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Python integration with RStudio Connect

Project description

The rsconnect-python CLI

!!! warning

As of version 1.7.0, rsconnect-python requires Python version 3.5 or higher. Please see the
[official announcement](https://www.rstudio.com/blog/rstudio-connect-2021-08-python-updates/)
for details about this decision.

This package provides a CLI (command-line interface) for interacting with and deploying to RStudio Connect. This 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.

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 those based on Flask or FastAPI) 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 list --format=freeze yourself, ensuring that you use the same Python that you use to run your Jupyter Notebook:

/path/to/python -m pip list --format=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

You can deploy a variety of APIs and applications using sub-commands of the rsconnect deploy command.

  • api: WSGI-compliant APIs such as Flask and packages based on Flask
  • fastapi: ASGI-compliant APIs (FastAPI, Quart, Sanic, and Falcon)
  • dash: Python Dash apps
  • streamlit: Streamlit apps
  • bokeh: Bokeh server apps

All options below apply equally to the api, fastapi, 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 or directories to exclude.

rsconnect deploy dash --exclude dash-app-venv --exclude TODO.txt dash-app/

You can exclude a directory by naming it:

rsconnect deploy dash --exclude dash-app-venv --exclude output/ dash-app/

The --exclude option may be repeated, and may include a glob pattern. 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).

rsconnect deploy dash --exclude dash-app-venv --exclude “*.txt” dash-app/

The following shows an example of an extra file taking precedence:

rsconnect deploy dash --exclude “*.csv” dash-app/ important_data.csv

Some directories are excluded by default, to prevent bundling and uploading files that are not needed or might interfere with the deployment process:

.Rproj.user
.env
.git
.svn
.venv
__pycache__
env
packrat
renv
rsconnect-python
rsconnect
venv

Any directory that appears to be a Python virtual environment (by containing bin/python) will also be excluded.

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 list --format=freeze yourself, ensuring that you use the same Python that you use to run your API or application:

/path/to/python -m pip list --format=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 fastapi, 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.

Environment Variables

You can set environment variables during deployment. Their names and values will be passed to RStudio Connect during deployment so you can use them in your code.

For example, if notebook.ipynb contains

print(os.environ["MYVAR"])

You can set the value of MYVAR that will be set when your code runs in RStudio Connect using the -E/--environment option:

rsconnect deploy notebook --environment MYVAR='hello world' notebook.ipynb

To avoid exposing sensitive values on the command line, you can specify a variable without a value. In this case, it will use the value from the environment in which rsconnect-python is running:

export SECRET_KEY=12345

rsconnect deploy notebook --environment SECRET_KEY notebook.ipynb

If you specify environment variables when updating an existing deployment, new values will be set for the variables you provided. Other variables will remain unchanged. If you don't specify any variables, all of the existing variables will remain unchanged.

Environment variables are set on the content item before the content bundle is uploaded and deployed. If the deployment fails, the new environment variables will still take effect.

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 {{ rsconnect_python.version }}

Hide Jupyter Notebook Input Code Cells

The user can render a Jupyter notebook without its corresponding input code cells by passing the '--hide-all-input' flag through the cli:

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

To selectively hide input cells in a Jupyter notebook, the user needs to follow a two step process:

  1. tag cells with the 'hide_input' tag,
  2. then pass the ' --hide-tagged-input' flag through the cli:
rsconnect deploy notebook \
    --server https://connect.example.org:3939 \
    --api-key my-api-key \
    --hide-tagged-input \
    my-notebook.ipynb

By default, rsconnect-python does not install Jupyter notebook related depenencies. These dependencies are installed via rsconnect-jupyter. When the user is using the hide input features in rsconnect-python by itself without rsconnect-jupyter, he/she needs to install the following package depenecies:

notebook
nbformat
nbconvert>=5.6.1

Content subcommands

rsconnect-python supports multiple options for interacting with RStudio Connect's /v1/content API. Both administrators and publishers can use the content subcommands to search, download, and rebuild content on RStudio Connect without needing to access the dashboard from a browser.

Note: The rsconnect content CLI subcommands are intended to be easily scriptable. The default output format is JSON so that the results can be easily piped into other command line utilities like jq for further post-processing.

$ rsconnect content --help
Usage: rsconnect content [OPTIONS] COMMAND [ARGS]...

  Interact with RStudio Connect's content API.

