such as:
- ``app``: an instance of JupyterHub with mocked parts
- ``auth_state_enabled``: enables persisting auth_state (like authentication tokens)
- ``db``: a sqlite in-memory DB session
- ``io_loop```: a Tornado event loop
- ``event_loop``: a new asyncio event loop
- ``user``: creates a new temporary user
- ``admin_user``: creates a new temporary admin user
- single user servers
- ``cleanup_after``: allows cleanup of single user servers between tests
- mocked service
- ``MockServiceSpawner``: a spawner that mocks services for testing with a short poll interval
- ``mockservice```: mocked service with no external service url
- ``mockservice_url``: mocked service with a url to test external services
And fixtures to add functionality or spawning behavior:
- ``admin_access``: grants admin access
- ``no_patience```: sets slow-spawning timeouts to zero
- ``slow_spawn``: enables the SlowSpawner (a spawner that takes a few seconds to start)
- ``never_spawn``: enables the NeverSpawner (a spawner that will never start)
- ``bad_spawn``: enables the BadSpawner (a spawner that fails immediately)
- ``slow_bad_spawn``: enables the SlowBadSpawner (a spawner that fails after a short delay)
Refer to the `pytest fixtures documentation <https://pytest.readthedocs.io/en/latest/fixture.html>`_ to learn how to use fixtures that exists already and to create new ones.
Troubleshooting Test Failures
=============================
@@ -105,34 +64,5 @@ Troubleshooting Test Failures
All the tests are failing
-------------------------
Make sure you have completed all the steps in :ref:`contributing/setup` successfully, and are able to access JupyterHub from your browser at http://localhost:8000 after starting ``jupyterhub`` in your command line.
Code formatting and linting
===========================
JupyterHub automatically enforces code formatting. This means that pull requests
with changes breaking this formatting will receive a commit from pre-commit.ci
automatically.
To automatically format code locally, you can install pre-commit and register a
*git hook* to automatically check with pre-commit before you make a commit if
the formatting is okay.
.. code:: bash
pip install pre-commit
pre-commit install --install-hooks
To run pre-commit manually you would do:
.. code:: bash
# check for changes to code not yet committed
pre-commit run
# check for changes also in already committed code
pre-commit run --all-files
You may also install `black integration <https://github.com/psf/black#editor-integration>`_
into your text editor to format code automatically.
Make sure you have completed all the steps in :ref:`contributing/setup` successfully, and
can launch ``jupyterhub`` from the terminal.

View File

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

View File

@@ -1,5 +1,5 @@
Event logging and telemetry
===========================
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_.
@@ -15,7 +15,7 @@ Event logging is handled by its ``Eventlog`` object. This leverages Python's sta
To begin recording events, you'll need to set two configurations:
1. ``handlers``: tells the EventLog *where* to route your events. This trait is a list of Python logging handlers that route events to the event log file.
1. ``handlers``: tells the EventLog *where* to route your events. This trait is a list of Python logging handlers that route events to
2. ``allows_schemas``: tells the EventLog *which* events should be recorded. No events are emitted by default; all recorded events must be listed here.
Here's a basic example:

View File

@@ -61,13 +61,6 @@ easy to do with RStudio too.
- [jupyterhub-deploy-teaching](https://github.com/jupyterhub/jupyterhub-deploy-teaching) based on work by Brian Granger for Cal Poly's Data Science 301 Course
### 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
@@ -97,7 +90,7 @@ easy to do with RStudio too.
### University of Illinois
- https://datascience.business.illinois.edu (currently down; checked 10/26/22)
- https://datascience.business.illinois.edu (currently down; checked 04/26/19)
### IllustrisTNG Simulation Project
@@ -126,7 +119,7 @@ easy to do with RStudio too.
### Penn State University
- [Press release](https://news.psu.edu/story/523093/2018/05/24/new-open-source-web-apps-available-students-and-faculty): "New open-source web apps available for students and faculty"
- [Press release](https://news.psu.edu/story/523093/2018/05/24/new-open-source-web-apps-available-students-and-faculty): "New open-source web apps available for students and faculty" (but Hub is currently down; checked 04/26/19)
### University of Rochester CIRC
@@ -156,13 +149,13 @@ easy to do with RStudio too.
### Elucidata
- What's new in Jupyter Notebooks @[Elucidata](https://elucidata.io/):
- [Using Jupyter Notebooks with Jupyterhub on GCP, managed by GKE](https://medium.com/elucidata/why-you-should-be-using-a-jupyter-notebook-8385a4ccd93d)
- Using Jupyter Notebooks with Jupyterhub on GCP, managed by GKE - https://medium.com/elucidata/why-you-should-be-using-a-jupyter-notebook-8385a4ccd93d
## Service Providers
### AWS
- [Run Jupyter Notebook and JupyterHub on Amazon EMR](https://aws.amazon.com/blogs/big-data/running-jupyter-notebook-and-jupyterhub-on-amazon-emr/)
- [running-jupyter-notebook-and-jupyterhub-on-amazon-emr](https://aws.amazon.com/blogs/big-data/running-jupyter-notebook-and-jupyterhub-on-amazon-emr/)
### Google Cloud Platform
@@ -175,12 +168,12 @@ easy to do with RStudio too.
### Microsoft Azure
- [Azure Data Science Virtual Machine release notes](https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-linux-dsvm-intro)
- https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-linux-dsvm-intro
### Rackspace Carina
- https://getcarina.com/blog/learning-how-to-whale/
- http://carolynvanslyck.com/talk/carina/jupyterhub/#/ (but carolynvanslyck is currently down; checked 10/26/22)
- http://carolynvanslyck.com/talk/carina/jupyterhub/#/
### Hadoop
@@ -189,14 +182,13 @@ easy to do with RStudio too.
## Miscellaneous
- https://medium.com/@ybarraud/setting-up-jupyterhub-with-sudospawner-and-anaconda-844628c0dbee#.rm3yt87e1
- [Mailing list UT deployment](https://groups.google.com/forum/#!topic/jupyter/nkPSEeMr8c0)
- [JupyterHub setup on Centos](https://gist.github.com/johnrc/604971f7d41ebf12370bf5729bf3e0a4)
- [Deploy JupyterHub to Docker Swarm](https://jupyterhub.surge.sh/#/welcome)
- https://groups.google.com/forum/#!topic/jupyter/nkPSEeMr8c0 Mailing list UT deployment
- JupyterHub setup on Centos https://gist.github.com/johnrc/604971f7d41ebf12370bf5729bf3e0a4
- Deploy JupyterHub to Docker Swarm https://jupyterhub.surge.sh/#/welcome
- http://www.laketide.com/building-your-lab-part-3/
- http://estrellita.hatenablog.com/entry/2015/07/31/083202
- http://www.walkingrandomly.com/?p=5734
- https://wrdrd.com/docs/consulting/education-technology
- https://bitbucket.org/jackhale/fenics-jupyter
- [LinuxCluster blog](https://linuxcluster.wordpress.com/category/application/jupyterhub/)
- [Network Technology](https://arnesund.com/tag/jupyterhub/)
- [Spark Cluster on OpenStack with Multi-User Jupyter Notebook](https://arnesund.com/2015/09/21/spark-cluster-on-openstack-with-multi-user-jupyter-notebook/)
- [Network Technology](https://arnesund.com/tag/jupyterhub/) [Spark Cluster on OpenStack with Multi-User Jupyter Notebook](https://arnesund.com/2015/09/21/spark-cluster-on-openstack-with-multi-user-jupyter-notebook/)

View File

@@ -1,10 +1,10 @@
# Authentication and User Basics
The default Authenticator uses [PAM][] (Pluggable Authentication Module) to authenticate system users with
The default Authenticator uses [PAM][] to authenticate system users with
their username and password. With the default Authenticator, any user
with an account and password on the system will be allowed to login.
## Create a set of allowed users (`allowed_users`)
## Create a set of allowed users
You can restrict which users are allowed to login with a set,
`Authenticator.allowed_users`:
@@ -16,19 +16,8 @@ c.Authenticator.allowed_users = {'mal', 'zoe', 'inara', 'kaylee'}
Users in the `allowed_users` set are added to the Hub database when the Hub is
started.
```{warning}
If this configuration value is not set, then **all authenticated users will be allowed into your hub**.
```
## Configure admins (`admin_users`)
```{note}
As of JupyterHub 2.0, the full permissions of `admin_users`
should not be required.
Instead, you can assign [roles](define-role-target) to users or groups
with only the scopes they require.
```
Admin users of JupyterHub, `admin_users`, can add and remove users from
the user `allowed_users` set. `admin_users` can take actions on other users'
behalf, such as stopping and restarting their servers.
@@ -42,10 +31,10 @@ c.Authenticator.admin_users = {'mal', 'zoe'}
Users in the admin set are automatically added to the user `allowed_users` set,
if they are not already present.
Each Authenticator may have different ways of determining whether a user is an
administrator. By default, JupyterHub uses the PAMAuthenticator which provides the
Each authenticator may have different ways of determining whether a user is an
administrator. By default JupyterHub uses the PAMAuthenticator which provides the
`admin_groups` option and can set administrator status based on a user
group. For example, we can let any user in the `wheel` group be an admin:
group. For example we can let any user in the `wheel` group be admin:
```python
c.PAMAuthenticator.admin_groups = {'wheel'}
@@ -57,12 +46,12 @@ Since the default `JupyterHub.admin_access` setting is `False`, the admins
do not have permission to log in to the single user notebook servers
owned by _other users_. If `JupyterHub.admin_access` is set to `True`,
then admins have permission to log in _as other users_ on their
respective machines for debugging. **As a courtesy, you should make
respective machines, for debugging. **As a courtesy, you should make
sure your users know if admin_access is enabled.**
## Add or remove users from the Hub
Users can be added to and removed from the Hub via the admin
Users can be added to and removed from the Hub via either the admin
panel or the REST API. When a user is **added**, the user will be
automatically added to the `allowed_users` set and database. Restarting the Hub
will not require manually updating the `allowed_users` set in your config file,
@@ -76,12 +65,12 @@ fresh.
## Use LocalAuthenticator to create system users
The `LocalAuthenticator` is a special kind of Authenticator that has
The `LocalAuthenticator` is a special kind of authenticator that has
the ability to manage users on the local system. When you try to add a
new user to the Hub, a `LocalAuthenticator` will check if the user
already exists. If you set the configuration value, `create_system_users`,
to `True` in the configuration file, the `LocalAuthenticator` has
the ability to add users to the system. The setting in the config
the privileges to add users to the system. The setting in the config
file is:
```python
@@ -91,7 +80,7 @@ c.LocalAuthenticator.create_system_users = True
Adding a user to the Hub that doesn't already exist on the system will
result in the Hub creating that user via the system `adduser` command
line tool. This option is typically used on hosted deployments of
JupyterHub to avoid the need to manually create all your users before
JupyterHub, to avoid the need to manually create all your users before
launching the service. This approach is not recommended when running
JupyterHub in situations where JupyterHub users map directly onto the
system's UNIX users.
@@ -101,25 +90,25 @@ system's UNIX users.
JupyterHub's [OAuthenticator][] currently supports the following
popular services:
- [Auth0](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.auth0.html#module-oauthenticator.auth0)
- [Azure AD](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.azuread.html#module-oauthenticator.azuread)
- [Bitbucket](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.bitbucket.html#module-oauthenticator.bitbucket)
- [CILogon](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.cilogon.html#module-oauthenticator.cilogon)
- [GitHub](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.github.html#module-oauthenticator.github)
- [GitLab](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.gitlab.html#module-oauthenticator.gitlab)
- [Globus](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.globus.html#module-oauthenticator.globus)
- [Google](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.google.html#module-oauthenticator.google)
- [MediaWiki](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.mediawiki.html#module-oauthenticator.mediawiki)
- [Okpy](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.okpy.html#module-oauthenticator.okpy)
- [OpenShift](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.openshift.html#module-oauthenticator.openshift)
- Auth0
- Azure AD
- Bitbucket
- CILogon
- GitHub
- GitLab
- Globus
- Google
- MediaWiki
- Okpy
- OpenShift
A [generic implementation](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.generic.html#module-oauthenticator.generic), which you can use for OAuth authentication
A generic implementation, which you can use for OAuth authentication
with any provider, is also available.
## Use DummyAuthenticator for testing
The `DummyAuthenticator` is a simple Authenticator that
allows for any username or password unless a global password has been set. If
The `DummyAuthenticator` is a simple authenticator that
allows for any username/password unless a global password has been set. If
set, it will allow for any username as long as the correct password is provided.
To set a global password, add this to the config file:

View File

@@ -1,6 +1,6 @@
# Configuration Basics
This section contains basic information about configuring settings for a JupyterHub
The section contains basic information about configuring settings for a JupyterHub
deployment. The [Technical Reference](../reference/index)
documentation provides additional details.
@@ -49,7 +49,7 @@ that Jupyter uses.
## Configure using command line options
To display all command line options that are available for configuration run the following command:
To display all command line options that are available for configuration:
```bash
jupyterhub --help-all
@@ -77,11 +77,11 @@ jupyterhub --Spawner.notebook_dir='~/assignments'
## Configure for various deployment environments
The default authentication and process spawning mechanisms can be replaced, and
specific [authenticators](authenticators-users-basics) and
[spawners](spawners-basics) can be set in the configuration file.
specific [authenticators](./authenticators-users-basics) and
[spawners](./spawners-basics) can be set in the configuration file.
This enables JupyterHub to be used with a variety of authentication methods or
process control and deployment environments. [Some examples](../reference/config-examples),
meant as illustrations, are:
meant as illustration, are:
- Using GitHub OAuth instead of PAM with [OAuthenticator](https://github.com/jupyterhub/oauthenticator)
- Spawning single-user servers with Docker, using the [DockerSpawner](https://github.com/jupyterhub/dockerspawner)

View File

@@ -1,6 +1,6 @@
# 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`).
@@ -16,8 +16,7 @@ to come to _your server_ and look at the exact same file.
In most circumstances, this is forbidden by permissions because the person you share with does not have access to your server.
What actually happens when someone visits this URL will depend on whether your server is running and other factors.
**But what is our actual goal?**
But what is our actual goal?
A typical situation is that you have some shared or common filesystem,
such that the same path corresponds to the same document
(either the exact same document or another copy of it).

View File

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

View File

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

View File

@@ -5,17 +5,17 @@ Security settings
You should not run JupyterHub without SSL encryption on a public network.
Security is the most important aspect of configuring Jupyter.
Three (3) configuration settings are the main aspects of security configuration:
Security is the most important aspect of configuring Jupyter. Three
configuration settings are the main aspects of security configuration:
1. :ref:`SSL encryption <ssl-encryption>` (to enable HTTPS)
2. :ref:`Cookie secret <cookie-secret>` (a key for encrypting browser cookies)
3. Proxy :ref:`authentication token <authentication-token>` (used for the Hub and
other services to authenticate to the Proxy)
The Hub hashes all secrets (e.g. auth tokens) before storing them in its
The Hub hashes all secrets (e.g., auth tokens) before storing them in its
database. A loss of control over read-access to the database should have
minimal impact on your deployment. If your database has been compromised, it
minimal impact on your deployment; if your database has been compromised, it
is still a good idea to revoke existing tokens.
.. _ssl-encryption:
@@ -31,7 +31,7 @@ Using an SSL certificate
This will require you to obtain an official, trusted SSL certificate or create a
self-signed certificate. Once you have obtained and installed a key and
certificate, you need to specify their locations in the ``jupyterhub_config.py``
certificate you need to specify their locations in the ``jupyterhub_config.py``
configuration file as follows:
.. code-block:: python
@@ -72,13 +72,13 @@ would be the needed configuration:
If SSL termination happens outside of the Hub
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In certain cases, for example, if the hub is running behind a reverse proxy, and
In certain cases, for example if the hub is running behind a reverse proxy, and
`SSL termination is being provided by NGINX <https://www.nginx.com/resources/admin-guide/nginx-ssl-termination/>`_,
it is reasonable to run the hub without SSL.
To achieve this, remove ``c.JupyterHub.ssl_key`` and ``c.JupyterHub.ssl_cert``
from your configuration (setting them to ``None`` or an empty string does not
have the same effect, and will result in an error).
To achieve this, simply omit the configuration settings
``c.JupyterHub.ssl_key`` and ``c.JupyterHub.ssl_cert``
(setting them to ``None`` does not have the same effect, and is an error).
.. _authentication-token:
@@ -92,7 +92,7 @@ use an auth token.
The value of this token should be a random string (for example, generated by
``openssl rand -hex 32``). You can store it in the configuration file or an
environment variable.
environment variable
Generating and storing token in the configuration file
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -118,8 +118,8 @@ This environment variable needs to be visible to the Hub and Proxy.
Default if token is not set
~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you do not set the Proxy authentication token, the Hub will generate a random
key itself. This means that any time you restart the Hub, you **must also
If you don't set the Proxy authentication token, the Hub will generate a random
key itself, which means that any time you restart the Hub you **must also
restart the Proxy**. If the proxy is a subprocess of the Hub, this should happen
automatically (this is the default configuration).
@@ -128,7 +128,7 @@ automatically (this is the default configuration).
Cookie secret
-------------
The cookie secret is an encryption key, used to encrypt the browser cookies,
The cookie secret is an encryption key, used to encrypt the browser cookies
which are used for authentication. Three common methods are described for
generating and configuring the cookie secret.
@@ -136,8 +136,8 @@ Generating and storing as a cookie secret file
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The cookie secret should be 32 random bytes, encoded as hex, and is typically
stored in a ``jupyterhub_cookie_secret`` file. Below, is an example command to generate the
``jupyterhub_cookie_secret`` file:
stored in a ``jupyterhub_cookie_secret`` file. An example command to generate the
``jupyterhub_cookie_secret`` file is:
.. code-block:: bash
@@ -155,7 +155,7 @@ The location of the ``jupyterhub_cookie_secret`` file can be specified in the
If the cookie secret file doesn't exist when the Hub starts, a new cookie
secret is generated and stored in the file. The file must not be readable by
``group`` or ``other``, otherwise the server won't start. The recommended permissions
``group`` or ``other`` or the server won't start. The recommended permissions
for the cookie secret file are ``600`` (owner-only rw).
Generating and storing as an environment variable
@@ -176,13 +176,19 @@ the Hub starts.
Generating and storing as a binary string
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
You can also set the cookie secret, as a binary string,
in the configuration file (``jupyterhub_config.py``) itself:
You can also set the cookie secret in the configuration file
itself, ``jupyterhub_config.py``, as a binary string:
.. code-block:: python
c.JupyterHub.cookie_secret = bytes.fromhex('64 CHAR HEX STRING')
.. important::
If the cookie secret value changes for the Hub, all single-user notebook
servers must also be restarted.
.. _cookies:
Cookies used by JupyterHub authentication
@@ -198,7 +204,7 @@ jupyterhub-hub-login
~~~~~~~~~~~~~~~~~~~~
This is the login token used when visiting Hub-served pages that are
protected by authentication, such as the main home, the spawn form, etc.
protected by authentication such as the main home, the spawn form, etc.
If this cookie is set, then the user is logged in.
Resetting the Hub cookie secret effectively revokes this cookie.
@@ -209,7 +215,7 @@ jupyterhub-user-<username>
~~~~~~~~~~~~~~~~~~~~~~~~~~
This is the cookie used for authenticating with a single-user server.
It is set by the single-user server, after OAuth with the Hub.
It is set by the single-user server after OAuth with the Hub.
Effectively the same as ``jupyterhub-hub-login``, but for the
single-user server instead of the Hub. It contains an OAuth access token,
@@ -218,13 +224,14 @@ which is checked with the Hub to authenticate the browser.
Each OAuth access token is associated with a session id (see ``jupyterhub-session-id`` section
below).
To avoid hitting the Hub on every request, the authentication response is cached.
The cache key is comprised of both the token and session id, to avoid a stale cache.
To avoid hitting the Hub on every request, the authentication response
is cached. And to avoid a stale cache the cache key is comprised of both
the token and session id.
Resetting the Hub cookie secret effectively revokes this cookie.
This cookie is restricted to the path ``/user/<username>``,
to ensure that only the users server receives it.
This cookie is restricted to the path ``/user/<username>``, so that
only the users server receives it.
jupyterhub-session-id
~~~~~~~~~~~~~~~~~~~~~
@@ -232,9 +239,9 @@ jupyterhub-session-id
This is a random string, meaningless in itself, and the only cookie
shared by the Hub and single-user servers.
Its sole purpose is to coordinate the logout of the multiple OAuth cookies.
Its sole purpose is to coordinate logout of the multiple OAuth cookies.
This cookie is set to ``/`` so all endpoints can receive it, clear it, etc.
This cookie is set to ``/`` so all endpoints can receive it, or clear it, etc.
jupyterhub-user-<username>-oauth-state
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -244,7 +251,7 @@ It is only set while OAuth between the single-user server and the Hub
is processing.
If you use your browser development tools, you should see this cookie
for a very brief moment before you are logged in,
for a very brief moment before your are logged in,
with an expiration date shorter than ``jupyterhub-hub-login`` or
``jupyterhub-user-<username>``.

View File

@@ -24,7 +24,7 @@ Hub via the REST API.
## API Token basics
### Step 1: Generate an API token
### Create an API token
To run such an external service, an API token must be created and
provided to the service.
@@ -43,12 +43,12 @@ generating an API token is available from the JupyterHub user interface:
![API TOKEN success page](../images/token-request-success.png)
### Step 2: Pass environment variable with token to the Hub
### Pass environment variable with token to the Hub
In the case of `cull_idle_servers`, it is passed as the environment
variable called `JUPYTERHUB_API_TOKEN`.
### Step 3: Use API tokens for services and tasks that require external access
### Use API tokens for services and tasks that require external access
While API tokens are often associated with a specific user, API tokens
can be used by services that require external access for activities
@@ -62,12 +62,12 @@ c.JupyterHub.services = [
]
```
### Step 4: Restart JupyterHub
### Restart JupyterHub
Upon restarting JupyterHub, you should see a message like below in the
logs:
```none
```
Adding API token for <username>
```
@@ -78,55 +78,34 @@ single-user servers, and only cookies can be used for authentication.
0.8 supports using JupyterHub API tokens to authenticate to single-user
servers.
## How to configure the idle culler to run as a Hub-Managed Service
## Configure the idle culler to run as a Hub-Managed Service
### Step 1: Install the idle culler:
Install the idle culler:
```
pip install jupyterhub-idle-culler
```
### Step 2: In `jupyterhub_config.py`, add the following dictionary for the `idle-culler` Service to the `c.JupyterHub.services` list:
In `jupyterhub_config.py`, add the following dictionary for the
`idle-culler` Service to the `c.JupyterHub.services` list:
```python
c.JupyterHub.services = [
{
'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
```
## How to run `cull-idle` manually as a standalone script
## Run `cull-idle` manually as a standalone script
Now you can run your script by providing it
the API token and it will authenticate through the REST API to
@@ -135,8 +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

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

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@@ -9,7 +9,5 @@ well as other information relevant to running your own JupyterHub over time.
:maxdepth: 2
troubleshooting
admin/capacity-planning
admin/upgrading
admin/log-messages
changelog

View File

@@ -1,32 +1,32 @@
==========
JupyterHub
==========
`JupyterHub`_ is the best way to serve `Jupyter notebook`_ for multiple users.
Because JupyterHub manages a separate Jupyter environment for each user,
it can be used in a class of students, a corporate data science group, or a scientific
`JupyterHub`_ is the best way to serve `Jupyter notebook`_ for multiple users.
It can be used in a class of students, a corporate data science group or scientific
research group. It is a multi-user **Hub** that spawns, manages, and proxies multiple
instances of the single-user `Jupyter notebook`_ server.
JupyterHub offers distributions for different use cases. As of now, you can find two main cases:
To make life easier, JupyterHub has distributions. Be sure to
take a look at them before continuing with the configuration of the broad
original system of `JupyterHub`_. Today, you can find two main cases:
1. `The Littlest JupyterHub <https://github.com/jupyterhub/the-littlest-jupyterhub>`__ distribution is suitable if you need a small number of users (1-100) and a single server with a simple environment.
2. `Zero to JupyterHub with Kubernetes <https://github.com/jupyterhub/zero-to-jupyterhub-k8s>`__ allows you to deploy dynamic servers on the cloud if you need even more users.
1. If you need a simple case for a small amount of users (0-100) and single server
take a look at
`The Littlest JupyterHub <https://github.com/jupyterhub/the-littlest-jupyterhub>`__ distribution.
2. If you need to allow for even more users, a dynamic amount of servers can be used on a cloud,
take a look at the `Zero to JupyterHub with Kubernetes <https://github.com/jupyterhub/zero-to-jupyterhub-k8s>`__ .
JupyterHub can be used in a collaborative environment by both both small (0-100 users) and
large teams (more than 100 users) such as a class of students, corporate data science group
or scientific research group. It has distributions which are developed to serve the needs of
each of these teams respectively.
JupyterHub is made up of four subsystems:
Four subsystems make up JupyterHub:
* a **Hub** (tornado process) that is the heart of JupyterHub
* a **configurable http proxy** (node-http-proxy) that receives the requests from the client's browser
* multiple **single-user Jupyter notebook servers** (Python/IPython/tornado) that are monitored by Spawners
* an **authentication class** that manages how users can access the system
Additionally, optional configurations can be added through a `config.py` file and manage users
kernels on an admin panel. A simplification of the whole system is displayed in the figure below:
Besides these central pieces, you can add optional configurations through a `config.py` file and manage users kernels on an admin panel. A simplification of the whole system can be seen in the figure below:
.. image:: images/jhub-fluxogram.jpeg
:alt: JupyterHub subsystems
@@ -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>`_.
@@ -56,22 +56,17 @@ Contents
Distributions
-------------
A JupyterHub **distribution** is tailored
towards a particular set of use cases. These are generally easier
to set up than setting up JupyterHub from scratch, assuming they fit your use case.
A JupyterHub **distribution** is tailored towards a particular set of
use cases. These are generally easier to set up than setting up
JupyterHub from scratch, assuming they fit your use case.
Today, you can find two main use cases:
The two popular ones are:
1. If you need a simple case for a small amount of users (0-100) and single server
take a look at
`The Littlest JupyterHub <https://github.com/jupyterhub/the-littlest-jupyterhub>`__ distribution.
2. If you need to allow for a larger number of machines and users,
a dynamic amount of servers can be used on a cloud,
take a look at the `Zero to JupyterHub with Kubernetes <https://github.com/jupyterhub/zero-to-jupyterhub-k8s>`__ distribution.
This distribution runs JupyterHub on top of `Kubernetes <https://k8s.io>`_.
*It is important to evaluate these distributions before you can continue with the
configuration of JupyterHub*.
* `Zero to JupyterHub on Kubernetes <http://z2jh.jupyter.org>`_, for
running JupyterHub on top of `Kubernetes <https://k8s.io>`_. This
can scale to large number of machines & users.
* `The Littlest JupyterHub <http://tljh.jupyter.org>`_, for an easy
to set up & run JupyterHub supporting 1-100 users on a single machine.
Installation Guide
------------------
@@ -113,23 +108,16 @@ API Reference
api/index
RBAC Reference
--------------
.. toctree::
:maxdepth: 2
rbac/index
Contributing
------------
We welcome you to contribute to JupyterHub in ways that are most exciting
& useful to you. We value documentation, testing, bug reporting & code equally
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>`_ and `reporting guidelines <https://github.com/jupyter/governance/blob/HEAD/conduct/reporting_online.md>`_
help keep our community welcoming to as many people as possible.
Our `Code of Conduct <https://github.com/jupyter/governance/blob/HEAD/conduct/code_of_conduct.md>`_
(`reporting guidelines <https://github.com/jupyter/governance/blob/HEAD/conduct/reporting_online.md>`_)
helps keep our community welcoming to as many people as possible.
.. toctree::
:maxdepth: 2
@@ -153,9 +141,10 @@ Indices and tables
Questions? Suggestions?
=======================
All questions and suggestions are welcome. Please feel free to use our `Jupyter Discourse Forum <https://discourse.jupyter.org/>`_ to contact our team.
Looking forward to hearing from you!
- `Jupyter mailing list <https://groups.google.com/forum/#!forum/jupyter>`_
- `Jupyter website <https://jupyter.org>`_
.. _JupyterHub: https://github.com/jupyterhub/jupyterhub
.. _Jupyter notebook: https://jupyter-notebook.readthedocs.io/en/latest/
.. _REST API: https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/jupyterhub/jupyterhub/HEAD/docs/rest-api.yml#!/default

