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Python integration with RStudio Connect

Project description


This package is a library used by the rsconnect-jupyter package to deploy Jupyter notebooks to RStudio Connect. It can also be used by other Python-based deployment tools.

There is also a CLI deployment tool which can be used directly to deploy Jupyter notebooks. Other content types can be deployed if they include a prepared manifest.json file.


To install from this repository:

git clone
cd rsconnect-python
python install

To install a version directly from pip:

pip install rsconnect-python

Using the rsconnect CLI

rsconnect deploy notebook \
	--server https://my.connect.server:3939 \
	--api-key my-api-key \

Note: the examples here use long command line options, but there are short options (-s, -k, etc.) available. Run rsconnect deploy notebook --help for details.

Setting up rsconnect CLI autocompletion


eval "$(_RSCONNECT_COMPLETION=source rsconnect)"


eval "$(_RSCONNECT_COMPLETION=source_zsh rsconnect)"

If you get command not found: compdef, you need to add the following lines to your .zshrc before the completion setup:

autoload -Uz compinit

Saving Server Information

To avoid having to provide your server information at each deployment, you can optionally save server information:

rsconnect add \
	--api-key my-api-key \
	--server https://my.connect.server:3939 \
	--name myserver

Once the server is saved, you can refer to it by name:

rsconnect deploy notebook --server myserver my-notebook.ipynb

If there is only one server saved, this will work too:

rsconnect deploy notebook my-notebook.ipynb

You can see the list of saved servers with:

rsconnect list

and remove servers with:

rsconnect remove myserver

You can verify a server URL (and optionally, an API key):

rsconnect test \
	--server https://my.connect.server:3939 \
	--api-key my-api-key

Notebook Deployment Options

Including Extra Files

You can include extra files in the deployment bundle to make them available when your notebook is run by the Connect server. Just specify them on the command line after the notebook file:

rsconnect deploy notebook my-notebook.ipynb data.csv

Package Dependencies

If a requirements.txt file exists in the same directory as the notebook file, it will be included in the bundle. It must specify the package dependencies needed to execute the notebook. RStudio Connect will reconstruct the python environment using the specified package list.

If there is no requirements.txt file, the package dependencies will be determined from the current Python environment, or from an alternative Python executable specified in the --python option or via the RETICULATE_PYTHON environment variable.

rsconnect deploy notebook --python /path/to/python my-notebook.ipynb

You can see the packages list that will be included by running pip freeze yourself, ensuring that you use the same Python that you use to run your Jupyter Notebook:

/path/to/python -m pip freeze

Static (Snapshot) Deployment

By default, rsconnect deploys the original notebook with source code. This enables the RStudio Connect server to re-run the notebook upon request or on a schedule.

If you just want to publish an HTML snapshot of the notebook, you can use the --static option. This will cause rsconnect to execute your notebook locally to produce the HTML file, then publish the HTML file to the Connect server.

rsconnect deploy notebook --static my-notebook.ipynb

Creating a Manifest for Future Deployment

You can create a manifest.json file for a Jupyter Notebook, then use that manifest in a later deployment.

The manifest command will also create a requirements.txt file, if it does not already exist. It will contain the package dependencies from the current Python environment, or from an alternative Python executable specified in the --python option or via the RETICULATE_PYTHON environment variable.

Note: manifests for static (pre-rendered) notebooks cannot be created.

rsconnect manifest notebook my-notebook.ipynb

Deploying R or Other Content

You can deploy other content that has an existing RStudio Connect manifest.json file. For example, if you download and unpack a source bundle from Connect, you can deploy the resulting directory. The options are similar to notebook deployment; see rsconnect deploy manifest --help.

Note that in this case, the existing content is deployed as-is. Python environment inspection and notebook pre-rendering, if needed, are assumed to be already done and represented in the manifest.

rsconnect deploy manifest /path/to/manifest.json

If you have R content but don't have a manifest.json file, you can use the RStudio IDE to create the manifest. See the help for the rsconnect::writeManifest R function:


Options for All Types of Deployments


The title of the deployed content is derived from the filename. For example, if you deploy my-notebook.ipynb, the title will be my-notebook. To change this, use the --title option:

rsconnect deploy notebook --title "My Notebook" my-notebook.ipynb

When using rsconnect deploy manifest, the title is derived from the primary filename referenced in the manifest.

Network Options

TLS/SSL Certificates

RStudio Connect servers can be configured to use TLS/SSL. If your server's certificate is trusted by your Jupyter Notebook server, then you don't need to do anything special. Just provide the URL and API Key:

rsconnect add \
	--api-key my-api-key \
	--server https://my.connect.server:3939 \
	--name my-server

If this fails with a TLS Certificate Validation error, then you have two options.

  • Provide the Root CA certificate that is at the root of the signing chain for your RStudio Connect server. This will enable rsconnect to securely validate the server's TLS certificate.

     rsconnect add \
     	--api-key my-api-key \
     	--server https://my.connect.server:3939 \
     	--cacert /path/to/certificate.pem \
     	--name my-server
  • Connect in "insecure mode". This disables TLS certificate verification, which results in a less secure connection.

     rsconnect add \
     	--api-key my-api-key \
     	--server https://my.connect.server:3939 \
     	--insecure \
     	--name my-server

Updating a Deployment

If you deploy a file again to the same server, rsconnect will update the previous deployment by default. This means that you can keep running rsconnect deploy notebook my-notebook.ipynb as you develop new versions of your notebook.

Forcing a New Deployment

To bypass this behavior and force a new deployment, use the --new option:

rsconnect deploy notebook --new my-notebook.ipynb

Updating a Different Deployment

If you want to update an existing deployment but don't have the saved deployment data, you can provide the app's numeric ID or GUID on the command line:

rsconnect deploy notebook --app-id 123456 my-notebook.ipynb

You must be the owner of the target deployment, or a collaborator with permission to change the content. The type of content (static notebook, or notebook with source code) must match the existing deployment.

Note: there is no confirmation required to update a deployment. If you do so accidentally, use the "Source Versions" dialog in the Connect dashboard to activate the previous version and remove the erroneous one.

Showing the Deployment Information

You can see the information that rsconnect-python has saved for the most recent deployment with the info command:

rsconnect info my-notebook.ipynb

If you have deployed to multiple servers, the most recent deployment information for each server will be shown. This command also displays the path to the file where the deployment data is stored.

Configuration Files

Configuration files are stored in a platform-specific directory:

Platform Location
Mac $HOME/Library/Application Support/rsconnect-python/
Linux $HOME/.rsconnect-python/ or $XDG_CONFIG_HOME/rsconnect-python/
Windows $APPDATA/rsconnect-python

Server information is stored in the servers.json file in that directory.

Deployment Data

After a deployment is completed, information about the deployment is saved to enable later redeployment. This data is stored alongside the deployed file, in an rsconnect-python subdirectory, if possible. If that location is not writable during deployment, then the deployment data will be stored in the global configuration directory specified above.

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