Integrating RStudio Workbench with Jupyter Notebooks on a Single Server
Overview
These steps describe how to integrate RStudio Workbench, formerly RStudio Server Pro, with Jupyter Notebooks
running on a single server.
This integration makes use of the Launcher in RStudio Workbench to spawn
Jupyter Notebook and JupyterLab sessions on a single node without the use of an
external resource manager.
Info
Launcher is a new feature of RStudio Workbench 1.2 that is only available
under named user licensing. RStudio Workbench 1.2 without Launcher is
available under existing server-based licensing. For questions about using
Launcher with RStudio Workbench, please contact sales@rstudio.com.
Prerequisites
This integration is intended to be performed on top of a base installation of
RStudio Workbench.
-
Add the following lines to the RStudio Workbench configuration file:
File: /etc/rstudio/rserver.conf
# Launcher Config
launcher-address=127.0.0.1
launcher-port=5559
launcher-sessions-enabled=1
launcher-default-cluster=Local
launcher-sessions-callback-address=http://127.0.0.1:8787
-
In the launcher-sessions-callback-address setting, you should change the
protocol and port if you are using HTTPS or a different port for RStudio
Workbench.
-
Add the following lines to the Launcher configuration file:
File: /etc/rstudio/launcher.conf
[server]
address=127.0.0.1
port=5559
server-user=rstudio-server
admin-group=rstudio-server
authorization-enabled=1
thread-pool-size=4
enable-debug-logging=1
[cluster]
name=Local
type=Local
-
Now that you have updated the file, you must restart the RStudio Server service:
Terminal
$ sudo rstudio-server restart
-
Once you have restarted RStudio Server, run the following command to restart
the RStudio Launcher service:
Terminal
$ sudo rstudio-launcher restart
Step 3. Install Python
- Use the Install Python steps to install the
following on the server:
- Python,
pip
-
virtualenv
Info
Our recommended installation instructions for Python allow you to make
multiple versions of Python available and avoid replacing existing versions
of Python when updating system packages.
Step 4. Install Jupyter Notebooks, JupyterLab, and Python packages
-
From the previous step, you should still have the PYTHON_VERSION environment
variable defined with the version of Python that you installed. If not, then do
then you can define this environment variable before proceed by running the following command
and replacing 3.7.7 with the version of Python that
you are using:
Terminal
$ export PYTHON_VERSION=3.7.7
-
Install Jupyter Notebooks, JupyterLab, and the notebook extensions for RStudio
Workbench and RStudio Connect:
Terminal
$ sudo /opt/python/${PYTHON_VERSION}/bin/pip install jupyter jupyterlab rsp_jupyter rsconnect_jupyter
-
Install and enable the Jupyter Notebook extensions:
Terminal
$ sudo /opt/python/${PYTHON_VERSION}/bin/jupyter-nbextension install --sys-prefix --py rsp_jupyter
$ sudo /opt/python/${PYTHON_VERSION}/bin/jupyter-nbextension enable --sys-prefix --py rsp_jupyter
$ sudo /opt/python/${PYTHON_VERSION}/bin/jupyter-nbextension install --sys-prefix --py rsconnect_jupyter
$ sudo /opt/python/${PYTHON_VERSION}/bin/jupyter-nbextension enable --sys-prefix --py rsconnect_jupyter
$ sudo /opt/python/${PYTHON_VERSION}/bin/jupyter-serverextension enable --sys-prefix --py rsconnect_jupyter
-
(Optional) Install supplemental Python packages:
Terminal
$ sudo /opt/python/${PYTHON_VERSION}/bin/pip install altair beautifulsoup4 \
cloudpickle cython dask gensim keras matplotlib nltk numpy pandas pillow \
pyarrow requests scipy scikit-image scikit-learn scrapy seaborn spacy \
sqlalchemy statsmodels tensorflow xgboost
Step 6. Restart RStudio Workbench and Launcher Services
Step 7. Test RStudio Workbench with Launcher and Jupyter Notebooks
Additional Documentation
For more information on RStudio Workbench and Jupyter Notebooks, reference the Jupyter section in the RStudio Workbench Administration
Guide.