Jupyter Configuration

Workbench

Configuration of the Jupyter feature is handled via the config file /etc/rstudio/jupyter.conf. The following table lists the various configuration options that are available to be specified in the jupyter.conf configuration file:

/etc/rstudio/jupyter.conf

Config Option	Description	Default Value
labs-enabled	Enables launching of JupyterLab sessions. Defaults to `0`, but new installs place `1` in the default version of `jupyter.conf`.	0, 1 for new installs
notebooks-enabled	Enables launching of Jupyter Notebook Classic sessions.	0 (disabled)
jupyter-exe	Path to the Jupyter executable.	“”
lab-command	The Jupyter command to run when starting a Lab session.	lab
lab-version	The version of Jupyter Lab being used. If set to `auto`, the version is automatically detected by running the Jupyter Lab version command on the Posit Workbench node. If Jupyter is being launched via the Launcher and is not installed on the Posit Workbench node, must set this to the version in use. Using different Jupyter versions for different sessions is not supported.	auto
lab-args	Arguments to be passed to the Jupyter Lab command. Note that this does not override the default value - if you wish to use some of the default arguments, you must contain the default arguments in the configuration value. It is strongly recommended that you do not change this unless you know what you’re doing!	–no-browser –allow-root –ip=0.0.0.0
notebook-command	The Jupyter command to run when starting a Jupyter Notebook Classic session.	notebook
notebook-version	The version of Jupyter Notebook being used. If set to `auto`, the version is automatically detected by running the Jupyter Notebook version command. If Jupyter is being launched via the Launcher and is not installed locally, you are strongly encouraged to set this to the version in use. Running against multiple different Jupyter versions simultaneously in different Launcher clusters is not supported - they must all be running the same version, or proxying issues will occur.	auto
notebook-args	Arguments to be passed to the Jupyter Notebook command. Note that this does not override the default value - if you wish to use some of the default arguments, you must contain the default arguments in the configuration value. It is strongly recommended that you do not change this unless you know what you’re doing!	–no-browser –allow-root –ip=0.0.0.0
session-clusters	Deprecated in favor of `launcher-sessions-clusters`. List of available Job Launcher clusters for launching Jupyter sessions.	`launcher-sessions-clusters` in `rserver.conf`
default-session-cluster	Deprecated in favor of `launcher-default-cluster`. The default Job Launcher cluster to use when launching a Jupyter session.	`launcher-default-cluster` in `rserver.conf`
default-session-container-image	The default container image to use when launching a containerized Jupyter session.
session-container-images	Comma-delimited list of images that may be used for running Jupyter sessions.
jupyter-session-path	Path to the Jupyter Session launcher executable/script. It is recommended that you do not change this unless you know what you’re doing, and you need to point to a different script.	/usr/lib/rstudio-server/bin/jupyter-session-run
session-no-profile	Enables/disables running of bash profile scripts when starting Jupyter sessions.	0 (run profile scripts)
session-cull-minutes	The amount of idle time to wait in minutes before a Jupyter session’s kernels and terminals are automatically “culled” (shut down). To disable culling, set the value to `0`. Enabling this sets the following Jupyter arguments: `--MappingKernelManager.cull_interval=60 --MappingKernelManager.cull_idle_timeout=<60 * session-cull-minutes value>`	120, however, it is highly recommend that you set this 0 to disable culling. See note below.
session-shutdown-minutes	The amount of idle time to wait in minutes before a Jupyter session is shut down after its kernels and terminals have been culled. To disable automatic shutdown, set the value to `0`. Sets the following Jupyter arguments: `--NotebookApp.shutdown_no_activity_timeout=<60 * session-shutdown-minutes value> --LapApp.shutdown_no_activity_timeout=<60 * session-shutdown-minutes value>`	5
session-cull-connected	Whether or not to cull sessions that have a browser connected. Regardless of this setting, only idle (unused) sessions are culled. Sets the `--MappingKernelManager.cull_connected` Jupyter setting.	1 (true)

Warning

It is highly recommended that you disable session culling by setting session-cull-minutes=0. When the server shuts down Jupyter sessions, users are not given a clear indicator that the session has ended. They may continue to work in the session, losing all changes made after the session shutdown.

For example, your jupyter.conf file might look like the following:

# /etc/rstudio/jupyter.conf
jupyter-exe=/usr/bin/jupyter
labs-enabled=1
session-cull-minutes=0
default-session-cluster=Kubernetes
default-session-container-image=rstudio:jupyter-session

Jupyter versions

Due to differences between Jupyter versions, Posit Workbench needs to know the versions of JupyterLab and Jupyter Notebook, when running Jupyter Notebook Classic, in use. They can be automatically detected by Posit Workbench on start up by running the jupyter command, or can be specified by using the lab-version and notebook-version settings in /etc/rstudio/jupyter.conf. Automatic version detection is done if the default value of auto is used.

If you are running Jupyter in a remote cluster like Kubernetes, you will need to manually specify the version of Jupyter if it is not locally installed or is not running the same version as the remote cluster.

To run multiple versions of Jupyter across different clusters, follow the instructions at Varied installation locations.

You should specify a full Jupyter 3-part version number like so:

# /etc/rstudio/jupyter.conf
lab-version=4.2.5

Varied installation locations

In some cases, the path to jupyter might be different depending on where the JupyterLab or Jupyter Notebook session is running. The path to jupyter might vary from container to container. By specifying a WORKBENCH_JUPYTER_PATH environment variable in your container build, you can ensure that the desired Jupyter installation will be executed by Workbench. The WORKBENCH_JUPYTER_PATH variable can also be set in the launcher-env file as described in the Launcher environment section. Workbench will resolve which jupyter executable should be used with the following priority:

The value of the WORKBENCH_JUPYTER_PATH environment variable, if set.
The value of exe as defined in jupyter.conf.
A jupyter installation that is on the PATH.