View File

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

View File

@@ -4,9 +4,9 @@
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
- a Linux/Unix based system
- [Python](https://www.python.org/downloads/) 3.5 or greater. An understanding
of using [`pip`](https://pip.pypa.io/en/stable/) or
[`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),
@@ -20,11 +20,11 @@ Before installing JupyterHub, you will need:
For example, install it on Linux (Debian/Ubuntu) using:
```
sudo apt-get install nodejs npm
sudo apt-get install npm nodejs-legacy
```
[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).
The `nodejs-legacy` package installs the `node` executable and is currently
required for npm to work on Debian/Ubuntu.
- A [pluggable authentication module (PAM)](https://en.wikipedia.org/wiki/Pluggable_authentication_module)
to use the [default Authenticator](./getting-started/authenticators-users-basics.md).
@@ -33,17 +33,11 @@ Before installing JupyterHub, you will need:
- 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'
@@ -80,7 +74,7 @@ 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

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()

View File

@@ -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
```

View File

@@ -1,159 +0,0 @@
# Roles
JupyterHub provides four (4) roles that are available by default:
```{admonition} **Default roles**
- `user` role provides a {ref}`default user scope <default-user-scope-target>` `self` that grants access to the user's own resources.
- `admin` role contains all available scopes and grants full rights to all actions. This role **cannot be edited**.
- `token` role provides a {ref}`default token scope <default-token-scope-target>` `inherit` that resolves to the same permissions as the owner of the token has.
- `server` role allows for posting activity of "itself" only.
**These roles cannot be deleted.**
```
We call these 'default' roles because they are available by default and have a default collection of scopes.
However, you can define the scopes associated with each role (excluding the admin role) to suit your needs,
as seen [below](overriding-default-roles).
The `user`, `admin`, and `token` roles, by default, all preserve the permissions prior to Role-based Access Control (RBAC).
Only the `server` role is changed from pre-2.0, to reduce its permissions to activity-only
instead of the default of a full access token.
Additional custom roles can also be defined (see {ref}`define-role-target`).
Roles can be assigned to the following entities:
- Users
- Services
- Groups
An entity can have zero, one, or multiple roles, and there are no restrictions on which roles can be assigned to which entity. Roles can be added to or removed from entities at any time.
**Users** \
When a new user gets created, they are assigned their default role, `user`. Additionally, if the user is created with admin privileges (via `c.Authenticator.admin_users` in `jupyterhub_config.py` or `admin: true` via API), they will be also granted `admin` role. If existing user's admin status changes via API or `jupyterhub_config.py`, their default role will be updated accordingly (after next startup for the latter).
**Services** \
Services do not have a default role. Services without roles have no access to the guarded API end-points. So, most services will require assignment of a role in order to function.
**Groups** \
A group does not require any role, and has no roles by default. If a user is a member of a group, they automatically inherit any of the group's permissions (see {ref}`resolving-roles-scopes-target` for more details). This is useful for assigning a set of common permissions to several users.
**Tokens** \
A 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.

View File

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

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

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(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
@@ -31,12 +31,13 @@ popular services:
- Okpy
- OpenShift
A [generic implementation](https://github.com/jupyterhub/oauthenticator/blob/master/oauthenticator/generic.py), which you can use for OAuth authentication with any provider, is also available.
A generic implementation, which you can use for OAuth authentication
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.
@@ -136,8 +137,8 @@ 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).
@@ -164,7 +165,7 @@ setup(
```
If you have added this metadata to your package,
admins can select your authenticator with the configuration:
users can select your authenticator with the configuration:
```python
c.JupyterHub.authenticator_class = 'myservice'
@@ -222,7 +223,7 @@ If there are multiple keys present, the **first** key is always used to persist
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
@@ -246,59 +247,10 @@ class MyAuthenticator(Authenticator):
spawner.environment['UPSTREAM_TOKEN'] = auth_state['upstream_token']
```
Note that environment variable names and values are always strings, so passing multiple values means setting multiple environment variables or serializing more complex data into a single variable, e.g. as a JSON string.
auth state can also be used to configure the spawner via _config_ without subclassing
by setting `c.Spawner.auth_state_hook`. This function will be called with `(spawner, auth_state)`,
only when auth_state is defined.
For example:
(for KubeSpawner)
```python
def auth_state_hook(spawner, auth_state):
spawner.volumes = auth_state['user_volumes']
spawner.mounts = auth_state['user_mounts']
c.Spawner.auth_state_hook = auth_state_hook
```
(authenticator-groups)=
## Authenticator-managed group membership
:::{versionadded} 2.2
:::
Some identity providers may have their own concept of group membership that you would like to preserve in JupyterHub.
This is now possible with `Authenticator.managed_groups`.
You can set the config:
```python
c.Authenticator.manage_groups = True
```
to enable this behavior.
The default is False for Authenticators that ship with JupyterHub,
but may be True for custom Authenticators.
Check your Authenticator's documentation for manage_groups support.
If True, {meth}`.Authenticator.authenticate` and {meth}`.Authenticator.refresh_user` may include a field `groups`
which is a list of group names the user should be a member of:
- Membership will be added for any group in the list
- Membership in any groups not in the list will be revoked
- Any groups not already present in the database will be created
- If `None` is returned, no changes are made to the user's group membership
If authenticator-managed groups are enabled,
all group-management via the API is disabled.
## pre_spawn_start and post_spawn_stop hooks
Authenticators use 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.
@@ -307,7 +259,10 @@ PAM session.
Beginning with version 0.8, JupyterHub is an OAuth provider.
[authenticator]: https://github.com/jupyterhub/jupyterhub/blob/HEAD/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

@@ -5,15 +5,15 @@ 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](https://oauthenticator.readthedocs.io/en/latest)) for login
- 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
for notebooks within other users' home directories
- You want the landing page for each user to be a `Welcome.ipynb` notebook in
their assignments directory
- All runtime files are put into `/srv/jupyterhub` and log files in `/var/log`
their assignments directory.
- All runtime files are put into `/srv/jupyterhub` and log files in `/var/log`.
The `jupyterhub_config.py` file would have these settings:
@@ -69,7 +69,7 @@ c.Spawner.args = ['--NotebookApp.default_url=/notebooks/Welcome.ipynb']
```
Using the GitHub Authenticator requires a few additional
environment variables to be set prior to launching JupyterHub:
environment variable to be set prior to launching JupyterHub:
```bash
export GITHUB_CLIENT_ID=github_id
@@ -79,5 +79,3 @@ export CONFIGPROXY_AUTH_TOKEN=super-secret
# append log output to log file /var/log/jupyterhub.log
jupyterhub -f /etc/jupyterhub/jupyterhub_config.py &>> /var/log/jupyterhub.log
```
Visit the [Github OAuthenticator reference](https://oauthenticator.readthedocs.io/en/latest/api/gen/oauthenticator.github.html) to see the full list of options for configuring Github OAuth with JupyterHub.

View File

@@ -14,7 +14,7 @@ satisfy the following:
- 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 the needed JupyterHub configuration in `jupyterhub_config.py`:
Let's start out with needed JupyterHub configuration in `jupyterhub_config.py`:
```python
# Force the proxy to only listen to connections to 127.0.0.1 (on port 8000)
@@ -30,15 +30,15 @@ This can take a few minutes:
openssl dhparam -out /etc/ssl/certs/dhparam.pem 4096
```
## Nginx
## nginx
This **`nginx` config file** is fairly standard fare except for the two
`location` blocks within the main section for HUB.DOMAIN.tld.
To create a new site for jupyterhub in your Nginx config, make a new file
To create a new site for jupyterhub in your nginx config, make a new file
in `sites.enabled`, e.g. `/etc/nginx/sites.enabled/jupyterhub.conf`:
```bash
# Top-level HTTP config for WebSocket headers
# top-level http config for websocket headers
# If Upgrade is defined, Connection = upgrade
# If Upgrade is empty, Connection = close
map $http_upgrade $connection_upgrade {
@@ -51,7 +51,7 @@ server {
listen 80;
server_name HUB.DOMAIN.TLD;
# Redirect the request to HTTPS
# Tell all requests to port 80 to be 302 redirected to HTTPS
return 302 https://$host$request_uri;
}
@@ -75,7 +75,7 @@ server {
ssl_stapling_verify on;
add_header Strict-Transport-Security max-age=15768000;
# Managing literal requests to the JupyterHub frontend
# Managing literal requests to the JupyterHub front end
location / {
proxy_pass http://127.0.0.1:8000;
proxy_set_header X-Real-IP $remote_addr;
@@ -101,10 +101,10 @@ server {
If `nginx` is not running on port 443, substitute `$http_host` for `$host` on
the lines setting the `Host` header.
`nginx` will now be the front-facing element of JupyterHub on `443` which means
`nginx` will now be the front facing element of JupyterHub on `443` which means
it is also free to bind other servers, like `NO_HUB.DOMAIN.TLD` to the same port
on the same machine and network interface. In fact, one can simply use the same
server blocks as above for `NO_HUB` and simply add a line for the root directory
server blocks as above for `NO_HUB` and simply add line for the root directory
of the site as well as the applicable location call:
```bash
@@ -112,7 +112,7 @@ server {
listen 80;
server_name NO_HUB.DOMAIN.TLD;
# Redirect the request to HTTPS
# Tell all requests to port 80 to be 302 redirected to HTTPS
return 302 https://$host$request_uri;
}
@@ -143,12 +143,12 @@ Now restart `nginx`, restart the JupyterHub, and enjoy accessing
`https://HUB.DOMAIN.TLD` while serving other content securely on
`https://NO_HUB.DOMAIN.TLD`.
### SELinux permissions for Nginx
### SELinux permissions for nginx
On distributions with SELinux enabled (e.g. Fedora), one may encounter permission errors
when the Nginx service is started.
when the nginx service is started.
We need to allow Nginx to perform network relay and connect to the JupyterHub port. The
We need to allow nginx to perform network relay and connect to the jupyterhub port. The
following commands do that:
```bash
@@ -157,26 +157,26 @@ 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.
Replace 8000 with the port the jupyterhub server is running from.
## Apache
As with Nginx above, you can use [Apache](https://httpd.apache.org) as the reverse proxy.
First, we will need to enable the Apache modules that we are going to need:
As with nginx above, you can use [Apache](https://httpd.apache.org) as the reverse proxy.
First, we will need to enable the apache modules that we are going to need:
```bash
a2enmod ssl rewrite proxy headers proxy_http proxy_wstunnel
a2enmod ssl rewrite proxy proxy_http proxy_wstunnel
```
Our Apache configuration is equivalent to the Nginx configuration above:
Our Apache configuration is equivalent to the nginx configuration above:
- Redirect HTTP to HTTPS
- Good SSL Configuration
- Support for WebSocket on any proxied URL
- Support for websockets on any proxied URL
- JupyterHub is running locally at http://127.0.0.1:8000
```bash
# Redirect HTTP to HTTPS
# redirect HTTP to HTTPS
Listen 80
<VirtualHost HUB.DOMAIN.TLD:80>
ServerName HUB.DOMAIN.TLD
@@ -188,26 +188,15 @@ 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
# configure SSL
SSLEngine on
SSLCertificateFile /etc/letsencrypt/live/HUB.DOMAIN.TLD/fullchain.pem
SSLCertificateKeyFile /etc/letsencrypt/live/HUB.DOMAIN.TLD/privkey.pem
SSLProtocol All -SSLv2 -SSLv3
SSLOpenSSLConfCmd DHParameters /etc/ssl/certs/dhparam.pem
SSLCipherSuite EECDH+AESGCM:EDH+AESGCM:AES256+EECDH:AES256+EDH
# Intermediate configuration from SSL-config.mozilla.org (2022-03-03)
# Please note, that this configuration might be outdated - please update it accordingly using https://ssl-config.mozilla.org/
SSLProtocol all -SSLv3 -TLSv1 -TLSv1.1
SSLCipherSuite ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES128-GCM-SHA256:ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-CHACHA20-POLY1305:ECDHE-RSA-CHACHA20-POLY1305:DHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384
SSLHonorCipherOrder off
SSLSessionTickets off
# Use RewriteEngine to handle WebSocket connection upgrades
# Use RewriteEngine to handle websocket connection upgrades
RewriteEngine On
RewriteCond %{HTTP:Connection} Upgrade [NC]
RewriteCond %{HTTP:Upgrade} websocket [NC]
@@ -219,20 +208,19 @@ 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 JupyterHub under /jhub/ or another location please use the below configurations:
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/
@@ -240,8 +228,8 @@ httpd.conf amendments:
jupyterhub_config.py amendments:
```python
# The public facing URL of the whole JupyterHub application.
# This is the address on which the proxy will bind. Sets protocol, IP, base_url
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