Options:
  --help  Show this message and exit.

Commands:
  build            Build content on RStudio Connect.
  describe         Describe a content item on RStudio Connect.
  download-bundle  Download a content item's source bundle.
  search           Search for content on RStudio Connect.

Content Search

The rsconnect content search subcommands can be used by administrators and publishers to find specific content on an given RStudio Connect server. The search returns metadata for each content item that meets the search criteria.

$ rsconnect content search --help
Usage: rsconnect content search [OPTIONS]

Options:
  -n, --name TEXT                 The nickname of the RStudio Connect server.
  -s, --server TEXT               The URL for the RStudio Connect server.
  -k, --api-key TEXT              The API key to use to authenticate with
                                  RStudio Connect.

  -i, --insecure                  Disable TLS certification/host validation.
  -c, --cacert FILENAME           The path to trusted TLS CA certificates.
  --published                     Search only published content.
  --unpublished                   Search only unpublished content.
  --content-type [unknown|shiny|rmd-static|rmd-shiny|static|api|tensorflow-saved-model|jupyter-static|python-api|python-dash|python-streamlit|python-bokeh|python-fastapi|quarto-shiny|quarto-static]
                                  Filter content results by content type.
  --r-version VERSIONSEARCHFILTER
                                  Filter content results by R version.
  --py-version VERSIONSEARCHFILTER
                                  Filter content results by Python version.
  --title-contains TEXT           Filter content results by title.
  --order-by [created|last_deployed]
                                  Order content results.
  -v, --verbose                   Print detailed messages.
  --help                          Show this message and exit.

$ rsconnect content search
[
  {
    "max_conns_per_process": null,
    "content_category": "",
    "load_factor": null,
    "cluster_name": "Local",
    "description": "",
    "bundle_id": "142",
    "image_name": null,
    "r_version": null,
    "content_url": "https://connect.example.org:3939/content/4ffc819c-065c-420c-88eb-332db1133317/",
    "connection_timeout": null,
    "min_processes": null,
    "last_deployed_time": "2021-12-02T18:09:11Z",
    "name": "logs-api-python",
    "title": "logs-api-python",
    "created_time": "2021-07-19T19:17:32Z",
    "read_timeout": null,
    "guid": "4ffc819c-065c-420c-88eb-332db1133317",
    "parameterized": false,
    "run_as": null,
    "py_version": "3.8.2",
    "idle_timeout": null,
    "app_role": "owner",
    "access_type": "acl",
    "app_mode": "python-api",
    "init_timeout": null,
    "id": "18",
    "quarto_version": null,
    "dashboard_url": "https://connect.example.org:3939/connect/#/apps/4ffc819c-065c-420c-88eb-332db1133317",
    "run_as_current_user": false,
    "owner_guid": "edf26318-0027-4d9d-bbbb-54703ebb1855",
    "max_processes": null
  },
  ...
]

See this section for more comprehensive usage examples of the available search flags.

Content Build

Note: The rsconnect content build subcommand requires RStudio Connect >= 2021.11.1

RStudio Connect caches R and Python packages in the configured Server.DataDir. Under certain circumstances (examples below), these package caches can become stale and need to be rebuilt. This refresh automatically occurs when an RStudio Connect user visits the content. You may wish to refresh some content before it is visited because it is high priority or is not visited frequently (API content, emailed reports). In these cases, it is possible to preemptively build specific content items using the rsconnect content build subcommands. This way the user does not have to pay the build cost when the content is accessed next.

The following are some common scenarios where performing a content build might be necessary:

  • OS upgrade
  • changes to gcc or libc libraries
  • changes to Python or R installations
  • switching from source to binary package repositories or vice versa

Note: The content build command is non-destructive, meaning that it does nothing to purge existing packrat/python package caches before a build. If you have an existing cache, it should be cleared prior to starting a content build. See the migration documentation for details.

Note: You may use the rsconnect content search subcommand to help identify high priority content items to build.

rsconnect content build --help
Usage: rsconnect content build [OPTIONS] COMMAND [ARGS]...

  Build content on RStudio Connect. Requires Connect >= 2021.11.1

Options:
  --help  Show this message and exit.

Commands:
  add      Mark a content item for build. Use `build run` to invoke the build
           on the Connect server.

  history  Get the build history for a content item.
  logs     Print the logs for a content build.
  ls       List the content items that are being tracked for build on a given
           Connect server.

  rm       Remove a content item from the list of content that are tracked for
           build. Use `build ls` to view the tracked content.

  run      Start building content on a given Connect server.

