Integrating RStudio Workbench with Jupyter Notebooks, Launcher, and Kubernetes#
These steps describe how to integrate RStudio Workbench, formerly RStudio Server Pro1, with Jupyter Notebooks running with Launcher and Kubernetes.
The most recent
rstudio/r-session-complete Docker images referenced in these
steps include Python and Jupyter.
Launcher is a new feature of RStudio Server Pro 1.22 that is only available under named user licensing. RStudio Server Pro 1.2 without Launcher is available under existing server-based licensing.
For questions about using Launcher with RStudio Workbench, please contact email@example.com.
This integration is intended to be performed on top of an installation of RStudio Workbench that has already been configured with Launcher and Kubernetes.
Step 1. Configure Launcher with Jupyter Notebooks#
Add the following lines to the Launcher Jupyter configuration file:File: /etc/rstudio/jupyter.conf
jupyter-exe=/opt/python/3.7.7/bin/jupyter notebooks-enabled=1 labs-enabled=1 default-session-cluster=Kubernetes default-session-container-image=rstudio/r-session-complete:centos7-2021.09.0-351.pro6
Step 2. Restart RStudio Workbench and Launcher Services#
Restart the services:Terminal
$ sudo rstudio-server restart $ sudo rstudio-launcher restart
Step 3. Test RStudio Workbench with Launcher and Jupyter Notebooks#
- From your browser, navigate to the RStudio Workbench interface and log in.
- Select New Session and do the following:
- Give your session a name.
- In the Editor field, select either Jupyter Notebooks or JupyterLab as the IDE.
- Click Start Session.
Now, you can use the Jupyter Notebooks or JupyterLab interfaces.
Some local Jupyter configurations may prevent the JupyterLab session from correctly launching in RStudio Workbench. For example, setting a password in the files
~/jupyter/jupyter-server-config.py, will cause the JupyterLab session to start but not load through the RStudio Workbench interface. Commenting out the configuration in question is sufficient to restore expected functionality.
(Optional) Configure multiple Python versions or environments#
The Python integration steps described above result in a single Python environment that contains both core packages for Jupyter Notebooks as well as Python packages for end users.
While this is a simple approach, this setup can result in issues if end users want to use different versions of the same package or if some packages conflict with core packages for Jupyter Notebooks.
If you would like to use multiple versions of Python or different Python environments, or if you want to install Jupyter Notebook in a separate environment from Python packages for end users, then you can refer to the documentation for using multiple Python versions and environments with Jupyter.
Troubleshooting RStudio Workbench and Jupyter#
Refer to the support article on troubleshooting Jupyter Notebooks in RStudio Workbench for additional information on troubleshooting RStudio Workbench with Jupyter.
For more information on RStudio Workbench and Launcher, refer to the following reference documentation:
- Launcher section in the RStudio Workbench Administration Guide
- Jupyter section in the RStudio Workbench Administration Guide
- Launcher Administration Guide
We have renamed RStudio Server Pro to RStudio Workbench. This change reflects the product’s growing support for a wide range of different development environments. Please see our official Announcement and review our FAQ regarding the name change from RStudio Server Pro to RStudio Workbench. ↩
We will continue to use the RStudio Server Pro name for references to versions prior to 1.4. ↩