@@ -6,10 +6,10 @@ Only do this if you are very sure you must.
## Overview
There are many [Authenticators](../getting-started/authenticators-users-basics) and [Spawners](../getting-started/spawners-basics) available for JupyterHub. Some, such
as [DockerSpawner](https://github.com/jupyterhub/dockerspawner) or [OAuthenticator](https://github.com/jupyterhub/oauthenticator), do not need any elevated permissions. This
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
@@ -69,8 +69,7 @@ Cmnd_Alias JUPYTER_CMD = /usr/local/bin/sudospawner
rhea ALL=(JUPYTER_USERS) NOPASSWD:JUPYTER_CMD
```
It might be useful to modify `secure_path` to add commands in path. (Search for
`secure_path` in the [sudo docs](https://www.sudo.ws/man/1.8.14/sudoers.man.html)
It might be useful to modify `secure_path` to add commands in path.
As an alternative to adding every user to the `/etc/sudoers` file, you can
use a group in the last line above, instead of `JUPYTER_USERS`:
@@ -91,7 +90,7 @@ $ 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:
@@ -120,7 +119,7 @@ the shadow password database.
### Shadow group (Linux)
**Note:** On [Fedora based distributions](https://fedoraproject.org/wiki/List_of_Fedora_remixes) there is no clear way to configure
**Note:** On Fedora based distributions there is no clear way to configure
the PAM database to allow sufficient access for authenticating with the target user's password
from JupyterHub. As a workaround we recommend use an
[alternative authentication method](https://github.com/jupyterhub/jupyterhub/wiki/Authenticators).
@@ -151,7 +150,7 @@ 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
@@ -159,7 +158,6 @@ sudo setcap 'cap_net_bind_service=+ep' /usr/bin/node
```
However, you may want to further understand the consequences of this.
([Further reading](http://man7.org/linux/man-pages/man7/capabilities.7.html))
You may also be interested in limiting the amount of CPU any process can use
on your server. `cpulimit` is a useful tool that is available for many Linux
@@ -169,8 +167,7 @@ instructions](http://ubuntuforums.org/showthread.php?t=992706).
### Shadow group (FreeBSD)
**NOTE:** This has not been tested on FreeBSD and may not work as expected on
the FreeBSD platform. _Do not use in production without verifying that it works properly!_
**NOTE:** This has not been tested and may not work as expected.
```bash
$ ls -l /etc/spwd.db /etc/master.passwd
@@ -229,7 +226,7 @@ And try logging in.
## Troubleshooting: SELinux
If you still get a generic `Permission denied` `PermissionError`, it's possible SELinux is blocking you.
Here's how you can make a module to resolve this.
Here's how you can make a module to allow this.
First, put this in a file named `sudo_exec_selinux.te`:
```bash
@@ -256,6 +253,6 @@ $ semodule -i sudo_exec_selinux.pp
## Troubleshooting: PAM session errors
If the PAM authentication doesn't work and you see errors for
`login:session-auth`, or similar, consider updating to a more recent version
`login:session-auth`, or similar, considering updating to a more recent version
of jupyterhub and disabling the opening of PAM sessions with
`c.PAMAuthenticator.open_sessions=False`.

View File

@@ -1,47 +1,49 @@
# Configuring user environments
To deploy JupyterHub means you are providing Jupyter notebook environments for
Deploying JupyterHub means you are providing Jupyter notebook environments for
multiple users. Often, this includes a desire to configure the user
environment in a custom way.
environment in some way.
Since the `jupyterhub-singleuser` server extends the standard Jupyter notebook
server, most configuration and documentation that applies to Jupyter Notebook
applies to the single-user environments. Configuration of user environments
typically does not occur through JupyterHub itself, but rather through system-wide
configuration of Jupyter, which is inherited by `jupyterhub-singleuser`.
typically does not occur through JupyterHub itself, but rather through system-
wide configuration of Jupyter, which is inherited by `jupyterhub-singleuser`.
**Tip:** When searching for configuration tips for JupyterHub user environments, you might want to remove JupyterHub from your search because there are a lot more people out there configuring Jupyter than JupyterHub and the configuration is the same.
**Tip:** When searching for configuration tips for JupyterHub user
environments, try removing JupyterHub from your search because there are a lot
more people out there configuring Jupyter than JupyterHub and the
configuration is the same.
This section will focus on user environments, which includes the following:
This section will focus on user environments, including:
- [Installing packages](#installing-packages)
- [Configuring Jupyter and IPython](#configuring-jupyter-and-ipython)
- [Installing kernelspecs](#installing-kernelspecs)
- [Using containers vs. multi-user hosts](#multi-user-hosts-vs-containers)
- Installing packages
- Configuring Jupyter and IPython
- Installing kernelspecs
- Using containers vs. multi-user hosts
## Installing packages
To make packages available to users, you will typically install packages system-wide or in a shared environment.
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 where
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 your users to be able to install additional
packages, the installation location must also be _writable_ by your users.
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 Python installation on your system, use the following command:
If you are using a standard system Python install, you would use:
```bash
sudo python3 -m pip install numpy
```
to install the numpy package in the default Python 3 environment on your system
to install the numpy package in the default system Python 3 environment
(typically `/usr/local`).
You may also use conda to install packages. If you do, you should make sure
that the conda environment has appropriate permissions for users to be able to
run Python code in the env. The env must be _readable and executable_ by all
users. Additionally it must be _writeable_ if you want users to install
additional packages.
run Python code in the env.
## Configuring Jupyter and IPython
@@ -49,9 +51,15 @@ additional packages.
and [IPython](https://ipython.readthedocs.io/en/stable/development/config.html)
have their own configuration systems.
As a JupyterHub administrator, you will typically want to install and configure environments for all JupyterHub users. For example, let's say you wish for each student in a class to have the same user environment configuration.
As a JupyterHub administrator, you will typically want to install and configure
environments for all JupyterHub users. For example, you wish for each student in
a class to have the same user environment configuration.
Jupyter and IPython support **"system-wide"** locations for configuration, which
is the logical place to put global configuration that you want to affect all
users. It's generally more efficient to configure user environments "system-wide",
and it's a good idea to avoid creating files in users' home directories.
Jupyter and IPython support **"system-wide"** locations for configuration, which is the logical place to put global configuration that you want to affect all users. It's generally more efficient to configure user environments "system-wide", and it's a good practice to avoid creating files in the users' home directories.
The typical locations for these config files are:
- **system-wide** in `/etc/{jupyter|ipython}`
@@ -59,7 +67,8 @@ The typical locations for these config files are:
### Example: Enable an extension system-wide
For example, to enable the `cython` IPython extension for all of your users, create the file `/etc/ipython/ipython_config.py`:
For example, to enable the `cython` IPython extension for all of your users,
create the file `/etc/ipython/ipython_config.py`:
```python
c.InteractiveShellApp.extensions.append("cython")
@@ -67,23 +76,13 @@ c.InteractiveShellApp.extensions.append("cython")
### Example: Enable a Jupyter notebook configuration setting for all users
:::{note}
These examples configure the Jupyter ServerApp, which is used by JupyterLab, the default in JupyterHub 2.0.
If you are using the classing Jupyter Notebook server,
the same things should work,
with the following substitutions:
- Search for `jupyter_server_config`, and replace with `jupyter_notebook_config`
- Search for `NotebookApp`, and replace with `ServerApp`
:::
To enable Jupyter notebook's internal idle-shutdown behavior (requires notebook ≥ 5.4), set the following in the `/etc/jupyter/jupyter_server_config.py` file:
To enable Jupyter notebook's internal idle-shutdown behavior (requires
notebook ≥ 5.4), set the following in the `/etc/jupyter/jupyter_notebook_config.py`
file:
```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
@@ -92,14 +91,16 @@ c.MappingKernelManager.cull_interval = 2 * 60
## Installing kernelspecs
You may have multiple Jupyter kernels installed and want to make sure that they are available to all of your users. This means installing kernelspecs either system-wide (e.g. in /usr/local/) or in the `sys.prefix` of JupyterHub
You may have multiple Jupyter kernels installed and want to make sure that
they are available to all of your users. This means installing kernelspecs
either system-wide (e.g. in /usr/local/) or in the `sys.prefix` of JupyterHub
itself.
Jupyter kernelspec installation is system-wide by default, but some kernels
Jupyter kernelspec installation is system wide by default, but some kernels
may default to installing kernelspecs in your home directory. These will need
to be moved system-wide to ensure that they are accessible.
To see where your kernelspecs are, you can use the following command:
You can see where your kernelspecs are with:
```bash
jupyter kernelspec list
@@ -107,11 +108,12 @@ jupyter kernelspec list
### Example: Installing kernels system-wide
Let's assume that I have a Python 2 and Python 3 environment that I want to make sure are available, I can install their specs **system-wide** (in /usr/local) using the following command:
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
@@ -126,25 +128,31 @@ How you configure user environments for each category can differ a bit
depending on what Spawner you are using.
The first category is a **shared system (multi-user host)** where
each user has a JupyterHub account, a home directory as well as being
each user has a JupyterHub account and a home directory as well as being
a real system user. In this example, shared configuration and installation
must be in a 'system-wide' location, such as `/etc/`, or `/usr/local`
must be in a 'system-wide' location, such as `/etc/` or `/usr/local`
or a custom prefix such as `/opt/conda`.
When JupyterHub uses **container-based** Spawners (e.g. KubeSpawner or
DockerSpawner), the 'system-wide' environment is really the container image used for users.
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, home directories typically persist once they are created, thereby making it difficult for admins to update later.
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.
## Named servers
By default, in a JupyterHub deployment, each user has one server only.
By default, in a JupyterHub deployment each user has exactly one server.
JupyterHub can, however, have multiple servers per user.
This is mostly useful in deployments where users can configure the environment in which their server will start (e.g. resource requests on an HPC cluster), so that a given user can have multiple configurations running at the same time, without having to stop and restart their own server.
This is most useful in deployments where users can configure the environment
in which their server will start (e.g. resource requests on an HPC cluster),
so that a given user can have multiple configurations running at the same time,
without having to stop and restart their one server.
To allow named servers, include this code snippet in your config file:
To allow named servers:
```python
c.JupyterHub.allow_named_servers = True
@@ -160,66 +168,20 @@ as well as the admin page:
![named servers on the admin page](../images/named-servers-admin.png)
Named servers can be accessed, created, started, stopped, and deleted
from these pages. Activity tracking is now per server as well.
from these pages. Activity tracking is now per-server as well.
To limit the number of **named server** per user by setting a constant value, include this code snippet in your config file:
The number of named servers per user can be limited by setting
```python
c.JupyterHub.named_server_limit_per_user = 5
```
Alternatively, to use a callable/awaitable based on the handler object, include this code snippet in your config file:
## Switching to Jupyter Server
```python
def named_server_limit_per_user_fn(handler):
user = handler.current_user
if user and user.admin:
return 0
return 5
[Jupyter Server](https://jupyter-server.readthedocs.io/en/latest/) is a new Tornado Server backend for Jupyter web applications (e.g. JupyterLab 3.0 uses this package as its default backend).
c.JupyterHub.named_server_limit_per_user = named_server_limit_per_user_fn
```
This can be useful for quota service implementations. The example above limits the number of named servers for non-admin users only.
If `named_server_limit_per_user` is set to `0`, no limit is enforced.
(classic-notebook-ui)=
## Switching back to the classic notebook
By default, the single-user server launches JupyterLab,
which is based on [Jupyter Server][].
This is the default server when running JupyterHub ≥ 2.0.
To switch to using the legacy Jupyter Notebook server, you can set the `JUPYTERHUB_SINGLEUSER_APP` environment variable
(in the single-user environment) to:
```bash
export JUPYTERHUB_SINGLEUSER_APP='notebook.notebookapp.NotebookApp'
```
[jupyter server]: https://jupyter-server.readthedocs.io
[jupyter notebook]: https://jupyter-notebook.readthedocs.io
:::{versionchanged} 2.0
JupyterLab is now the default single-user UI, if available,
which is based on the [Jupyter Server][],
no longer the legacy [Jupyter Notebook][] server.
JupyterHub prior to 2.0 launched the legacy notebook server (`jupyter notebook`),
and the Jupyter server could be selected by specifying the following:
```python
# jupyterhub_config.py
c.Spawner.cmd = ["jupyter-labhub"]
```
Alternatively, for an otherwise customized Jupyter Server app,
set the environment variable using the following command:
By default, the single-user notebook server uses the (old) `NotebookApp` from the [notebook](https://github.com/jupyter/notebook) package. You can switch to using Jupyter Server's `ServerApp` backend (this will likely become the default in future releases) by setting the `JUPYTERHUB_SINGLEUSER_APP` environment variable to:
```bash
export JUPYTERHUB_SINGLEUSER_APP='jupyter_server.serverapp.ServerApp'
```
:::

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

View File

@@ -7,12 +7,9 @@ Hub manages by default as a subprocess (it can be run externally, as well, and
typically is in production deployments).
The upside to CHP, and why we use it by default, is that it's easy to install
and run (if you have nodejs, you are set!). The downsides are that
- it's a single process and
- does not support any persistence of the routing table.
So if the proxy process dies, your whole JupyterHub instance is inaccessible
and run (if you have nodejs, you are set!). The downsides are that it's a
single process and does not support any persistence of the routing table. So
if the proxy process dies, your whole JupyterHub instance is inaccessible
until the Hub notices, restarts the proxy, and restores the routing table. For
deployments that want to avoid such a single point of failure, or leverage
existing proxy infrastructure in their chosen deployment (such as Kubernetes
@@ -141,7 +138,7 @@ async def delete_route(self, routespec):
For retrieval, you only _need_ to implement a single method that retrieves all
routes. The return value for this function should be a dictionary, keyed by
`routespec`, of dicts whose keys are the same three arguments passed to
`routespect`, of dicts whose keys are the same three arguments passed to
`add_route` (`routespec`, `target`, `data`)
```python
@@ -207,7 +204,7 @@ setup(
```
If you have added this metadata to your package,
admins can select your authenticator with the configuration:
users can select your proxy with the configuration:
```python
c.JupyterHub.proxy_class = 'mything'
@@ -219,15 +216,7 @@ instead of the full
c.JupyterHub.proxy_class = 'mypackage:MyProxy'
```
as previously required.
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