To build a specific content item, first add it to the list of content that is "tracked" for building using its GUID.

Note: Metadata for "tracked" content items is stored in a local directory called rsconnect-build which will be automatically created in your current working directory. You may set the environment variable CONNECT_CONTENT_BUILD_DIR to override this directory location.

$ rsconnect content build add --guid 4ffc819c-065c-420c-88eb-332db1133317

Note: See this section for an example of how to add multiple content items in bulk, from the results of a rsconnect content search command.

To view all currently "tracked" content items, use the rsconnect content build ls subcommand.

$ rsconnect content build ls

To view only the "tracked" content items that have not yet been built, use the --status NEEDS_BUILD flag.

$ rsconnect content build ls --status NEEDS_BUILD

Once the content items have been added, you may initiate a build using the rsconnect content build run subcommand. This command will attempt to build all "tracked" content that has the status NEEDS_BUILD.

$ rsconnect content build run
[INFO] 2021-12-14T13:02:45-0500 Initializing ContentBuildStore for https://connect.example.org:3939
[INFO] 2021-12-14T13:02:45-0500 Starting content build (https://connect.example.org:3939)...
[INFO] 2021-12-14T13:02:45-0500 Starting build: 4ffc819c-065c-420c-88eb-332db1133317
[INFO] 2021-12-14T13:02:50-0500 Running = 1, Pending = 0, Success = 0, Error = 0
[INFO] 2021-12-14T13:02:50-0500 Build succeeded: 4ffc819c-065c-420c-88eb-332db1133317
[INFO] 2021-12-14T13:02:55-0500 Running = 0, Pending = 0, Success = 1, Error = 0
[INFO] 2021-12-14T13:02:55-0500 1/1 content builds completed in 0:00:10
[INFO] 2021-12-14T13:02:55-0500 Success = 1, Error = 0
[INFO] 2021-12-14T13:02:55-0500 Content build complete.

Sometimes content builds will fail and require debugging by the publisher or administrator. Use the rsconnect content build ls to identify content builds that resulted in errors and inspect the build logs with the rsconnect content build logs subcommand.

$ rsconnect content build ls --status ERROR
[INFO] 2021-12-14T13:07:32-0500 Initializing ContentBuildStore for https://connect.example.org:3939
[
  {
    "rsconnect_build_status": "ERROR",
    "last_deployed_time": "2021-12-02T18:09:11Z",
    "owner_guid": "edf26318-0027-4d9d-bbbb-54703ebb1855",
    "rsconnect_last_build_log": "/Users/david/code/rstudio/rsconnect-python/rsconnect-build/logs/connect_example_org_3939/4ffc819c-065c-420c-88eb-332db1133317/pZoqfBoi6BgpKde5.log",
    "guid": "4ffc819c-065c-420c-88eb-332db1133317",
    "rsconnect_build_task_result": {
      "user_id": 1,
      "error": "Cannot find compatible environment: no compatible Local environment with Python version 3.9.5",
      "code": 1,
      "finished": true,
      "result": {
        "data": "An error occurred while building the content",
        "type": "build-failed-error"
      },
      "id": "pZoqfBoi6BgpKde5"
    },
    "dashboard_url": "https://connect.example.org:3939/connect/#/apps/4ffc819c-065c-420c-88eb-332db1133317",
    "name": "logs-api-python",
    "title": "logs-api-python",
    "content_url": "https://connect.example.org:3939/content/4ffc819c-065c-420c-88eb-332db1133317/",
    "bundle_id": "141",
    "rsconnect_last_build_time": "2021-12-14T18:07:16Z",
    "created_time": "2021-07-19T19:17:32Z",
    "app_mode": "python-api"
  }
]

$ rsconnect content build logs --guid 4ffc819c-065c-420c-88eb-332db1133317
[INFO] 2021-12-14T13:09:27-0500 Initializing ContentBuildStore for https://connect.example.org:3939
Building Python API...
Cannot find compatible environment: no compatible Local environment with Python version 3.9.5
Task failed. Task exited with status 1.

Common Usage Examples

Searching for content

The following are some examples of how publishers might use the rsconnect content search subcommand to find content on RStudio Connect. By default, the rsconnect content search command will return metadata for ALL of the content on a RStudio Connect server, both published and unpublished content.