If there is no jupyter at any of those locations, JupyterLab and Jupyter Notebook sessions will fail to start.

Note

Please note that jupyter must still be installed at the location configured in jupyter.conf on the Workbench host.

Launcher Configuration

When creating containerized Jupyter sessions via the Job Launcher, you will need to specify mount points as appropriate to mount the users’ home drives and any other desired paths. In order for sessions to run properly within containers, it is required to mount the home directories into the containers.

For more information, see Launcher mounts.

Note

If you are using NFSv3 for the shared storage for user home directories, you will need to set the local_lock=all NFS setting in /etc/fstab in order for Jupyter sessions to work properly. Failure to do this will cause Jupyter sessions to hang. For this reason, we strongly recommend the use of NFSv4 for Jupyter sessions. See the NFS documentation for information on NFS settings.

Note

Only a user’s home folder is visible within Jupyter sessions. To access other folders, create symbolic links to them within the home folder.

Container configuration

When running Jupyter sessions in containers, such as by using the Kubernetes Job Launcher plugin, you will need to ensure that the image(s) used to launch Jupyter sessions contain, at minimum, the following:

Python 3.9+
JupyterLab 4.x and/or Jupyter Notebook 6.x or 7.x
Workbench session binaries

For containers within Kubernetes, we recommend using the rstudio/workbench-session Docker image as a base for any Jupyter session images you create. This will allow you to use one base image to provide Jupyter itself and a default version of Python, which you can extend if necessary to add or modify Jupyter and Python versions if desired. These images are available on Docker Hub.

When using the rstudio/workbench-session image on Kubernetes, the session init container should be enabled to ensure that Workbench session components are copied to the session container at runtime:

/etc/rstudio/rserver.conf

launcher-sessions-auto-update=1

See the Session init container section for more information.

When using a container-based job system other than Kubernetes, we recommend using the rstudio/r-session-complete Docker image. This image includes the Workbench session components in the image directly. This image is available on Docker Hub.

Installing the Posit Workbench JupyterLab Extension

Posit Workbench JupyterLab Sessions should be configured to run with the Posit Workbench JupyterLab extension. The extension provides JupyterLab sessions with a Posit Workbench home button and allows users to access remote web servers they are developing in their JupyterLab extensions through their browsers.

If a user launches a JupyterLab Session without the extension installed, Posit Workbench will attempt to install the extension while launching the session. In most configurations, the user will not have write access to the Python environment, and the extension will only be installed for that individual user. To provide the best user experience, admins should pre-install the extension before users run JupyterLab sessions. If you are going to rely on Workbench to install the extension, it must be able to download from www.pypi.org.

The JupyterLab extension can be installed using pip, and it must be installed within the same Python environment that JupyterLab is installed in. Additionally, the extension must be installed on the node that hosts the user’s sessions (e.g. ,the docker image used by a pod in a Kubernetes cluster). When configuring JupyterLab 4 environments, install the extension pwb_jupyterlab.

JupyterLab 3 is deprecated

JupyterLab 3 reached its end of maintenance date on May 15, 2024. Given the JupyterLab council is no longer maintaining this older version, Posit Workbench now requires JupyterLab 4.

Installing for JupyterLab 4

To install the extension in a JupyterLab 4 environment, run the following command as a user with write permissions to the Python directory (you may need to include sudo):

    pip install pwb_jupyterlab~=1.0

To read more about the JupyterLab 4 extension and check the latest version number, visit https://pypi.org/project/pwb-jupyterlab/.

Note

If the extension hasn’t been installed when the user launches a session, they can install it themselves using the same pip command. However, the install will not fully complete until they launch their next JupyterLab session. When the extension is installed within an active JupyterLab session, only its front-end components are installed for that session, and server proxying will not work properly.

Adding Python environments to Jupyter

You can add new Python virtual environments or conda environments to Jupyter as Python Kernels that users can select, allowing them to have flexibility in the Python versions they can use.

The following steps show how to add a new Python virtualenv as a Kernel to Jupyter:

Create the virtualenv or conda environment
Add the environment as an iPython kernel
Verify the kernel is available in Jupyter

# first, create the virtual env in a directory accessible by all users
cd /opt/python-kernels

# then make the virtualenv
virtualenv myenv

# alternatively, you can make a conda environment
# conda create -n myenv

# then, add the virtualenv to the Kernels list globally for all users
sudo ipython kernel install --name "myenv" --display-name "My Python Environment"

# finally, verify that the kernel is registered with Jupyter
sudo jupyter kernelspec list

Once the Kernel is listed in the output of the jupyter kernelspec list command, it will be available for use in both JupyterLab and Jupyter Notebooks.

To remove the kernel, simply run the following command:

sudo jupyter kernelspec remove "myenv"

For more information, see the IPython docs.

Installing additional JupyterLab extensions

Administrators can globally install JupyterLab extensions for all users using pip install.

For example, to install the jupyterlab-kernelspy extension using pip:

pip install jupyterlab-kernelspy

Note

You will need to install the desired extensions on all Launcher nodes that are running JupyterLab sessions. For containerized sessions, the extension must be installed in the container image(s) that are used.

There is no need to explicitly enable or disable the extension. Once installed, active sessions can see the extension after refreshing the browser. Other extensions can be found on Pypi.org.

In previous versions of JupyterLab, you needed to install packages from source and build them with nodejs. This flow is still supported, but installing prebuilt extensions via pip is much simpler. For more information, see the JupyterLab extension docs.