Metadata-Version: 2.1
Name: rsconnect-jupyter
Version: 1.3.1.2
Summary: Jupyter Notebook integration with RStudio Connect
Home-page: http://github.com/rstudio/rsconnect-jupyter
Author: Jonathan Curran
Author-email: jonathan.curran@rstudio.com
License: GPL-2.0
Project-URL: Documentation, https://docs.rstudio.com/rsconnect-jupyter
Description: # rsconnect-jupyter
        
        [rsconnect-jupyter](https://www.github.com/rstudio/rsconnect-jupyter/) is a
        plugin for [Jupyter Notebook](https://jupyter.org/) that enables
        publishing notebooks to [RStudio
        Connect](https://www.rstudio.com/products/connect/).
        
        # Requirements
        
        - Python 2.7.9 or Python 3.5.0 and higher
        - Jupyter Notebook 5.x
        - [pip](https://pypi.org/project/pip/)
        - [wheel](https://pypi.org/project/wheel/)
        - [RStudio Connect](https://www.rstudio.com/products/connect/download-commercial/) v1.7.0
          or higher, configured with Python support.
        
        If using `conda`, `pip` and `wheel` should already be installed.
        
        # Installation
        
        If you are installing `rsconnect-jupyter` for use in Jupyterhub, please see the
        [Jupyterhub](#installation-in-jupyterhub) section below.
        
        We recommend working within a `virtualenv`.  If you
        are unfamiliar, these commands create and activate a `virtualenv`
        at `/my/path`:
        
        ```bash
        pip install virtualenv
        virtualenv /my/path
        source /my/path/bin/activate
        ```
        
        Install Jupyter inside the `virtualenv`:
        ```bash
        pip install jupyter
        ```
        
        > Note: be sure to run Jupyter from the virtual environment, not from a global
        > installation.
        
        Install the `rsconnect-jupyter` package with the following command:
        
        ```bash
        pip install rsconnect_jupyter
        ```
        
        Enable the `rsconnect-jupyter` extension with the following commands:
        
        ```bash
        # Install `rsconnect-jupyter` as a jupyter extension
        jupyter-nbextension install --sys-prefix --py rsconnect_jupyter
        
        # Enable JavaScript extension
        jupyter-nbextension enable --sys-prefix --py rsconnect_jupyter
        
        # Enable Python extension
        jupyter-serverextension enable --sys-prefix --py rsconnect_jupyter
        ```
        
        Note: The above commands only need to be run once when installing
        `rsconnect_jupyter`.
        
        Note: In order to deploy content, you will need at least the 
        [rsconnect-python](https://github.com/rstudio/rsconnect-python) package
        in every kernel you plan to deploy from.
        
        Note: If you run into an issue during installation please let us know by filing
        a bug [here](https://github.com/rstudio/rsconnect-jupyter/issues).
        
        # Uninstalling
        
        First disable and remove the `rsconnect-jupyter` notebook extension:
        
        ```bash
        # Disable Python extensions found in `rsconnect-jupyter`
        jupyter-serverextension disable --sys-prefix --py rsconnect_jupyter
        
        # Remove JavaScript extension
        jupyter-nbextension uninstall --sys-prefix --py rsconnect_jupyter
        ```
        
        Finally, uninstall the `rsconnect-jupyter` python package:
        
        ```bash
        pip uninstall rsconnect_jupyter
        ```
        
        # Upgrading
        
        To upgrade `rsconnect-jupyter`, first uninstall the extension and then
        re-install it.
        
        # Usage
        
        Open a notebook and click the blue icon and select `Publish to RStudio Connect`
        to publish the current notebook to RStudio Connect.
        
        ![blue toolbar icon used for publishing the notebook](docs/images/publish-icon.gif)
        
        ### Entering server information
        
        If this is your first time publishing a notebook, you will be
        prompted to enter the location and a nickname for the RStudio Connect
        server.
        
        You will also be prompted to enter your API Key. See the [RStudio Connect User
        Guide](http://docs.rstudio.com/connect/user/api-keys.html) for
        instructions on generating API Keys for your user.
        
        When you click the Add Server button, `rsconnect-jupyter` will send a request 
        to the RStudio Connect server to verify that it can be reached via the requested 
        URL and that the API key is valid. 
        
        If your RStudio Connect server was configured with a self-signed certificate 
        (or other certificate that computer hosting your Jupyter notebook server does 
        not trust), the attempt to contact RStudio Connect may fail with a 
        TLS-related error. You have multiple options in this case, depending on your needs:
        
         1. If your RStudio Connect Administrator can give you the Certificate Authority (CA)
         Bundle for your RStudio Connect server, ask your Jupyter Administrator if it
         can be added to the trusted system store.
         
         2. If the CA Bundle cannot be added to the trusted system store, you may select
         `Upload TLS Certificate Bundle` to upload the bundle to Jupyter, which will verify
         your secure connection to RStudio Connect.
         
         3. If you cannot obtain the CA bundle, you can disable TLS verification completely
         by selecting the `Disable TLS Certificate Verification` box. Your connection to
         RStudio Connect will still be encrypted, but you will not be able to verify the
         identity of the RStudio Connect server.
        
        ![initial dialog that prompts for the location of RStudio Connect](docs/images/add-dialog.png)
        
        ### Publishing options
        
        ![publish dialog](docs/images/manage.png)
        
        There are two different publication modes. Selecting `Publish finished document only` will 
        publish an HTML snapshot of the notebook to RStudio Connect. HTML snapshots are static and 
        cannot be scheduled or re-run on the RStudio Connect server.
        
        If you select `Publish document with source code`, the notebook file and a list of the Python 
        packages installed in your environment will be sent to RStudio Connect. This enables RStudio 
        Connect to recreate the environment and re-run the notebook at a later time.
        
