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https://github.com/jupyterhub/systemdspawner

Spawn JupyterHub single-user notebook servers with systemd
https://github.com/jupyterhub/systemdspawner

jupyter jupyterhub systemd systemd-spawner

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Spawn JupyterHub single-user notebook servers with systemd

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README 

        
**[Features](#features)** |

**[Requirements](#requirements)** |

**[Installation](#installation)** |

**[Configuration](#configuration)** |

**[Getting help](#getting-help)** |

**[License](#license)**



# systemdspawner



[![Latest PyPI version](https://img.shields.io/pypi/v/jupyterhub-systemdspawner?logo=pypi)](https://pypi.python.org/pypi/jupyterhub-systemdspawner)

[![Latest conda-forge version](https://img.shields.io/conda/vn/conda-forge/jupyterhub-systemdspawner?logo=conda-forge)](https://anaconda.org/conda-forge/jupyterhub-systemdspawner)

[![GitHub Workflow Status - Test](https://img.shields.io/github/actions/workflow/status/jupyterhub/systemdspawner/test.yaml?logo=github&label=tests)](https://github.com/jupyterhub/systemdspawner/actions)

[![Test coverage of code](https://codecov.io/gh/jupyterhub/systemdspawner/branch/main/graph/badge.svg)](https://codecov.io/gh/jupyterhub/systemdspawner)

[![GitHub](https://img.shields.io/badge/issue_tracking-github-blue?logo=github)](https://github.com/jupyterhub/systemdspawner/issues)

[![Discourse](https://img.shields.io/badge/help_forum-discourse-blue?logo=discourse)](https://discourse.jupyter.org/c/jupyterhub)



The **systemdspawner** enables JupyterHub to spawn single-user

notebook servers using [systemd](https://www.freedesktop.org/wiki/Software/systemd/).



## Features



If you want to use Linux Containers (Docker, rkt, etc) for isolation and

security benefits, but don't want the headache and complexity of

container image management, then you should use the SystemdSpawner.



With the **systemdspawner**, you get to use the familiar, traditional system

administration tools, whether you love or meh them, without having to learn an

extra layer of container related tooling.



The following features are currently available:



1. Limit maximum memory permitted to each user.



   If they request more memory than this, it will not be granted (`malloc`

   will fail, which will manifest in different ways depending on the

   programming language you are using).



2. Limit maximum CPU available to each user.



3. Provide fair scheduling to users independent of the number of processes they

   are running.



   For example, if User A is running 100 CPU hogging processes, it will usually

   mean User B's 2 CPU hogging processes will never get enough CPU time as scheduling

   is traditionally per-process. With Systemd Spawner, both these users' processes

   will as a whole get the same amount of CPU time, regardless of number of processes

   being run. Good news if you are User B.



4. Accurate accounting of memory and CPU usage (via cgroups, which systemd uses internally).



   You can check this out with `systemd-cgtop`.



5. `/tmp` isolation.



   Each user gets their own `/tmp`, to prevent accidental information

   leakage.



6. Spawn notebook servers as specific local users on the system.



   This can replace the need for using SudoSpawner.



7. Restrict users from being able to sudo to root (or as other users) from within the

   notebook.



   This is an additional security measure to make sure that a compromise of

   a jupyterhub notebook instance doesn't allow root access.



8. Restrict what paths users can write to.



   This allows making `/` read only and only granting write privileges to

   specific paths, for additional security.



9. Automatically collect logs from each individual user notebook into

   `journald`, which also handles log rotation.



10. Dynamically allocate users with Systemd's [dynamic users](http://0pointer.net/blog/dynamic-users-with-systemd.html)

    facility. Very useful in conjunction with [tmpauthenticator](https://github.com/jupyterhub/tmpauthenticator).



## Requirements



### Systemd and Linux distributions



SystemdSpawner 1 is recommended to be used with systemd version 245 or higher,

but _may_ work with systemd version 243-244 as well. Below are examples of Linux

distributions that use systemd and has a recommended version.



- Ubuntu 20.04+

- Debian 11+

- Rocky 9+ / CentOS 9+



The command `systemctl --version` can be used to verify that systemd is used,

and what version is used.



### Kernel Configuration



Certain kernel options need to be enabled for the CPU / Memory limiting features

to work. If these are not enabled, CPU / Memory limiting will just fail

silently. You can check if your kernel supports these features by running

the [`check-kernel.bash`](check-kernel.bash) script.



### Root access



Currently, JupyterHub must be run as root to use Systemd Spawner. `systemd-run`

needs to be run as root to be able to set memory & cpu limits. Simple sudo rules

do not help, since unrestricted access to `systemd-run` is equivalent to root. We

will explore hardening approaches soon.



### Local Users



If running with `c.SystemdSpawner.dynamic_users = False` (the default), each user's

server is spawned to run as a local unix user account. Hence this spawner

requires that all users who authenticate have a local account already present on the

machine.