@@ -1,39 +1,34 @@
(rest-api)=
# Using JupyterHub's REST API
This section will give you information on:
- What you can do with the API
- How to create an API token
- Assigning permissions to a token
- Updating to admin services
- Making an API request programmatically using the requests library
- Paginating API requests
- Enabling users to spawn multiple named-servers via the API
- Learn more about JupyterHub's API
Before we discuss about JupyterHub's REST API, you can learn about [REST APIs here](https://en.wikipedia.org/wiki/Representational_state_transfer). A REST
API provides a standard way for users to get and send information to the
Hub.
- what you can do with the API
- create an API token
- add API tokens to the config files
- make an API request programmatically using the requests library
- learn more about JupyterHub's API
## What you can do with the API
Using the [JupyterHub REST API][], you can perform actions on the Hub,
such as:
- Checking which users are active
- Adding or removing users
- Stopping or starting single user notebook servers
- Authenticating services
- Communicating with an individual Jupyter server's REST API
- checking which users are active
- adding or removing users
- stopping or starting single user notebook servers
- authenticating services
A [REST](https://en.wikipedia.org/wiki/Representational_state_transfer)
API provides a standard way for users to get and send information to the
Hub.
## Create an API token
To send requests using the JupyterHub API, you must pass an API token with
To send requests using JupyterHub API, you must pass an API token with
the request.
The preferred way of generating an API token is by running:
As of [version 0.6.0](../changelog.md), the preferred way of
generating an API token is:
```bash
openssl rand -hex 32
@@ -43,12 +38,8 @@ This `openssl` command generates a potential token that can then be
added to JupyterHub using `.api_tokens` configuration setting in
`jupyterhub_config.py`.
```{note}
The api_tokens configuration has been softly deprecated since the introduction of services.
```
Alternatively, you can use the `jupyterhub token` command to generate a token
for a specific hub user by passing the **username**:
Alternatively, use the `jupyterhub token` command to generate a token
for a specific hub user by passing the 'username':
```bash
jupyterhub token <username>
@@ -57,92 +48,61 @@ 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:
:::{figure-md}
![Request API TOKEN page](../images/token-request.png)
![token request page](../images/token-request.png)
![API TOKEN success page](../images/token-request-success.png)
JupyterHub's API token page
:::
## Add API tokens to the config file
:::{figure-md}
![token-request-success](../images/token-request-success.png)
**This is deprecated. We are in no rush to remove this feature,
but please consider if service tokens are right for you.**
JupyterHub's token page after successfully requesting a token.
:::
## Assigning permissions to a token
Prior to JupyterHub 2.0, there were two levels of permissions:
1. user, and
2. admin
where a token would always have full permissions to do whatever its owner could do.
In JupyterHub 2.0,
specific permissions are now defined as '**scopes**',
and can be assigned both at the user/service level,
and at the individual token level.
This allows e.g. a user with full admin permissions to request a token with limited permissions.
## Updating to admin services
```{note}
The `api_tokens` configuration has been softly deprecated since the introduction of services.
We have no plans to remove it,
but deployments are encouraged to use service configuration instead.
```
If you have been using `api_tokens` to create an admin user
and the token for that user to perform some automations, then
the services' mechanism may be a better fit if you have the following configuration:
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'):
```python
c.JupyterHub.admin_users = {"service-admin"}
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 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,
the services mechanism may be a better fit.
If you have the following configuration:
```python
c.JupyterHub.admin_users = {"service-admin",}
c.JupyterHub.api_tokens = {
"secret-token": "service-admin",
}
```
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 were introduced in JupyterHub 2.0
# prior to 2.0, only "admin": True or False was available
c.JupyterHub.load_roles = [
{
"name": "service-role",
"scopes": [
# specify the permissions the token should have
"admin:users",
],
"services": [
# assign the service the above permissions
"service-admin",
],
}
]
```
The token will have the permissions listed in the role
(see [scopes][] for a list of available permissions),
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 between a user and a service is that there will be no notebook server associated with the account
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.
## Make an API request
@@ -152,9 +112,10 @@ Authorization header.
### Use requests
Using the popular Python [requests](https://docs.python-requests.org)
library, an API GET request is made, and the request sends an API token for
authorization. The response contains information about the users, here's example code to make an API request for the users of a JupyterHub deployment
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:
```python
import requests
@@ -163,9 +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()
@@ -183,100 +144,23 @@ 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 one of the following is true:
provided by notebook servers managed by JupyterHub if it has the necessary `access:servers` scope.
(api-pagination)=
## Paginating API requests
```{versionadded} 2.0
```
Pagination is available through the `offset` and `limit` query parameters on
list endpoints, which can be used to return ideally sized windows of results.
Here's example code demonstrating pagination on the `GET /users`
endpoint to fetch the first 20 records.
```python
import os
import requests
api_url = 'http://127.0.0.1:8081/hub/api'
r = requests.get(
api_url + '/users?offset=0&limit=20',
headers={
"Accept": "application/jupyterhub-pagination+json",
"Authorization": f"token {token}",
},
)
r.raise_for_status()
r.json()
```
For backward-compatibility, the default structure of list responses is unchanged.
However, this lacks pagination information (e.g. is there a next page),
so if you have enough users that they won't fit in the first response,
it is a good idea to opt-in to the new paginated list format.
There is a new schema for list responses which include pagination information.
You can request this by including the header:
```
Accept: application/jupyterhub-pagination+json
```
with your request, in which case a response will look like:
```python
{
"items": [
{
"name": "username",
"kind": "user",
...
},
],
"_pagination": {
"offset": 0,
"limit": 20,
"total": 50,
"next": {
"offset": 20,
"limit": 20,
"url": "http://127.0.0.1:8081/hub/api/users?limit=20&offset=20"
}
}
}
```
where the list results (same as pre-2.0) will be in `items`,
and pagination info will be in `_pagination`.
The `next` field will include the `offset`, `limit`, and `url` for requesting the next page.
`next` will be `null` if there is no next page.
Pagination is governed by two configuration options:
- `JupyterHub.api_page_default_limit` - the page size, if `limit` is unspecified in the request
and the new pagination API is requested
(default: 50)
- `JupyterHub.api_page_max_limit` - the maximum page size a request can ask for (default: 200)
Pagination is enabled on the `GET /users`, `GET /groups`, and `GET /proxy` REST endpoints.
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
Support for multiple servers per user was introduced in JupyterHub [version 0.8.](../changelog.md)
With JupyterHub version 0.8, support for multiple servers per user has landed.
Prior to that, each user could only launch a single default server via the API
like this:
@@ -292,7 +176,7 @@ First you must enable named-servers by including the following setting in the `j
`c.JupyterHub.allow_named_servers = True`
If you are using the [zero-to-jupyterhub-k8s](https://github.com/jupyterhub/zero-to-jupyterhub-k8s) set-up to run JupyterHub,
If using the [zero-to-jupyterhub-k8s](https://github.com/jupyterhub/zero-to-jupyterhub-k8s) set-up to run JupyterHub,
then instead of editing the `jupyterhub_config.py` file directly, you could pass
the following as part of the `config.yaml` file, as per the [tutorial](https://zero-to-jupyterhub.readthedocs.io/en/latest/):
@@ -320,9 +204,12 @@ or kubernetes pods.
## Learn more about the API
You can see the full [JupyterHub REST API][] for more 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][].
[interactive style on swagger's petstore]: https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/jupyterhub/jupyterhub/HEAD/docs/rest-api.yml#!/default
[openapi initiative]: https://www.openapis.org/
[jupyterhub rest api]: ./rest-api
[scopes]: ../rbac/scopes.md
[jupyter notebook rest api]: https://petstore3.swagger.io/?url=https://raw.githubusercontent.com/jupyter/notebook/HEAD/notebook/services/api/api.yaml

View File

@@ -2,7 +2,7 @@
## Background
The thing which users directly connect to is the proxy, which by default is
The thing which users directly connect to is the proxy, by default
`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
@@ -10,15 +10,16 @@ servers continues, even if the hub itself restarts or goes down.
When you first configure the hub, you may not even realize this
because the proxy is automatically managed by the hub. This is great
for getting started and even most use-cases, although, everytime you restart the
hub, all user connections are also restarted. However, it is also simple to
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
run the proxy as a service separate from the hub, so that you are free
to reconfigure the hub while only interrupting users who are waiting for their notebook server to start.
starting their notebook server.
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). If you are using a different proxy, such
as [Traefik](https://github.com/traefik/traefik), these instructions are probably not relevant to you.
[configurable-http-proxy](https://github.com/jupyterhub/configurable-http-proxy),
and that page has some docs. If you are using a different proxy, such
as Traefik, these instructions are probably not relevant to you.
## Configuration options
@@ -39,14 +40,9 @@ 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 argument for this is:
```bash
$ configurable-http-proxy --ip=127.0.0.1 --port=8000 --api-ip=127.0.0.1 --api-port=8001 --default-target=http://localhost:8081 --error-target=http://localhost:8081/hub/error
```
(Details on how to do this is out of the scope of this tutorial. For example, it might be a
systemd service configured within another docker container). The proxy has no
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
configuration files, all configuration is via the command line and
environment variables.
@@ -61,9 +57,9 @@ match the token given to `c.ConfigurableHTTPProxy.auth_token`.
You should check the [configurable-http-proxy
options](https://github.com/jupyterhub/configurable-http-proxy) to see
what other options are needed, for example, SSL options. Note that
these options are configured in the hub if the hub is starting the proxy, so you
need to configure the options there.
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

View File

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

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
@@ -35,17 +47,6 @@ A Service may have the following properties:
the service will be added to the proxy at `/services/:name`
- `api_token: str (default - None)` - For Externally-Managed Services you need to specify
an API token to perform API requests to the Hub
- `display: bool (default - True)` - When set to true, display a link to the
service's URL under the 'Services' dropdown in user's hub home page.
- `oauth_no_confirm: bool (default - False)` - When set to true,
skip the OAuth confirmation page when users access this service.
By default, when users authenticate with a service using JupyterHub,
they are prompted to confirm that they want to grant that service
access to their credentials.
Skipping the confirmation page is useful for admin-managed services that are considered part of the Hub
and shouldn't need extra prompts for login.
If a service is also to be managed by the Hub, it has a few extra options:
@@ -61,7 +62,7 @@ If a service is also to be managed by the Hub, it has a few extra options:
A **Hub-Managed Service** is started by the Hub, and the Hub is responsible
for the Service's actions. A Hub-Managed Service can only be a local
subprocess of the Hub. The Hub will take care of starting the process and
restart the service if the service stops.
restarts it if it stops.
While Hub-Managed Services share some similarities with notebook Spawners,
there are no plans for Hub-Managed Services to support the same spawning
@@ -85,20 +86,10 @@ 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',
'admin': True,
'command': [sys.executable, '-m', 'jupyterhub_idle_culler', '--timeout=3600']
}
]
@@ -115,8 +106,6 @@ parameters, which describe the environment needed to start the Service process:
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
@@ -125,10 +114,6 @@ 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
@@ -186,7 +171,7 @@ information to the Service via the environment variables described above. A
flexible Service, whether managed by the Hub or not, can make use of these
same environment variables.
When you run a service that has a URL, it will be accessible under a
When you run a service that has a url, it will be accessible under a
`/services/` prefix, such as `https://myhub.horse/services/my-service/`. For
your service to route proxied requests properly, it must take
`JUPYTERHUB_SERVICE_PREFIX` into account when routing requests. For example, a
@@ -201,54 +186,27 @@ 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.
The reference, or base, implementation is the [`HubAuth`][hubauth] class,
which implements the requests to the Hub.
There are two levels of authentication with the Hub:
- {class}`.HubAuth` - the most basic authentication,
for services that should only accept API requests authorized with a token.
- {class}`.HubOAuth` - For services that should use oauth to authenticate with the Hub.
This should be used for any service that serves pages that should be visited with a browser.
To use HubAuth, you must set the `.api_token` instance variable. This can be
