rsconnect-jupyter is a plugin for Jupyter Notebook that enables publishing notebooks to RStudio Connect.
If using conda, pip and wheel should already be installed.
If you are installing rsconnect-jupyter for use in Jupyterhub, please see the Jupyterhub section below.
We recommend working within a virtualenv. If you are unfamiliar, these commands create and activate a virtualenv at /my/path:
Install Jupyter inside the virtualenv:
Note: be sure to run Jupyter from the virtual environment, not from a global installation.
Install the rsconnect-jupyter package with the following command:
Enable the rsconnect-jupyter extension with the following commands:
# 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_jupyterNote: 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 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.
First disable and remove the rsconnect-jupyter notebook extension:
# 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_jupyterFinally, uninstall the rsconnect-jupyter python package:
To upgrade rsconnect-jupyter, first uninstall the extension and then re-install it.
Open a notebook and click the blue icon and select Publish to RStudio Connect to publish the current notebook to RStudio Connect.
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 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:
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.
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.
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.
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.
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.
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.
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.
To begin, select Create Manifest for git Publishing.
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
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.
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.
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.
In JupyterHub, follow the directions above 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.
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:
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:
docker build -t jupyterhub:rsconnect-jupyter .
docker run --rm -p 8000:8000 --name jupyterhub jupyterhub:rsconnect-jupyterConnect 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.