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RStudio Connect offers a flexible way of deploying Python APIs, web apps such as Dash and Streamlit, Jupyter Notebooks, and reticulated R content against a variety of Python versions.

A compatible version of Python is identified when content is deployed. That Python installation is used any time Python is needed for that content. Package installation and rendering content that utilized Python will use the version of Python discovered at deploy-time.

RStudio Connect allows different content to rely on different versions of Python. For example, Alice's Flask API may require version 2.7.17 of Python while Bob's Shiny application with reticulate needs Python version 3.8.1. Those two deployments using different Python versions can coexist in RStudio Connect without conflict.

This chapter discusses how to install Python, how RStudio Connect can be configured to support one or more versions of Python, how Python version compatibility is determined, and other configuration settings relevant to Python support in RStudio Connect.

Available Python installations are analyzed at startup. RStudio Connect logs the provided Python versions and whether it can use each installation.

Changing any of the configuration items discussed in this chapter requires a restart of RStudio Connect.

Additional Requirements#

Each Python installation is required to have the pip and virtualenv Python packages installed. virtualenv is used to create content-specific environments and pip is used to install Python packages.


The sunsetting of Python 2 has made it more challenging to build a new environment with Python 2 compatibility. We recommend using conda to install the virtualenv package into Python 2 environments.

Here is an command that can be used to install virtualenv after following our instructions to Install Python:

# Install virtualenv into an already-available Python 2.7.18 installation.
/opt/miniconda/bin/conda install --prefix=/opt/python/2.7.18 --channel conda-forge python=2.7.18 virtualenv

Additionally, Python APIs and web apps built on WSGI require the libev development package to be installed on your OS.

  • On CentOS * and Red Hat Enterprise Linux:

    sudo yum install libev-devel

  • On Ubuntu:

    sudo apt install libev-dev

  • On SUSE:

    sudo zypper install libev-devel

Enabling Python Support#

Python support can enabled by setting the Python.Enabled property to true. Note that it is false by default.

; /etc/rstudio-connect/rstudio-connect.gcfg
Enabled = true

RStudio Connect also needs to be configured to use a specific Python installation in order to fully support Python content.

The Python.Executable property can be used to specify locations for Python installations. Specify this property once for each Python installation directory.

; /etc/rstudio-connect/rstudio-connect.gcfg
Enabled = true
Executable = /shared/Python/2.7.17/bin/python
Executable = /shared/Python/3.7.6/bin/python3
Executable = /shared/Python/3.8.1/bin/python3


The Python.Executable property is permitted to be a symbolic link to a Python installation.

Installing Python#

The minimum Python 2 version supported is 2.7.9 and the minimum Python 3 supported version is 3.4.0.

See Installing Python to learn how to install Python.


We do not recommend installing Python with a package manager like apt, yum, or zypper.

Once you have Python installed on the server, it is important to understand how Connect discovers Python and chooses the Python version that an asset will use. Python Version Matching explains more about this process.

Upgrading Python#

We strongly recommend supporting multiple versions of Python instead of upgrading and maintaining a single version of Python. Supporting multiple versions of Python is the best way to ensure applications or reports published with specific package dependencies will continue to run.

After installing another Python version:

  1. Stop RStudio Connect

  2. Add an additional Python.Executable property to the configuration file that points to the newly built Python installation.

  3. Start RStudio Connect

Following an upgrade, content dependent on Python will be rebuilt on-demand. For example, during the next execution of a scheduled Jupyter Notebook, RStudio Connect will automatically reinstall and rebuild all of the necessary packages before rendering the report, if needed. During the package updates, RStudio Connect will present a message and spinner indicating to the end user that the rendered Jupyter Notebook will be available after the packages are successfully installed and built for the new version of Python.


Not all packages can be reinstalled and rebuilt on newer versions of Python. Rebuilding and restoring packages can take a significant amount of time and could delay or prevent the rendering of a Jupyter Notebook or reticulated R content.

Python Version Matching#

RStudio Connect attempts to find a Python installation that is appropriate for your content. By default, it applies a "major-minor" matching approach. This algorithm attempts to find a version of Python to use with your content. More deployments will succeed but not always with the same version of Python that is used by the author.

If you would prefer a strict association between authored and deployed Python versions, you can choose to use an "exact" matching approach.

The "major-minor" algorithm requires exact MAJOR.MINOR matching but is flexible about the patch level. This is a useful option when your desktop and server may occasionally have different update cycles when installing bug fix releases.

e.g. If content was deployed with Python 3.4.0 and RStudio Connect is configured with Python 3.4.3, then Python 3.4.3 will be used as the MAJOR.MINOR numbers match (3.4 and 3.4) but if RStudio Connect is configured with only Python 3.5.6 then the content will fail to deploy as the MAJOR.MINOR versions are different (3.4 v. 3.5).

An inconsistent version of Python occasionally causes problems when installing package dependencies. For the best results, make sure that RStudio Connect has access to the same versions of Python used to author content.

The Python version matching approach is controlled with the Python.VersionMatching configuration setting.


Find a Python installation that is close to the version of Python used when authoring content, requiring an exact MAJOR.MINOR version match. If a compatible version cannot be found, content will fail to deploy.

  1. Use exact version match.

  2. If there are matching MAJOR.MINOR releases, use least-greater version.

  3. If there are matching MAJOR.MINOR releases, use latest of these.


Finds a Python installation that exactly matches the version of Python used when authoring the deployed content. If a matching version cannot be found, content will fail to deploy.


The table below shows some examples of how the matching algorithm takes the version of Python used in your development environment and chooses an appropriate Python version from the ones available to RStudio Connect.

Matching Algorithm Development Version Server Versions Used Version Reason
major-minor 3.5.6 3.5.4, 3.5.6, 3.5.9 3.5.6 Chose exact match
major-minor 3.7.3 3.7.1, 3.7.5, 3.7.6 3.7.5 Chose lowest 3.7.X release after 3.7.3
major-minor 3.6.10 3.6.4, 3.6.9, 3.7.2 3.6.9 Chose the latest 3.6.X release since all are before 3.6.10
exact 3.5.6 3.5.6, 3.5.7 3.5.6 Chose exact match
exact 3.5.6 3.5.4, 3.5.7 error no match