done either programmatically when constructing the class, or via the
`JUPYTERHUB_API_TOKEN` environment variable. A number of the examples in the
root of the jupyterhub git repository set the `JUPYTERHUB_API_TOKEN` variable
so consider having a look at those for futher reading
([cull-idle](https://github.com/jupyterhub/jupyterhub/tree/master/examples/cull-idle),
[external-oauth](https://github.com/jupyterhub/jupyterhub/tree/master/examples/external-oauth),
[service-notebook](https://github.com/jupyterhub/jupyterhub/tree/master/examples/service-notebook)
and [service-whoiami](https://github.com/jupyterhub/jupyterhub/tree/master/examples/service-whoami))
(TODO: Where is this API TOKen set?)
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:
[`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:
@@ -257,9 +215,7 @@ which makes a request of the Hub, and returns:
{
"name": "username",
"groups": ["list", "of", "groups"],
"scopes": [
"access:servers!server=username/",
],
"admin": False, # or True
}
```
@@ -269,19 +225,6 @@ action.
HubAuth also caches the Hub's response for a number of seconds,
configurable by the `cookie_cache_max_age` setting (default: five minutes).
If your service would like to make further requests _on behalf of users_,
it should use the token issued by this OAuth process.
If you are using tornado,
you can access the token authenticating the current request with {meth}`.HubAuth.get_token`.
:::{versionchanged} 2.2
{meth}`.HubAuth.get_token` adds support for retrieving
tokens stored in tornado cookies after the completion of OAuth.
Previously, it only retrieved tokens from URL parameters or the Authorization header.
Passing `get_token(handler, in_cookie=False)` preserves this behavior.
:::
### Flask Example
For example, you have a Flask service that returns information about a user.
@@ -290,24 +233,70 @@ service. See the `service-whoami-flask` example in the
[JupyterHub GitHub repo](https://github.com/jupyterhub/jupyterhub/tree/HEAD/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
@@ -317,59 +306,39 @@ class MyHandler(HubOAuthenticated, web.RequestHandler):
...
```
The HubAuth class will automatically load the desired configuration from the Service
[environment variables](service-env).
The HubAuth will automatically load the desired configuration from the Service
environment variables.
:::{versionchanged} 2.0
Access scopes are used to govern access to services.
Prior to 2.0,
sets of users and groups could be used to grant access
by defining `.hub_groups` or `.hub_users` on the authenticated handler.
These are ignored if the 2.0 `.hub_scopes` is defined.
:::
:::{seealso}
{meth}`.HubAuth.check_scopes`
:::
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.
### 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][]:
```python
r = requests.get(
"http://127.0.0.1:8081/hub/api/user",
'/'.join(["http://127.0.0.1:8081/hub/api",
"authorizations/cookie/jupyterhub-services",
quote(encrypted_cookie, safe=''),
]),
headers = {
'Authorization' : f'token {api_token}',
'Authorization' : 'token %s' % api_token,
},
)
r.raise_for_status()
@@ -378,36 +347,24 @@ and taking note of the following process:
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",
"..."
]
"groups": ["serenity", "guild"]
}
```
The `scopes` field can be used to manage access.
Note: a user will have access to a service to complete oauth access to the service for the first time.
Individual permissions may be revoked at any later point without revoking the token,
in which case the `scopes` field in this model should be checked on each access.
The default required scopes for access are available from `hub_auth.oauth_scopes` or `$JUPYTERHUB_OAUTH_ACCESS_SCOPES`.
An example of using an Externally-Managed Service and authentication is
in the [nbviewer README][nbviewer example] section on securing the notebook viewer,
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

@@ -4,9 +4,9 @@ A [Spawner][] starts each single-user notebook server.
The Spawner represents an abstract interface to a process,
and a custom Spawner needs to be able to take three actions:
- start a process
- poll whether a process is still running
- stop a process
- start the process
- poll whether the process is still running
- stop the process
## Examples
@@ -15,9 +15,9 @@ Some examples include:
- [DockerSpawner](https://github.com/jupyterhub/dockerspawner) for spawning user servers in Docker containers
- `dockerspawner.DockerSpawner` for spawning identical Docker containers for
each user
each users
- `dockerspawner.SystemUserSpawner` for spawning Docker containers with an
environment and home directory for each user
environment and home directory for each users
- both `DockerSpawner` and `SystemUserSpawner` also work with Docker Swarm for
launching containers on remote machines
- [SudoSpawner](https://github.com/jupyterhub/sudospawner) enables JupyterHub to
@@ -28,23 +28,23 @@ Some examples include:
servers in YARN containers on a Hadoop cluster
- [SSHSpawner](https://github.com/NERSC/sshspawner) to spawn notebooks
on a remote server using SSH
- [KubeSpawner](https://github.com/jupyterhub/kubespawner) to spawn notebook servers on kubernetes cluster.
## Spawner control methods
### Spawner.start
`Spawner.start` should start a single-user server for a single user.
`Spawner.start` should start the single-user server for a single user.
Information about the user can be retrieved from `self.user`,
an object encapsulating the user's name, authentication, and server info.
The return value of `Spawner.start` should be the `(ip, port)` of the running server,
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,
@@ -56,10 +56,8 @@ 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,
@@ -67,65 +65,13 @@ not just requested. JupyterHub can handle `Spawner.start` being very slow
(such as PBS-style batch queues, or instantiating whole AWS instances)
via relaxing the `Spawner.start_timeout` config value.
#### Note on IPs and ports
`Spawner.ip` and `Spawner.port` attributes set the _bind_ URL,
which the single-user server should listen on
(passed to the single-user process via the `JUPYTERHUB_SERVICE_URL` environment variable).
The _return_ value is the IP and port (or full URL) the Hub should _connect to_.
These are not necessarily the same, and usually won't be in any Spawner that works with remote resources or containers.
The default for `Spawner.ip`, and `Spawner.port` is `127.0.0.1:{random}`,
which is appropriate for Spawners that launch local processes,
where everything is on localhost and each server needs its own port.
For remote or container Spawners, it will often make sense to use a different value,
such as `ip = '0.0.0.0'` and a fixed port, e.g. `8888`.
The defaults can be changed in the class,
preserving configuration with traitlets:
```python
from traitlets import default
from jupyterhub.spawner import Spawner
class MySpawner(Spawner):
@default("ip")
def _default_ip(self):
return '0.0.0.0'
@default("port")
def _default_port(self):
return 8888
async def start(self):
env = self.get_env()
cmd = []
# get jupyterhub command to run,
# typically ['jupyterhub-singleuser']
cmd.extend(self.cmd)
cmd.extend(self.get_args())
remote_server_info = await self._actually_start_server_somehow(cmd, env)
url = self.get_public_url_from(remote_server_info)
return url
```
#### Exception handling
When `Spawner.start` raises an Exception, a message can be passed on to the user via the exception using a `.jupyterhub_html_message` or `.jupyterhub_message` attribute.
When the Exception has a `.jupyterhub_html_message` attribute, it will be rendered as HTML to the user.
Alternatively `.jupyterhub_message` is rendered as unformatted text.
If both attributes are not present, the Exception will be shown to the user as unformatted text.
### Spawner.poll
`Spawner.poll` checks if the spawner is still running.
`Spawner.poll` should check if the spawner is still running.
It should return `None` if it is still running,
and an integer exit status, otherwise.
In the case of local processes, `Spawner.poll` uses `os.kill(PID, 0)`
For the local process case, `Spawner.poll` uses `os.kill(PID, 0)`
to check if the local process is still running. On Windows, it uses `psutil.pid_exists`.
### Spawner.stop
@@ -141,7 +87,7 @@ A JSON-able dictionary of state can be used to store persisted information.
Unlike start, stop, and poll methods, the state methods must not be coroutines.
In the case of single processes, the Spawner state is only the process ID of the server:
For the single-process case, the Spawner state is only the process ID of the server:
```python
def get_state(self):
@@ -261,76 +207,6 @@ 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: `['jupyterhub-singleuser']`)
- Spawner.args (CLI args to pass to the cmd, default: empty)
where the configuration:
```python
c.Spawner.cmd = ["my-singleuser-wrapper"]
c.Spawner.args = ["--debug", "--flag"]
```
would result in spawning the command:
```bash
my-singleuser-wrapper --debug --flag
```
The `Spawner.get_args()` method is how `Spawner.args` is accessed,
and can be used by Spawners to customize/extend user-provided arguments.
Prior to 2.0, JupyterHub unconditionally added certain options _if specified_ to the command-line,
such as `--ip={Spawner.ip}` and `--port={Spawner.port}`.
These have now all been moved to environment variables,
and from JupyterHub 2.0,
the command-line launched by JupyterHub is fully specified by overridable configuration `Spawner.cmd + Spawner.args`.
Most process configuration is passed via environment variables.
Additional variables can be specified via the `Spawner.environment` configuration.
The process environment is returned by `Spawner.get_env`, which specifies the following environment variables:
- JUPYTERHUB*SERVICE_URL - the \_bind* URL where the server should launch its HTTP server (`http://127.0.0.1:12345`).
This includes `Spawner.ip` and `Spawner.port`; _new in 2.0, prior to 2.0 IP, port were on the command-line and only if specified_
- JUPYTERHUB_SERVICE_PREFIX - the URL prefix the service will run on (e.g. `/user/name/`)
- JUPYTERHUB_USER - the JupyterHub user's username
- JUPYTERHUB_SERVER_NAME - the server's name, if using named servers (default server has an empty name)
- JUPYTERHUB_API_URL - the full URL for the JupyterHub API (http://17.0.0.1:8001/hub/api)
- JUPYTERHUB_BASE_URL - the base URL of the whole jupyterhub deployment, i.e. the bit before `hub/` or `user/`,
as set by `c.JupyterHub.base_url` (default: `/`)
- JUPYTERHUB_API_TOKEN - the API token the server can use to make requests to the Hub.
This is also the OAuth client secret.
- JUPYTERHUB_CLIENT_ID - the OAuth client ID for authenticating visitors.
- JUPYTERHUB_OAUTH_CALLBACK_URL - the callback URL to use in OAuth, typically `/user/:name/oauth_callback`
- JUPYTERHUB_OAUTH_ACCESS_SCOPES - the scopes required to access the server (called JUPYTERHUB_OAUTH_SCOPES prior to 3.0)
- JUPYTERHUB_OAUTH_CLIENT_ALLOWED_SCOPES - the scopes the service is allowed to request.
If no scopes are requested explicitly, these scopes will be requested.
Optional environment variables, depending on configuration:
- JUPYTERHUB*SSL*[KEYFILE|CERTFILE|CLIENT_CI] - SSL configuration, when `internal_ssl` is enabled
- JUPYTERHUB_ROOT_DIR - the root directory of the server (notebook directory), when `Spawner.notebook_dir` is defined (new in 2.0)
- JUPYTERHUB_DEFAULT_URL - the default URL for the server (for redirects from `/user/:name/`),
if `Spawner.default_url` is defined
(new in 2.0, previously passed via CLI)
- JUPYTERHUB_DEBUG=1 - generic debug flag, sets maximum log level when `Spawner.debug` is True
(new in 2.0, previously passed via CLI)
- JUPYTERHUB_DISABLE_USER_CONFIG=1 - disable loading user config,
sets maximum log level when `Spawner.debug` is True (new in 2.0,
previously passed via CLI)
- JUPYTERHUB*[MEM|CPU]*[LIMIT_GUARANTEE] - the values of CPU and memory limits and guarantees.
These are not expected to be enforced by the process,
but are made available as a hint,
e.g. for resource monitoring extensions.
## Spawners, resource limits, and guarantees (Optional)
Some spawners of the single-user notebook servers allow setting limits or
@@ -338,10 +214,9 @@ guarantees on resources, such as CPU and memory. To provide a consistent
experience for sysadmins and users, we provide a standard way to set and
discover these resource limits and guarantees, such as for memory and CPU.
For the limits and guarantees to be useful, **the spawner must implement
support for them**. For example, `LocalProcessSpawner`, the default
support for them**. For example, LocalProcessSpawner, the default
spawner, does not support limits and guarantees. One of the spawners
that supports limits and guarantees is the
[`systemdspawner`](https://github.com/jupyterhub/systemdspawner).
that supports limits and guarantees is the `systemdspawner`.
### Memory Limits & Guarantees
@@ -368,7 +243,7 @@ limits or guarantees are provided, and no environment values are set.
`c.Spawner.cpu_limit`: In supported spawners, you can set
`c.Spawner.cpu_limit` to limit the total number of cpu-cores that a
single-user notebook server can use. These can be fractional - `0.5` means 50%
of one CPU core, `4.0` is 4 CPU-cores, etc. This value is also set in the
of one CPU core, `4.0` is 4 cpu-cores, etc. This value is also set in the
single-user notebook server's environment variable `CPU_LIMIT`. The limit does
not claim that you will be able to use all the CPU up to your limit as other
higher priority applications might be taking up CPU.
@@ -401,10 +276,9 @@ container `ip` prior to starting and pass that to `.create_certs` (TODO: edit).
In general though, this method will not need to be changed and the default
`ip`/`dns` (localhost) info will suffice.
When `.create_certs` is run, it will create the certificates in a default,
central location specified by `c.JupyterHub.internal_certs_location`. For
`Spawners` that need access to these certs elsewhere (i.e. on another host
altogether), the `.move_certs` method can be overridden to move the certs
appropriately. Again, using `DockerSpawner` as an example, this would entail
moving certs to a directory that will get mounted into the container this
spawner starts.
When `.create_certs` is run, it will `.create_certs` in a default, central
location specified by `c.JupyterHub.internal_certs_location`. For `Spawners`
that need access to these certs elsewhere (i.e. on another host altogether),
the `.move_certs` method can be overridden to move the certs appropriately.
Again, using `DockerSpawner` as an example, this would entail moving certs
to a directory that will get mounted into the container this spawner starts.

