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The rsconnect-python CLI and library

This package provides both a CLI (command-line interface) and a library for interacting with and deploying to RStudio Connect. The library is also used by the rsconnect-jupyter package to deploy Jupyter notebooks via the Jupyter web console. Many types of content supported by RStudio Connect may be deployed by this package, including WSGI-style APIs, Dash, Streamlit, and Bokeh applications.

Important

Bokeh support in RStudio Connect is currently in beta.

Content types not directly supported by the CLI may also be deployed if they include a prepared manifest.json file. See "Deploying R or Other Content" for details.

Deploying Python Content to RStudio Connect

RStudio Connect supports the deployment of Jupyter notebooks, Python APIs (such as flask-based) and apps (such as Dash, Streamlit, and Bokeh apps). Much like deploying R content to RStudio Connect, there are some caveats to understand when replicating your environment on the RStudio Connect server:

RStudio Connect insists on matching <MAJOR.MINOR> versions of Python. For example, a server with only Python 3.8 installed will fail to match content deployed with Python 3.7. Your administrator may also enable exact Python version matching which will be stricter and require matching major, minor, and patch versions. For more information see the RStudio Connect Admin Guide chapter titled Python Version Matching.

Installation

To install rsconnect-python from PYPI, you may use any python package manager such as pip:

pip install rsconnect-python

You may also build and install a wheel directly from a repository clone:

git clone https://github.com/rstudio/rsconnect-python.git
cd rsconnect-python
pip install pipenv
make deps dist
pip install ./dist/rsconnect_python-*.whl

Using the rsconnect CLI

Here's an example command that deploys a Jupyter notebook to RStudio Connect.

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

Note

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

Setting up rsconnect CLI auto-completion

If you would like to use your shell's tab completion support with the rsconnect command, use the command below for the shell you are using.

bash

If you are using the bash shell, use this to enable tab completion.

#~/.bashrc
eval "$(_RSCONNECT_COMPLETE=source rsconnect)"

zsh

If you are using the zsh shell, use this to enable tab completion.

#~/.zshrc
eval "$(_RSCONNECT_COMPLETE=source_zsh rsconnect)"

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

#~/.zshrc
autoload -Uz compinit
compinit

Managing Server Information

The information used by the rsconnect command to communicate with an RStudio Connect server can be tedious to repeat on every command. To help, the CLI supports the idea of saving this information, making it usable by a simple nickname.

Important

One item of information saved is the API key used to authenticate with RStudio Connect. Although the file where this information is saved is marked as accessible by the owner only, it's important to remember that the key is present in the file as plain text so care must be taken to prevent any unauthorized access to the server information file.

TLS Support and RStudio Connect

Usually, an RStudio Connect server will be set up to be accessed in a secure manner, using the https protocol rather than simple http. If RStudio Connect is set up with a self-signed certificate, you will need to include the --insecure flag on all commands. If RStudio Connect is set up to require a client-side certificate chain, you will need to include the --cacert option that points to your certificate authority (CA) trusted certificates file. Both of these options can be saved along with the URL and API Key for a server.

Note

When certificate information is saved for the server, the specified file is read and its contents are saved under the server's nickname. If the CA file's contents are ever changed, you will need to add the server information again.

See the Network Options section for more details about these options.

Remembering Server Information

Use the add command to store information about an RStudio Connect server:

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

Note

The rsconnect CLI will verify that the serve URL and API key are valid. If either is found not to be, no information will be saved.

If any of the access information for the server changes, simply rerun the add command with the new information and it will replace the original information.

Once the server's information is saved, you can refer to it by its nickname:

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

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

rsconnect deploy notebook my-notebook.ipynb

Listing Server Information

You can see the list of saved server information with:

rsconnect list

Removing Server Information

You can remove information about a server with:

rsconnect remove --name myserver

Removing may be done by its nickname (--name) or URL (--server).

Verifying Server Information

You can verify that a URL refers to a running instance of RStudio Connect by using the details command:

rsconnect details --server https://connect.example.org:3939

In this form, rsconnect will only tell you whether the URL given does, in fact, refer to a running RStudio Connect instance. If you include a valid API key:

rsconnect details --server https://connect.example.org:3939 --api-key my-api-key

the tool will provide the version of RStudio Connect (if the server is configured to divulge that information) and environmental information including versions of Python that are installed on the server.