Note: When using the --r-version and --py-version flags, users should make sure to quote the arguments to avoid conflicting with your shell. For example, bash would interpret --py-version >3.0.0 as a shell redirect because of the unquoted > character.

# return only published content
$ rsconnect content search --published

# return only unpublished content
$ rsconnect content search --unpublished

# return published content where the python version is at least 3.9.0
$ rsconnect content search --published --py-version ">=3.9.0"

# return published content where the R version is exactly 3.6.3
$ rsconnect content search --published --r-version "==3.6.3"

# return published content where the content type is a static RMD
$ rsconnect content search --content-type rmd-static

# return published content where the content type is either shiny OR fast-api
$ rsconnect content search --content-type shiny --content-type python-fastapi

# return all content, published or unpublished, where the title contains the text "Stock Report"
$ rsconnect content search --title-contains "Stock Report"

# return published content, results are ordered by when the content was last deployed
$ rsconnect content search --published --order-by last_deployed

# return published content, results are ordered by when the content was created
$ rsconnect content search --published --order-by created

Finding r and python versions

One common use for the search command might be to find the versions of r and python that are currently in use on your RStudio Connect server before a migration.

# search for all published content and print the unique r and python version combinations
$ rsconnect content search --published | jq -c '.[] | {py_version,r_version}' | sort |
uniq
{"py_version":"3.8.2","r_version":"3.5.3"}
{"py_version":"3.8.2","r_version":"3.6.3"}
{"py_version":"3.8.2","r_version":null}
{"py_version":null,"r_version":"3.5.3"}
{"py_version":null,"r_version":"3.6.3"}
{"py_version":null,"r_version":null}

Finding recently deployed content

# return only the 10 most recently deployed content items
$ rsconnect content search --order-by last_deployed --published | jq -c 'limit(10; .[]) | { guid, last_deployed_time }'
{"guid":"4ffc819c-065c-420c-88eb-332db1133317","last_deployed_time":"2021-12-02T18:09:11Z"}
{"guid":"aa2603f8-1988-484f-a335-193f2c57e6c4","last_deployed_time":"2021-12-01T20:56:07Z"}
{"guid":"051252f0-4f70-438f-9be1-d818a3b5f8d9","last_deployed_time":"2021-12-01T20:37:01Z"}
{"guid":"015143da-b75f-407c-81b1-99c4a724341e","last_deployed_time":"2021-11-30T16:56:21Z"}
{"guid":"bcc74209-3a81-4b9c-acd5-d24a597c256c","last_deployed_time":"2021-11-30T15:51:07Z"}
{"guid":"f21d7767-c99e-4dd4-9b00-ff8ec9ae2f53","last_deployed_time":"2021-11-23T18:46:28Z"}
{"guid":"da4f709c-c383-4fbc-89e2-f032b2d7e91d","last_deployed_time":"2021-11-23T18:46:28Z"}
{"guid":"9180809d-38fd-4730-a0e0-8568c45d87b7","last_deployed_time":"2021-11-23T15:16:19Z"}
{"guid":"2b1d2ab8-927d-4956-bbf9-29798d039bc5","last_deployed_time":"2021-11-22T18:33:17Z"}
{"guid":"c96db3f3-87a1-4df5-9f58-eb109c397718","last_deployed_time":"2021-11-19T20:25:33Z"}

Add to build from search results

One common use case might be to rsconnect content build add content for build based on the results of a rsconnect content search. For example:

# search for all API type content, then
# for each guid, add it to the "tracked" content items
$ for guid in $(rsconnect content search \
--published \
--content-type python-api \
--content-type api | jq -r '.[].guid'); do \
rsconnect content build add --guid $guid; done

Adding content items one at a time can be a slow operation. This is because rsconnect content build add must fetch metadata for each content item before it is added to the "tracked" content items. By providing multiple --guid arguments to the rsconnect content build add subcommand, we can fetch metadata for multiple content items in a single api call, which speeds up the operation sigificantly.

# write the guid of every published content item to a file called guids.txt
rsconnect content search --published | jq '.[].guid' > guids.txt

# bulk-add from the guids.txt by executing a single `rsconnect content build add` command
xargs printf -- '-g %s\n' < guids.txt | xargs rsconnect content build add

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