View File

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

View File

@@ -1,29 +1,28 @@
# Working with templates and UI
The pages of the JupyterHub application are generated from
[Jinja](https://jinja.palletsprojects.com) templates. These allow the header, for
[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 template(s), you can have complete control over JupyterHub's
your own templates, you can have complete control over JupyterHub's
appearance.
## Custom Templates
JupyterHub will look for custom templates in all paths included in the
`JupyterHub.template_paths` configuration option, falling back on these
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)
if no custom template(s) with specified name(s) are found. This fallback
behavior is new in version 0.9; previous versions searched only the paths
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
or as few templates as you desire.
## Extending Templates
Jinja provides a mechanism to [extend templates](https://jinja.palletsprojects.com/en/3.0.x/templates/#template-inheritance).
A base template can define `block`(s) within itself that child templates can fill up or
supply content to. The
[JupyterHub default templates](https://github.com/jupyterhub/jupyterhub/tree/HEAD/share/jupyterhub/templates)
make extensive use of blocks, thus allowing you to customize parts of the
Jinja provides a mechanism to [extend templates](http://jinja.pocoo.org/docs/2.10/templates/#template-inheritance).
A base template can define a `block`, and child templates can replace or
supplement the material in the block. The
[JupyterHub templates](https://github.com/jupyterhub/jupyterhub/tree/HEAD/share/jupyterhub/templates)
make extensive use of blocks, which allows you to customize parts of the
interface easily.
In general, a child template can extend a base template, `page.html`, by beginning with:
@@ -41,15 +40,15 @@ file with this block:
{% extends "templates/page.html" %}
```
By defining `block`s with the same name as in the base template, child templates
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
template can be included in the child template with the `{{ super() }}` directive.
template can be included with the `{{ super() }}` directive.
### Example
To add an additional message to the spawn-pending page, below the existing
text about the server starting up, place the content below in a file named
`spawn_pending.html`. This directory must also be included in the
text about the server starting up, place this content in a file named
`spawn_pending.html` in a directory included in the
`JupyterHub.template_paths` configuration option.
```html
@@ -62,7 +61,7 @@ text about the server starting up, place the content below in a file named
To add announcements to be displayed on a page, you have two options:
- [Extend the page templates as described above](#extending-templates)
- Extend the page templates as described above
- Use configuration variables
### Announcement Configuration Variables
@@ -72,10 +71,10 @@ the top of all pages. The more specific variables
`announcement_login`, `announcement_spawn`, `announcement_home`, and
`announcement_logout` are more specific and only show on their
respective pages (overriding the global `announcement` variable).
Note that changing these variables requires a restart, unlike direct
Note that changing these variables require a restart, unlike direct
template extension.
Alternatively, you can get the same effect by extending templates, which allows you
You can get the same effect by extending templates, which allows you
to update the messages without restarting. Set
`c.JupyterHub.template_paths` as mentioned above, and then create a
template (for example, `login.html`) with:
@@ -85,5 +84,5 @@ template (for example, `login.html`) with:
```
Extending `page.html` puts the message on all pages, but note that
extending `page.html` takes precedence over an extension of a specific
extending `page.html` take precedence over an extension of a specific
page (unlike the variable-based approach above).

View File

@@ -2,13 +2,13 @@
This document describes how JupyterHub routes requests.
This does not include the [REST API](./rest.md) URLs.
This does not include the [REST API](./rest.md) urls.
In general, all URLs can be prefixed with `c.JupyterHub.base_url` to
run the whole JupyterHub application on a prefix.
All authenticated handlers redirect to `/hub/login` to log-in users
before being redirected back to the originating page.
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.
## `/`
@@ -25,12 +25,12 @@ This is an authenticated URL.
This handler redirects users to the default URL of the application,
which defaults to the user's default server.
That is, the handler redirects to `/hub/spawn` if the user's server is not running,
or to the server itself (`/user/:name`) if the server is running.
That is, it redirects to `/hub/spawn` if the user's server is not running,
or the server itself (`/user/:name`) if the server is running.
This default URL behavior can be customized in two ways:
This default url behavior can be customized in two ways:
First, to redirect users to the JupyterHub home page (`/hub/home`)
To redirect users to the JupyterHub home page (`/hub/home`)
instead of spawning their server,
set `redirect_to_server` to False:
@@ -40,7 +40,7 @@ c.JupyterHub.redirect_to_server = False
This might be useful if you have a Hub where you expect
users to be managing multiple server configurations
but automatic spawning is not desirable.
and automatic spawning is not desirable.
Second, you can customise the landing page to any page you like,
such as a custom service you have deployed e.g. with course information:
@@ -57,7 +57,7 @@ By default, the Hub home page has just one or two buttons
for starting and stopping the user's server.
If named servers are enabled, there will be some additional
tools for management of the named servers.
tools for management of named servers.
_Version added: 1.0_ named server UI is new in 1.0.
@@ -65,34 +65,34 @@ _Version added: 1.0_ named server UI is new in 1.0.
This is the JupyterHub login page.
If you have a form-based username+password login,
such as the default [PAMAuthenticator](https://en.wikipedia.org/wiki/Pluggable_authentication_module),
such as the default PAMAuthenticator,
this page will render the login form.
![A login form](../images/login-form.png)
If login is handled by an external service,
e.g. with OAuth, this page will have a button,
declaring "Log in with ..." which users can click
to log in with the chosen service.
declaring "Login with ..." which users can click
to login with the chosen service.
![A login redirect button](../images/login-button.png)
If you want to skip the user interaction and initiate login
via the button, you can set:
If you want to skip the user-interaction to initiate logging in
via the button, you can set
```python
c.Authenticator.auto_login = True
```
This can be useful when the user is "already logged in" via some mechanism.
However, a handshake via `redirects` is necessary to complete the authentication with JupyterHub.
This can be useful when the user is "already logged in" via some mechanism,
but a handshake via redirects is necessary to complete the authentication with JupyterHub.
## `/hub/logout`
Visiting `/hub/logout` clears [cookies](https://en.wikipedia.org/wiki/HTTP_cookie) from the current browser.
Visiting `/hub/logout` clears cookies from the current browser.
Note that **logging out does not stop a user's server(s)** by default.
If you would like to shut down user servers on logout,
If you would like to shutdown user servers on logout,
you can enable this behavior with:
```python
@@ -105,8 +105,8 @@ does not mean the user is no longer actively using their server from another mac
## `/user/:username[/:servername]`
If a user's server is running, this URL is handled by the user's given server,
not by the Hub.
The username is the first part, and if using named servers,
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/...`
@@ -117,15 +117,14 @@ This URL indicates a request for a user server that is not running
(because `/user/...` would have been handled by the notebook server
if the specified server were running).
Handling this URL depends on two conditions: whether a requested user is found
as a match and the state of the requested user's notebook server,
for example:
Handling this URL is the most complicated condition in JupyterHub,
because there can be many states:
1. the server is not active
1. server is not active
a. user matches
b. user doesn't match
2. the server is ready
3. the server is pending, but not ready
2. server is ready
3. server is pending, but not ready
If the server is pending spawn,
the browser will be redirected to `/hub/spawn-pending/:username/:servername`
@@ -141,37 +140,39 @@ Some checks are performed and a delay is added before redirecting back to `/user
If something is really wrong, this can result in a redirect loop.
Visiting this page will never result in triggering the spawn of servers
without additional user action (i.e. clicking the link on the page).
without additional user action (i.e. clicking the link on the page)
![Visiting a URL for a server that's not running](../images/not-running.png)
_Version changed: 1.0_
Prior to 1.0, this URL itself was responsible for spawning servers.
If the progress page was pending, the URL redirected it to running servers.
This was useful because it made sure that the requested servers were restarted after they stopped.
However, it could also be harmful because unused servers would continuously be restarted if e.g.
an idle JupyterLab frontend that constantly makes polling requests was openly pointed at it.
Prior to 1.0, this URL itself was responsible for spawning servers,
and served the progress page if it was pending,
redirected to running servers, and
This was useful because it made sure that requested servers were restarted after they stopped,
but could also be harmful because unused servers would continuously be restarted if e.g.
an idle JupyterLab frontend were open pointed at it,
which constantly makes polling requests.
### Special handling of API requests
Requests to `/user/:username[/:servername]/api/...` are assumed to be
from applications connected to stopped servers.
These requests fail with a `503` status code and an informative JSON error message
that indicates how to spawn the server.
This is meant to help applications such as JupyterLab,
These are failed with 503 and an informative JSON error message
indicating how to spawn the server.
This is meant to help applications such as JupyterLab
that are connected to a server that has stopped.
_Version changed: 1.0_
JupyterHub version 0.9 failed these API requests with status `404`,
but version 1.0 uses 503.
JupyterHub 0.9 failed these API requests with status 404,
but 1.0 uses 503.
## `/user-redirect/...`
The `/user-redirect/...` URL is for sharing a URL that will redirect a user
This URL is for sharing a URL that will redirect a user
to a path on their own default server.
This is useful when different users have the same file at the same URL on their servers,
This is useful when users have the same file at the same URL on their servers,
and you want a single link to give to any user that will open that file on their server.
e.g. a link to `/user-redirect/notebooks/Index.ipynb`
@@ -193,7 +194,7 @@ that is intended to make it possible.
### `/hub/spawn[/:username[/:servername]]`
Requesting `/hub/spawn` will spawn the default server for the current user.
If the `username` and optionally `servername` are specified,
If `username` and optionally `servername` are specified,
then the specified server for the specified user will be spawned.
Once spawn has been requested,
the browser is redirected to `/hub/spawn-pending/...`.
@@ -206,7 +207,7 @@ and a POST request will trigger the actual spawn and redirect.
_Version added: 1.0_
1.0 adds the ability to specify `username` and `servername`.
1.0 adds the ability to specify username and servername.
Prior to 1.0, only `/hub/spawn` was recognized for the default server.
_Version changed: 1.0_
@@ -246,7 +247,7 @@ against the [JupyterHub REST API](./rest.md).
Administrators can take various administrative actions from this page:
- add/remove users
- grant admin privileges
- start/stop user servers
- shutdown JupyterHub itself
1. add/remove users
2. grant admin privileges
3. start/stop user servers
4. shutdown JupyterHub itself

View File

@@ -5,7 +5,7 @@ The **Security Overview** section helps you learn about:
- the design of JupyterHub with respect to web security
- the semi-trusted user
- the available mitigations to protect untrusted users from each other
- the value of periodic security audits
- the value of periodic security audits.
This overview also helps you obtain a deeper understanding of how JupyterHub
works.
@@ -13,12 +13,12 @@ works.
## Semi-trusted and untrusted users
JupyterHub is designed to be a _simple multi-user server for modestly sized
groups_ of **semi-trusted** users. While the design reflects serving
semi-trusted users, JupyterHub can also be suitable for serving **untrusted** users.
groups_ of **semi-trusted** users. While the design reflects serving semi-trusted
users, JupyterHub is not necessarily unsuitable for serving **untrusted** users.
As a result, using JupyterHub with **untrusted** users means more work by the
administrator, since much care is required to secure a Hub, with extra caution on
protecting users from each other.
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
@@ -31,8 +31,9 @@ servers) as a single website (i.e. single domain).
## Protect users from each other
To protect users from each other, a user must **never** be able to write arbitrary
HTML and serve it to another user on the Hub's domain. This is prevented by JupyterHub's
authentication setup because only the owner of a given single-user notebook server is
HTML and serve it to another user on the Hub's domain. JupyterHub's
authentication setup prevents a user writing arbitrary HTML and serving it to
another user because only the owner of a given single-user notebook server is
allowed to view user-authored pages served by the given single-user notebook
server.
@@ -41,15 +42,15 @@ ensure that:
- A user **does not have permission** to modify their single-user notebook server,
including:
- the installation of new packages in the Python environment that runs
their single-user server;
- the creation of new files in any `PATH` directory that precedes the
directory containing `jupyterhub-singleuser` (if the `PATH` is used
to resolve the single-user executable instead of using an absolute path);
- the modification of environment variables (e.g. PATH, PYTHONPATH) for
their single-user server;
- the modification of the configuration of the notebook server
(the `~/.jupyter` or `JUPYTER_CONFIG_DIR` directory).
- A user **may not** install new packages in the Python environment that runs
their single-user server.
- If the `PATH` is used to resolve the single-user executable (instead of
using an absolute path), a user **may not** create new files in any `PATH`
directory that precedes the directory containing `jupyterhub-singleuser`.
- A user may not modify environment variables (e.g. PATH, PYTHONPATH) for
their single-user 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_
@@ -57,7 +58,7 @@ If any additional services are run on the same domain as the Hub, the services
## Mitigate security issues
The several approaches to mitigating security issues with configuration
Several approaches to mitigating these issues with configuration
options provided by JupyterHub include:
### Enable subdomains
@@ -68,23 +69,24 @@ desired effect, and user servers and the Hub are protected from each other. A
user's single-user server will be at `username.jupyter.mydomain.com`. This also
requires all user subdomains to point to the same address, which is most easily
accomplished with wildcard DNS. Since this spreads the service across multiple
domains, you will need wildcard SSL as well. Unfortunately, for many
domains, you will need wildcard SSL, as well. Unfortunately, for many
institutional domains, wildcard DNS and SSL are not available. **If you do plan
to serve untrusted users, enabling subdomains is highly encouraged**, as it
resolves the cross-site issues.
### Disable user config
If subdomains are unavailable or undesirable, JupyterHub provides a
If subdomains are not available or not desirable, JupyterHub provides a
configuration option `Spawner.disable_user_config`, which can be set to prevent
the user-owned configuration files from being loaded. After implementing this
option, `PATH`s and package installation are the other things that the
option, PATHs and package installation and PATHs are the other things that the
admin must enforce.
### Prevent spawners from evaluating shell configuration files
For most Spawners, `PATH` is not something users can influence, but it's important that
the Spawner should _not_ evaluate shell configuration files prior to launching the server.
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
files prior to launching the server.
### Isolate packages using virtualenv
@@ -99,14 +101,14 @@ pose additional risk to the web application's security.
### Encrypt internal connections with SSL/TLS
By default, all communications on the server, between the proxy, hub, and single
-user notebooks are performed unencrypted. Setting the `internal_ssl` flag in
By default, all communication on the server, between the proxy, hub, and single
-user notebooks is performed unencrypted. Setting the `internal_ssl` flag in
`jupyterhub_config.py` secures the aforementioned routes. Turning this
feature on does require that the enabled `Spawner` can use the certificates
generated by the `Hub` (the default `LocalProcessSpawner` can, for instance).
It is also important to note that this encryption **does not** cover the
`zmq tcp` sockets between the Notebook client and kernel yet. While users cannot
It is also important to note that this encryption **does not** (yet) cover the
`zmq tcp` sockets between the Notebook client and kernel. While users cannot
submit arbitrary commands to another user's kernel, they can bind to these
sockets and listen. When serving untrusted users, this eavesdropping can be
mitigated by setting `KernelManager.transport` to `ipc`. This applies standard
@@ -117,8 +119,8 @@ extend to securing the `tcp` sockets as well.
## Security audits
We recommend that you do periodic reviews of your deployment's security. It's
good practice to keep [JupyterHub](https://readthedocs.org/projects/jupyterhub/), [configurable-http-proxy][], and [nodejs
versions](https://github.com/nodejs/Release) up to date.
good practice to keep JupyterHub, configurable-http-proxy, and nodejs
versions up to date.
A handy website for testing your deployment is
[Qualsys' SSL analyzer tool](https://www.ssllabs.com/ssltest/analyze.html).
@@ -127,8 +129,8 @@ A handy website for testing your deployment is
## Vulnerability reporting
If you believe you have found a security vulnerability in JupyterHub, or any
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

