Deploying Flask Applications to RStudio Connect with Git and rsconnect-python


Beginning in RStudio Connect 1.8.2+ it is possible to deploy and manage runtime settings for Flask applications. 

The rsconnect-python package provides a command-line interface (CLI) based workflow that enables publishing Flask applications from the command line or continuous integration workflows.  The package requires an API key associated with an operational installation of RStudio Connect.

This workflow provides a Python-only user the ability to create, publish, and update Flask applications running in RStudio Connect. Flask is a popular Python framework typically used to create standalone web applications or REST-based HTTP APIs. 

In this example, we'll deploy the getting started example provided by the Flask documentation to RStudio Connect.

A pure Python approach to deploying Flask applications to RStudio Connect is possible by leveraging Git and the environment manifest (manifest.json) created by rsconnect-python.



For this example to work, prerequisite configurations and setup include:



If needed, rsconnect-python is available on PyPI.  To install with pip, run the following command:

pip install rsconnect-python


Git backed deployment with rsconnect-python

In addition to deploying from the command line, rsconnect-python can generate a manifest.json file that RStudio Connect can recognize to deploy directly from a Git repository.

This is beneficial for hardened deployment workflows and those that may require deployment as a successor to a continuous integration pipeline or testing suite.

The workflow is as follows:

  • Create an application structure
  • Initialize Git
  • Generate manifest.json using rsconnect-python
  • Commit changes
  • Import from Git in RStudio Connect
  • Regenerate and commit manifest.json, if necessary
  • Trigger deploy in RStudio Connect or set to Check for updates periodically

If a virtual environment is used then it is encouraged to continue to do so. Integrate the above steps into your already functional workflow.

A requirements.txt file along with a manifest.json are mandatory for RStudio Connect to deploy content from a git repository. It is best practice to create and validate the requirements.txt although rsconnect-python will create one if not present.


Initialize git

To leverage Git backed deployment, a git repository must be initialized within the working directory of the project. This will provide a mechanism for tracking changes to the code and also allow the repository to be pushed to a remote git server, if necessary.

In the working directory, run:

git init

Also, add a .gitignore file into the directory.

+ └─ .gitignore


Create the Flask application structure

The application structure below is the minimal base structure required for deployment.

+ ├─
+ ├─ requirements.txt
  └─ .gitignore

Typically, a requirements.txt file is generated using pip or another package manager.

If pip is used, then the following command will create the correct file:

pip freeze > requirements.txt

It is necessary to write a new manifest each time the application dependencies change. This instructs RStudio Connect to update the environment with the modified requirements.


Generate application manifest

Generate the manifest.json by running the following command within the active environment that is configured for use with rsconnect-python:

rsconnect write-manifest api .

This will create a manifest.json file in the working directory. Note, there are additional options available in rsconnect-python for specifying custom application entrypoint and directory locations.

Once executed, the working directory will contain the following files and directory.

+ ├─ manifest.json
  ├─ .gitignore
  └─ requirements.txt


Inspecting manifest.json

The JSON  content in the new manifest file will be similar to the below snippet: 

{ "version": 1, "metadata": { "appmode": "python-api", "entrypoint": "app:app" }, "locale": "en_US.UTF-8", "python": { "version": "3.7.4", "package_manager": { "name": "pip", "version": "19.0.3", "package_file": "requirements.txt" } }, "files": { "requirements.txt": { "checksum": "f3993fe6b978850a694d8d64e83b233b" }, "": { "checksum": "75e08453fa46fb6ee7835e323ce0860e" } } }

Note: The versions and hashes represented above may be different in your file.



"metadata": {
"appmode": "python-api",
"entrypoint": "app:app"



This instructs RStudio Connect that the application associated with this manifest is a Flask application.  This is derived from the api argument in the rsconnect write-manifest api command.


The module:app reference that is executed on deployment.  This is consistent with the naming convention used in the minimal application.

A file named that contains a Flask application instance named app.

app = Flask(__name__)

The route decorator references may not always be @app if Flask blueprints or other extensions are used.  It is important to identify, test, and validate the entrypoint as it relates to the rsconnect-python manifest.


The python entry identifies version and package manager information.  

"python": {
    "version": "3.7.4",
    "package_manager": {
      "name": "pip",
      "version": "19.0.3",
      "package_file": "requirements.txt"

It is important to validate that the correct Python version is recognized by rsconnect-python.  This is the version that RStudio Connect will try to match.


Commit and push

Commit the new files once the manifest is generated and the application is running correctly in the local development environment.

git add .
git commit -m 'initial structure with rstudio connect manifest'

Push these commits to the server hosting the remote origin of the Git repository.


Import from Git in RStudio Connect

Once pushed, the Flask application will be available to import into RStudio Connect following the documentation outlined here


Update and redeploy

The process to update the deployed application is as follows:

  • Commit updated code changes
  • Regenerate and commit manifest.json, if necessary
  • Push code changes
  • Trigger deploy in RStudio Connect or allow for automatic deployment triggered by the RStudio Connect Git update interval