You can also use nicknames with the details command if you want to verify that the stored information is still valid.

Notebook Deployment Options

There are a variety of options available to you when deploying a Jupyter notebook to RStudio Connect.

Including Extra Files

You can include extra files in the deployment bundle to make them available when your notebook is run by the RStudio 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 or the --force-generate option is specified, the package dependencies will be determined from the current Python environment, or from an alternative Python executable specified via 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 all its 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 RStudio 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. Use the write-manifest command to do this.

The write-manifest command will also create a requirements.txt file, if it does not already exist or the --force-generate option is specified. 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.

Here is an example of the write-manifest command:

rsconnect write-manifest notebook my-notebook.ipynb

Note

Manifests for static (pre-rendered) notebooks cannot be created.

API/Application Deployment Options

There are a variety of options available to you when deploying a Python WSGI-style API, Dash, Streamlit, or Bokeh application. All options below apply equally to api, dash, streamlit, and bokeh sub-commands.

Including Extra Files

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

rsconnect deploy api flask-api/ data.csv

Since deploying an API or application starts at a directory level, there will be times when some files under that directory subtree should not be included in the deployment or manifest. Use the --exclude option to specify files to exclude. An exclusion may be a glob pattern and the --exclude option may be repeated.

rsconnect deploy dash --exclude "workfiles/*" dash-app/ data.csv

You should always quote a glob pattern so that it will be passed to rsconnect as-is instead of letting the shell expand it. If a file is specifically listed as an extra file that also matches an exclusion pattern, the file will still be included in the deployment (i.e., extra files take precedence).

Package Dependencies

If a requirements.txt file exists in the API/application directory, it will be included in the bundle. It must specify the package dependencies needed to execute the API or application. RStudio Connect will reconstruct the Python environment using the specified package list.

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

rsconnect deploy api --python /path/to/python my-api/

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 API or application:

/path/to/python -m pip freeze

Creating a Manifest for Future Deployment

You can create a manifest.json file for an API or application, then use that manifest in a later deployment. Use the write-manifest command to do this.

The write-manifest command will also create a requirements.txt file, if it does not already exist or the --force-generate option is specified. 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.

Here is an example of the write-manifest command:

rsconnect write-manifest api my-api/

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 RStudio Connect, you can deploy the resulting directory. The options are similar to notebook or API/application deployment; see rsconnect deploy manifest --help for details.

Here is an example of the deploy manifest command:

rsconnect deploy manifest /path/to/manifest.json

Note

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

The argument to deploy manifest may also be a directory so long as that directory contains a manifest.json file.

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:

install.packages('rsconnect')
library(rsconnect)
?rsconnect::writeManifest

Options for All Types of Deployments

These options apply to any type of content deployment.

Title

The title of the deployed content is, by default, 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 api, rsconnect deploy dash, rsconnect deploy streamlit, or rsconnect deploy bokeh, the title is derived from the directory containing the API or application.

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

Network Options

When specifying information that rsconnect needs to be able to interact with RStudio Connect, you can tailor how transport layer security is performed.

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, API client or user's browser, then you don't need to do anything special. You can test this out with the details command:

rsconnect details --api-key my-api-key --server https://connect.example.org:3939

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

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

Once you work out the combination of options that allow you to successfully work with an instance of RStudio Connect, you'll probably want to use the add command to have rsconnect remember those options and allow you to just use a nickname.

Updating a Deployment

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

Forcing a New Deployment

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

rsconnect deploy dash --new my-app/

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, notebook with source code, API, or application) 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 RStudio Connect dashboard to activate the previous version and remove the erroneous one.

Finding the App ID

The App ID associated with a piece of content you have previously deployed from the rsconnect command line interface can be found easily by querying the deployment information using the info command. For more information, see the Showing the Deployment Information section.

If the content was deployed elsewhere or info does not return the correct App ID, but you can open the content on RStudio Connect, find the content and open it in a browser. The URL in your browser's location bar will contain #/apps/NNN where NNN is your App ID. The GUID identifier for the app may be found on the Info tab for the content in the RStudio Connect UI.

Showing the Deployment Information

You can see the information that the rsconnect command 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.

Stored Information Files

Stored information 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

Remembered 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.

Generated from rsconnect-python 1.5.0b5