@@ -1,9 +1,35 @@
# Troubleshooting
When troubleshooting, you may see unexpected behaviors or receive an error
message. This section provides links for identifying the cause of the
message. This section provide links for identifying the cause of the
problem and how to resolve it.
[_Behavior_](#behavior)
- JupyterHub proxy fails to start
- sudospawner fails to run
- What is the default behavior when none of the lists (admin, allowed,
allowed groups) are set?
- JupyterHub Docker container not accessible at localhost
[_Errors_](#errors)
- 500 error after spawning my single-user server
[_How do I...?_](#how-do-i)
- Use a chained SSL certificate
- Install JupyterHub without a network connection
- I want access to the whole filesystem, but still default users to their home directory
- How do I increase the number of pySpark executors on YARN?
- How do I use JupyterLab's prerelease version with JupyterHub?
- How do I set up JupyterHub for a workshop (when users are not known ahead of time)?
- How do I set up rotating daily logs?
- Toree integration with HDFS rack awareness script
- Where do I find Docker images and Dockerfiles related to JupyterHub?
[_Troubleshooting commands_](#troubleshooting-commands)
## Behavior
### JupyterHub proxy fails to start
@@ -14,9 +40,9 @@ If you have tried to start the JupyterHub proxy and it fails to start:
`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 you are using a
recent version of [Node](https://nodejs.org). Some versions of Ubuntu/Debian come with a very old version
of Node and it is necessary to update Node.
**Note**: If this occurs on Ubuntu/Debian, check that the you are using a
recent version of node. Some versions of Ubuntu/Debian come with a version
of node that is very old, and it is necessary to update node.
### sudospawner fails to run
@@ -35,24 +61,24 @@ to the config file, `jupyterhub_config.py`.
### What is the default behavior when none of the lists (admin, allowed, allowed groups) are set?
When nothing is given for these lists, there will be no admins, and all users
who can authenticate on the system (i.e. all the Unix users on the server with
who can authenticate on the system (i.e. all the unix users on the server with
a password) will be allowed to start a server. The allowed username set lets you limit
this to a particular set of users, and admin_users lets you specify who
among them may use the admin interface (not necessary, unless you need to do
things like inspect other users' servers or modify the user list at runtime).
things like inspect other users' servers, or modify the user list at runtime).
### JupyterHub Docker container is not accessible at localhost
### 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,
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?
### How can I kill ports from JupyterHub managed services that have been orphaned?
I started JupyterHub + nbgrader on the same host without containers. When I try to restart JupyterHub + nbgrader with this configuration, errors appear that the service accounts cannot start because the ports are being used.
@@ -66,12 +92,12 @@ Where `<service_port>` is the port used by the nbgrader course service. This con
### Why am I getting a Spawn failed error message?
After successfully logging in to JupyterHub with a compatible authenticator, I get a 'Spawn failed' error message in the browser. The JupyterHub logs have `jupyterhub KeyError: "getpwnam(): name not found: <my_user_name>`.
After successfully logging in to JupyterHub with a compatible authenticators, I get a 'Spawn failed' error message in the browser. The JupyterHub logs have `jupyterhub KeyError: "getpwnam(): name not found: <my_user_name>`.
This issue occurs when the authenticator requires a local system user to exist. In these cases, you need to use a spawner
that does not require an existing system user account, such as `DockerSpawner` or `KubeSpawner`.
### How can I run JupyterHub with sudo but use my current environment variables and virtualenv location?
### How can I run JupyterHub with sudo but use my current env vars and virtualenv location?
When launching JupyterHub with `sudo jupyterhub` I get import errors and my environment variables don't work.
@@ -83,11 +109,25 @@ sudo MY_ENV=abc123 \
/srv/jupyterhub/jupyterhub
```
### How can I view the logs for JupyterHub or the user's Notebook servers when using the DockerSpawner?
Use `docker logs <container>` where `<container>` is the container name defined within `docker-compose.yml`. For example, to view the logs of the JupyterHub container use:
docker logs hub
By default, the user's notebook server is named `jupyter-<username>` where `username` is the user's username within JupyterHub's db. So if you wanted to see the logs for user `foo` you would use:
docker logs jupyter-foo
You can also tail logs to view them in real time using the `-f` option:
docker logs -f hub
## Errors
### Error 500 after spawning my single-user server
### 500 error after spawning my single-user server
You receive a 500 error while accessing the URL `/user/<your_name>/...`.
You receive a 500 error when accessing the URL `/user/<your_name>/...`.
This is often seen when your single-user server cannot verify your user cookie
with the Hub.
@@ -113,9 +153,9 @@ If everything is working, the response logged will be similar to this:
You should see a similar 200 message, as above, in the Hub log when you first
visit your single-user notebook server. If you don't see this message in the log, it
may mean that your single-user notebook server is not connecting to your Hub.
may mean that your single-user notebook server isn't connecting to your Hub.
If you see 403 (forbidden) like this, it is likely a token problem:
If you see 403 (forbidden) like this, it's likely a token problem:
```
403 GET /hub/api/authorizations/cookie/jupyterhub-token-name/[secret] (@10.0.1.4) 4.14ms
@@ -145,10 +185,10 @@ If you receive a 403 error, the API token for the single-user server is likely
invalid. Commonly, the 403 error is caused by resetting the JupyterHub
database (either removing jupyterhub.sqlite or some other action) while
leaving single-user servers running. This happens most frequently when using
DockerSpawner because Docker's default behavior is to stop/start containers
that reset the JupyterHub database, rather than destroying and recreating
DockerSpawner, because Docker's default behavior is to stop/start containers
which resets the JupyterHub database, rather than destroying and recreating
the container every time. This means that the same API token is used by the
server for its whole life until the container is rebuilt.
server for its whole life, until the container is rebuilt.
The fix for this Docker case is to remove any Docker containers seeing this
issue (typically all containers created before a certain point in time):
@@ -161,28 +201,28 @@ your server again.
##### Proxy settings (403 GET)
When your whole JupyterHub sits behind an organization proxy (_not_ a reverse proxy like NGINX as part of your setup and _not_ the configurable-http-proxy) the environment variables `HTTP_PROXY`, `HTTPS_PROXY`, `http_proxy`, and `https_proxy` might be set. This confuses the JupyterHub single-user servers: When connecting to the Hub for authorization they connect via the proxy instead of directly connecting to the Hub on localhost. The proxy might deny the request (403 GET). This results in the single-user server thinking it has the wrong auth token. To circumvent this you should add `<hub_url>,<hub_ip>,localhost,127.0.0.1` to the environment variables `NO_PROXY` and `no_proxy`.
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 as grading assignments.
- **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).
Standard JupyterHub installations include a [jupyterhub-singleuser](https://github.com/jupyterhub/jupyterhub/blob/9fdab027daa32c9017845572ad9d5ba1722dbc53/setup.py#L116) command which is built from the `jupyterhub.singleuser:main` method. The `jupyterhub-singleuser` command is the default command when JupyterHub launches single-user Jupyter Notebooks. One of the goals of this command is to make sure the version of JupyterHub installed within the Jupyter Notebook coincides with the version of the JupyterHub server itself.
If you launch a Jupyter Notebook with the `jupyterhub-singleuser` command directly from the command line, the Jupyter Notebook won't have access to the `JUPYTERHUB_API_TOKEN` and will return:
If you launch a Jupyter Notebook with the `jupyterhub-singleuser` command directly from the command line the Jupyter Notebook won't have access to the `JUPYTERHUB_API_TOKEN` and will return:
```
JUPYTERHUB_API_TOKEN env is required to run jupyterhub-singleuser.
Did you launch it manually?
```
If you plan on testing `jupyterhub-singleuser` independently from JupyterHub, then you can set the API token environment variable. For example, if you were to run the single-user Jupyter Notebook on the host, then:
If you plan on testing `jupyterhub-singleuser` independently from JupyterHub, then you can set the api token environment variable. For example, if were to run the single-user Jupyter Notebook on the host, then:
export JUPYTERHUB_API_TOKEN=my_secret_token
jupyterhub-singleuser
@@ -203,7 +243,7 @@ With a docker container, pass in the environment variable with the run command:
Some certificate providers, i.e. Entrust, may provide you with a chained
certificate that contains multiple files. If you are using a chained
certificate you will need to concatenate the individual files by appending the
chained cert and root cert to your host cert:
chain cert and root cert to your host cert:
cat your_host.crt chain.crt root.crt > your_host-chained.crt
@@ -216,7 +256,7 @@ You would then set in your `jupyterhub_config.py` file the `ssl_key` and
#### Example
Your certificate provider gives you the following files: `example_host.crt`,
`Entrust_L1Kroot.txt`, and `Entrust_Root.txt`.
`Entrust_L1Kroot.txt` and `Entrust_Root.txt`.
Concatenate the files appending the chain cert and root cert to your host cert:
@@ -235,7 +275,7 @@ 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
@@ -249,7 +289,7 @@ with npmbox:
python3 -m pip wheel jupyterhub
npmbox configurable-http-proxy
### I want access to the whole filesystem and still default users to their home directory
### I want access to the whole filesystem, but still default users to their home directory
Setting the following in `jupyterhub_config.py` will configure access to
the entire filesystem and set the default to the user's home directory.
@@ -268,7 +308,7 @@ similar to this one:
provides additional information. The [pySpark configuration documentation](https://spark.apache.org/docs/0.9.0/configuration.html)
is also helpful for programmatic configuration examples.
### How do I use JupyterLab's pre-release version with JupyterHub?
### How do I use JupyterLab's prerelease version with JupyterHub?
While JupyterLab is still under active development, we have had users
ask about how to try out JupyterLab with JupyterHub.
@@ -281,7 +321,7 @@ For instance:
python3 -m pip install jupyterlab
jupyter serverextension enable --py jupyterlab --sys-prefix
The important thing is that JupyterLab is installed and enabled in the
The important thing is that jupyterlab is installed and enabled in the
single-user notebook server environment. For system users, this means
system-wide, as indicated above. For Docker containers, it means inside
the single-user docker image, etc.
@@ -294,14 +334,14 @@ notebook servers to default to JupyterLab:
### How do I set up JupyterHub for a workshop (when users are not known ahead of time)?
1. Set up JupyterHub using OAuthenticator for GitHub authentication
2. Configure the admin list to have workshop leaders listed with administrator privileges.
2. Configure admin list to have workshop leaders be listed with administrator privileges.
Users will need a GitHub account to log in and be authenticated by the Hub.
Users will need a GitHub account to login and be authenticated by the Hub.
### How do I set up rotating daily logs?
You can do this with [logrotate](https://linux.die.net/man/8/logrotate),
or pipe to `logger` to use Syslog instead of directly to a file.
or pipe to `logger` to use syslog instead of directly to a file.
For example, with this logrotate config file:
@@ -322,9 +362,34 @@ Or use syslog:
jupyterhub | logger -t jupyterhub
## Troubleshooting commands
The following commands provide additional detail about installed packages,
versions, and system information that may be helpful when troubleshooting
a JupyterHub deployment. The commands are:
- System and deployment information
```bash
jupyter troubleshooting
```
- Kernel information
```bash
jupyter kernelspec list
```
- Debug logs when running JupyterHub
```bash
jupyterhub --debug
```
### Toree integration with HDFS rack awareness script
The Apache Toree kernel will have an issue when running with JupyterHub if the standard HDFS rack awareness script is used. This will materialize in the logs as a repeated WARN:
The Apache Toree kernel will an issue, when running with JupyterHub, if the standard HDFS
rack awareness script is used. This will materialize in the logs as a repeated WARN:
```bash
16/11/29 16:24:20 WARN ScriptBasedMapping: Exception running /etc/hadoop/conf/topology_script.py some.ip.address
@@ -347,47 +412,8 @@ In order to resolve this issue, there are two potential options.
Docker images can be found at the [JupyterHub organization on DockerHub](https://hub.docker.com/u/jupyterhub/).
The Docker image [jupyterhub/singleuser](https://hub.docker.com/r/jupyterhub/singleuser/)
provides an example single-user notebook server for use with DockerSpawner.
provides an example single user notebook server for use with DockerSpawner.
Additional single-user notebook server images can be found at the [Jupyter
Additional single user notebook server images can be found at the [Jupyter
organization on DockerHub](https://hub.docker.com/r/jupyter/) and information
about each image at the [jupyter/docker-stacks repo](https://github.com/jupyter/docker-stacks).
### How can I view the logs for JupyterHub or the user's Notebook servers when using the DockerSpawner?
Use `docker logs <container>` where `<container>` is the container name defined within `docker-compose.yml`. For example, to view the logs of the JupyterHub container use:
docker logs hub
By default, the user's notebook server is named `jupyter-<username>` where `username` is the user's username within JupyterHub's database.
So if you wanted to see the logs for user `foo` you would use:
docker logs jupyter-foo
You can also tail logs to view them in real-time using the `-f` option:
docker logs -f hub
## Troubleshooting commands
The following commands provide additional detail about installed packages,
versions, and system information that may be helpful when troubleshooting
a JupyterHub deployment. The commands are:
- System and deployment information
```bash
jupyter troubleshoot
```
- Kernel information
```bash
jupyter kernelspec list
```
- Debug logs when running JupyterHub
```bash
jupyterhub --debug
```

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

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