If running with `c.SystemdSpawner.dynamic_users = True`, no local user accounts

are required. Systemd will automatically create dynamic users as required.

See [this blog post](http://0pointer.net/blog/dynamic-users-with-systemd.html) for

details.



## Installation



You can install it from PyPI with:



```bash

pip install jupyterhub-systemdspawner

```



You can enable it for your jupyterhub with the following lines in your

`jupyterhub_config.py` file



```python

c.JupyterHub.spawner_class = "systemd"

```



Note that to confirm systemdspawner has been installed in the correct jupyterhub

environment, a newly generated config file should list `systemdspawner` as one of the

available spawner classes in the comments above the configuration line.



## Configuration



Lots of configuration options for you to choose! You should put all of these

in your `jupyterhub_config.py` file:



- **[`mem_limit`](#mem_limit)**

- **[`cpu_limit`](#cpu_limit)**

- **[`user_workingdir`](#user_workingdir)**

- **[`username_template`](#username_template)**

- **[`default_shell`](#default_shell)**

- **[`extra_paths`](#extra_paths)**

- **[`unit_name_template`](#unit_name_template)**

- **[`unit_extra_properties`](#unit_extra_properties)**

- **[`isolate_tmp`](#isolate_tmp)**

- **[`isolate_devices`](#isolate_devices)**

- **[`disable_user_sudo`](#disable_user_sudo)**

- **[`readonly_paths`](#readonly_paths)**

- **[`readwrite_paths`](#readwrite_paths)**

- **[`dynamic_users`](#dynamic_users)**



### `mem_limit`



Specifies the maximum memory that can be used by each individual user. It can be

specified as an absolute byte value. You can use the suffixes `K`, `M`, `G` or `T` to

mean Kilobyte, Megabyte, Gigabyte or Terabyte respectively. Setting it to `None` disables

memory limits.



Even if you want individual users to use as much memory as possible, it is still good

practice to set a memory limit of 80-90% of total physical memory. This prevents one

user from being able to single handedly take down the machine accidentally by OOMing it.



```python

c.SystemdSpawner.mem_limit = '4G'

```



Defaults to `None`, which provides no memory limits.



This info is exposed to the single-user server as the environment variable

`MEM_LIMIT` as integer bytes.



### `cpu_limit`



A float representing the total CPU-cores each user can use. `1` represents one

full CPU, `4` represents 4 full CPUs, `0.5` represents half of one CPU, etc.

This value is ultimately converted to a percentage and rounded down to the

nearest integer percentage, i.e. `1.5` is converted to 150%, `0.125` is

converted to 12%, etc.



```python

c.SystemdSpawner.cpu_limit = 4.0

```



Defaults to `None`, which provides no CPU limits.



This info is exposed to the single-user server as the environment variable

`CPU_LIMIT` as a float.



Note: there is [a bug](https://github.com/systemd/systemd/issues/3851) in

systemd v231 which prevents the CPU limit from being set to a value greater

than 100%.



#### CPU fairness



Completely unrelated to `cpu_limit` is the concept of CPU fairness - that each

user should have equal access to all the CPUs in the absense of limits. This

does not entirely work in the normal case for Jupyter Notebooks, since CPU

scheduling happens on a per-process level, rather than per-user. This means

a user running 100 processes has 100x more access to the CPU than a user running

one. This is far from an ideal situation.



Since each user's notebook server runs in its own Systemd Service, this problem

is mitigated - all the processes spawned from a user's notebook server are run

in one cgroup, and cgroups are treated equally for CPU scheduling. So independent

of how many processes each user is running, they all get equal access to the CPU.

This works out perfect for most cases, since this allows users to burst up and

use all CPU when nobody else is using CPU & forces them to automatically yield

when other users want to use the CPU.



### `user_workingdir`



The directory to spawn each user's notebook server in. This directory is what users

see when they open their notebooks servers. Usually this is the user's home directory.



`{USERNAME}` and `{USERID}` in this configuration value will be expanded to the

appropriate values for the user being spawned.



```python

c.SystemdSpawner.user_workingdir = '/home/{USERNAME}'

```



Defaults to the home directory of the user. Not respected if `dynamic_users` is true.



### `username_template`



Template for unix username each user should be spawned as.



`{USERNAME}` and `{USERID}` in this configuration value will be expanded to the

appropriate values for the user being spawned.



This user should already exist in the system.



```python

c.SystemdSpawner.username_template = 'jupyter-{USERNAME}'

```



Not respected if `dynamic_users` is set to True



### `default_shell`



The default shell to use for the terminal in the notebook. Sets the `SHELL` environment

variable to this.



```python

c.SystemdSpawner.default_shell = '/bin/bash'

```



Defaults to whatever the value of the `SHELL` environment variable is in the JupyterHub

process, or `/bin/bash` if `SHELL` isn't set.