        #### Additional Files
        
        If your notebook needs some external file in order to render, add the file using the 
        `Select Files` button. You can select any file within the notebook folder. However,
        these files may not be made available to users after render.
        
        #### Environment detection with pip
        
        The list of packages sent along with the notebook comes from the python
        environment where the notebook kernel is running. In order for environment
        inspection to work, the `rsconnect-jupyter` package must be installed in the
        kernel environment; that is, the environment where the `ipykernel` package is
        installed. In most cases that will be the same as the notebook server
        environment where `jupyter` is installed.
        
        The command `pip freeze` will be used to inspect the environment. The output
        of `pip freeze` lists all packages currently installed, as well as their
        versions, which enables RStudio Connect to recreate the same environment.
        
        ### Generating Manifests for git Publishing
        
        RStudio Connect can poll git repositories for deployable content and update
        as you add new commits to your repository. In order to be deployable, a
        directory must have a valid `manifest.json`. Python content should also have
        some kind of environment file (i.e.: `requirements.txt`) in order to be able
        to restore the package set in your current environment.
        
        ![Deployment drop-down menu showing "Publish to Connect" and "Create Manifest for git Publishing"](docs/images/deploy-options.png)
        
        To begin, select `Create Manifest for git Publishing`.
        
        ![Dialog titled "Create Manifest" explaining the manifest creation process with "Cancel" and "Create Manifest" options](docs/images/git-backed.png)
        
        When you click `Create Manifest`, a `manifest.json` and `requirements.txt`
        will be generated for the current notebook using your current environment,
        if they do not exist. If they do exist, you will be presented with a message
        informing you of this fact. If you need to regenerate the files, delete them
        in the Jupyter UI or using the console, then repeat this process.
        
        For more information on git publishing, see the 
        [RStudio Connect User Guide](https://docs.rstudio.com/connect/user/git-backed.html#git-backed-publishing)
        
        ### Handling conflicts
        If content that matches your notebook's title is found on RStudio Connect, you
        may choose to overwrite the existing content or create new content.
        
        ![dialog that prompts for overwriting or publishing new content](docs/images/overwrite.png)
        
        Choosing `New location` will create a new document in RStudio Connect. 
        You can choose either publication mode - an HTML snapshot or a document 
        with source code.
        
        Updating an existing document will not change its publication mode.
        
        
        Upon successful publishing of the document a notification will be
        shown in toolbar.  Clicking the notification will open the published
        document in the RStudio Connect server you selected in the previous
        dialog.
        
        ![notification that shows the notebook was published successfully](docs/images/published.gif)
        
        # Collaboration
        
        To collaborate with others add them as collaborators in RStudio Connect. During
        publishing they should provide their API key and will be able to choose a
        content location to publish to if the notebook title is the same.
        
        You may share notebooks if appropriate.
        
        # Installation in JupyterHub
        
        In JupyterHub, follow the directions [above](#installation) to install the
        `rsconnect-jupyter` package into the Python environment where the Jupyter
        notebook server and kernel are installed. Typically those will be the same
        environment. If you've configured separate kernel environments, install the
        `rsconnect-jupyter` package in the notebook server environment as well as each
        kernel environment.
        
        The exact install location depends on your Jupyterhub configuration.
        
        
        ## JupyterHub Example Configuration
        
        This section presents a simple working example of a Jupyterhub configuration
        with `rsconnect-jupyter` installed.
        
        This example uses Docker, but you can install the `rsconnect-jupyter` package in
        any Jupyterhub installation. Docker is not required.
        
        Example Dockerfile:
        
        ```dockerfile
        FROM jupyterhub/jupyterhub:0.9.4
        
        # Install Jupyter notebook into the existing base conda environment
        RUN conda install notebook
        
        # Download and install rsconnect-jupyter in the same environment
        # Update this to specify the desired version of the rsconnect-jupyter package,
        # or pass `--build-arg VERSION=...` to docker build.
        ARG VERSION=RSCONNECT_VERSION
        ARG REPOSITORY=https://s3.amazonaws.com/rstudio-rsconnect-jupyter
        
        RUN wget ${REPOSITORY}/rsconnect_jupyter-${VERSION}-py2.py3-none-any.whl
        RUN pip install rsconnect_jupyter-${VERSION}-py2.py3-none-any.whl && \
        	jupyter-nbextension install --sys-prefix --py rsconnect_jupyter && \
        	jupyter-nbextension enable --sys-prefix --py rsconnect_jupyter && \
        	jupyter-serverextension enable --sys-prefix --py rsconnect_jupyter
        
        # create test users
        RUN useradd -m -s /bin/bash user1 && \
        	useradd -m -s /bin/bash user2 && \
        	useradd -m -s /bin/bash user3 && \
        	bash -c 'echo -en "password\npassword" | passwd user1' && \
        	bash -c 'echo -en "password\npassword" | passwd user2' && \
        	bash -c 'echo -en "password\npassword" | passwd user3'
        
        CMD ["jupyterhub"]
        ```
        
        Run these commands to build and start the container:
        ```bash
        docker build -t jupyterhub:rsconnect-jupyter .
        docker run --rm -p 8000:8000 --name jupyterhub jupyterhub:rsconnect-jupyter
        ```
        
        Connect to Jupyterhub on http://localhost:8000 and log in as one of the test
        users. From there, you can create a notebook and publish it to RStudio Connect.
        Note that the current Jupyterhub docker image uses Python 3.6.5, so you will
        need a compatible Python version installed on your RStudio Connect server.
        
Platform: UNKNOWN
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*
Description-Content-Type: text/markdown