### `extra_paths`



List of paths that should be prepended to the `PATH` environment variable for the spawned

notebook server. This is easier than setting the `env` property, since you want to

add to PATH, not completely replace it. Very useful when you want to add a virtualenv

or conda install onto the user's `PATH` by default.



```python

c.SystemdSpawner.extra_paths = ['/home/{USERNAME}/conda/bin']

```



`{USERNAME}` and `{USERID}` in this configuration value will be expanded to the

appropriate values for the user being spawned.



Defaults to `[]` which doesn't add any extra paths to `PATH`



### `unit_name_template`



Template to form the Systemd Service unit name for each user notebook server. This

allows differentiating between multiple jupyterhubs with Systemd Spawner on the same

machine. Should contain only [a-zA-Z0-9_-].



```python

c.SystemdSpawner.unit_name_template = 'jupyter-{USERNAME}-singleuser'

```



`{USERNAME}` and `{USERID}` in this configuration value will be expanded to the

appropriate values for the user being spawned.



Defaults to `jupyter-{USERNAME}-singleuser`



### `unit_extra_properties`



Dict of key-value pairs used to add arbitrary properties to the spawned Jupyerhub units.



```python

c.SystemdSpawner.unit_extra_properties = {'LimitNOFILE': '16384'}

```



Read `man systemd-run` for details on per-unit properties available in transient units.



`{USERNAME}` and `{USERID}` in each parameter value will be expanded to the

appropriate values for the user being spawned.



Defaults to `{}` which doesn't add any extra properties to the transient scope.



### `isolate_tmp`



Setting this to true provides a separate, private `/tmp` for each user. This is very

useful to protect against accidental leakage of otherwise private information - it is

possible that libraries / tools you are using create /tmp files without you knowing and

this is leaking info.



```python

c.SystemdSpawner.isolate_tmp = True

```



Defaults to false.



### `isolate_devices`



Setting this to true provides a separate, private `/dev` for each user. This prevents the

user from directly accessing hardware devices, which could be a potential source of

security issues. `/dev/null`, `/dev/zero`, `/dev/random` and the ttyp pseudo-devices will

be mounted already, so most users should see no change when this is enabled.



```python

c.SystemdSpawner.isolate_devices = True

```



Defaults to false.



### `disable_user_sudo`



Set to true, this prevents users from being able to use `sudo` (or any other means) to

become other users (including root). This helps contain damage from a compromise of a user's

credentials if they also have sudo rights on the machine - a web based exploit will now only

be able to damage the user's own stuff, rather than have complete root access.



```python

c.SystemdSpawner.disable_user_sudo = True

```



Defaults to True.



### `readonly_paths`



List of filesystem paths that should be mounted readonly for the users' notebook server. This

will override any filesystem permissions that might exist. Subpaths of paths that are mounted

readonly can be marked readwrite with `readwrite_paths`. This is useful for marking `/` as

readonly & only whitelisting the paths where notebook users can write. If paths listed here

do not exist, you will get an error.



```python

c.SystemdSpawner.readonly_paths = ['/']

```



`{USERNAME}` and `{USERID}` in this configuration value will be expanded to the

appropriate values for the user being spawned.



Defaults to `None` which disables this feature.



### `readwrite_paths`



List of filesystem paths that should be mounted readwrite for the users' notebook server. This

only makes sense if `readonly_paths` is used to make some paths readonly - this can then be

used to make specific paths readwrite. This does _not_ override filesystem permissions - the

user needs to have appropriate rights to write to these paths.



```python

c.SystemdSpawner.readwrite_paths = ['/home/{USERNAME}']

```



`{USERNAME}` and `{USERID}` in this configuration value will be expanded to the

appropriate values for the user being spawned.



Defaults to `None` which disables this feature.



### `dynamic_users`



Allocate system users dynamically for each user.



Uses the DynamicUser= feature of Systemd to make a new system user

for each hub user dynamically. Their home directories are set up

under /var/lib/{USERNAME}, and persist over time. The system user

is deallocated whenever the user's server is not running.



See http://0pointer.net/blog/dynamic-users-with-systemd.html for more

information.



### `slice`



Run the spawned notebook in a given systemd slice. This allows aggregate configuration that

will apply to all the units that are launched. This can be used (for example) to control

the total amount of memory that all of the notebook users can use.



See https://samthursfield.wordpress.com/2015/05/07/running-firefox-in-a-cgroup-using-systemd/ for

an example of how this could look.



For detailed configuration see the [manpage](http://man7.org/linux/man-pages/man5/systemd.slice.5.html)



## Getting help



We encourage you to ask questions in the [Jupyter Discourse forum](https://discourse.jupyter.org/c/jupyterhub).



## License



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.