A Dashboard publishing solution for Data Science teams to share results with decision makers.
Run a private on-premise or cloud-based JupyterHub with extensions to instantly publish Jupyter notebooks (Voilà), Streamlit, Plotly Dash, Bokeh / Panel, and R Shiny apps as user-friendly interactive dashboards to share with non-technical colleagues.
The cdsdashboards open source package allows data scientists to instantly and reliably publish interactive notebooks or other scripts as secure interactive web apps.
Selected JupyterHub users can view the dashboard, so for example this can be linked to single sign on through corporate email accounts.
Source files can be obtained from a Git repo, or directly from the dashboard publisher’s Jupyter tree.
Read a full description in Overview.
JupyterHub is a way to run one website that provides Jupyter notebook environments to multiple users - your entire data science team, for example. To use ContainDS Dashboards, you will need a JupyterHub setup, but you don’t need to use it as the main data science environment for your organisation. ContainDS Dashboards leverages the standardised security and authentication functionality of JupyterHub, which makes ContainDS Dashboards incredibly powerful, even if you don’t believe your organisation requires a JupyterHub for any other purposes.
ContainDS is a data science platform for teams working on discrete projects. It provides simple infrastructure to share prototypes and dashboards based on any open source frameworks.
Your data scientists will always use their preferred development environments.
ContainDS Solutions will:
Grant decision makers and clients easy access to actionable insights helping them move projects forward quickly and with confidence.
Save time and reduce errors for your Data Science team, allowing them to focus on their core roles.
Eliminate IT security threats from data science teams hosting web apps and sensitive data in arbitrary insecure cloud locations.
Empower data scientists to use their dashboarding framework of choice while unifying your team’s approach to publishing.
ContainDS is a suite of two products: Dashboards for sharing online, and Desktop for local-only development and running of data science environments on laptops or desktop computers where the internet is unavailable or insufficiently secure.
Installation and Setup¶
Once you have set up JupyterHub on a server, you will install the cdsdashboards package and make some simple configuration changes to jupyterhub_config.py.
ContainDS Dashboards now works on single-server JupyterHubs and also Kubernetes-based depending on configuration. See other requirements.
To continue installation please see Setup.
Support and Mailing List¶
For more background on this project and our related ContainDS Desktop product, please see our website: containds.com.
Please contact email@example.com with any comments or questions at all.
Also sign up to our email list to receive notifications about updates to the project including new features and security advice.
And there is a Gitter room for general chat with other community members.
If you are using ContainDS Dashboards in production please consider subscribing to a support plan. This will back future development of the project and is a great way to satisfy your business stakeholders that you are adopting sustainable and supported software.
It also helps you reach your corporate social responsibility goals since our open source software is used by academic and non-profit organizations.
Optionally, your name and logo can be featured on GitHub and our website.
- LocalProcessSpawner or SystemdSpawner
- The Littlest JupyterHub
- Kubernetes (Z2JH)
- User Guide
- Dashboards Menu
- Prepare Dashboard
- Create Dashboard
- Building the Dashboard
- Working with Dashboards
- Hub Options
- User Server Options
- JupyterLab Extension
- Fine Tune the User Experience
- Restrict which Users can Spawn Servers and Dashboards
- GitHub for Login and Repos
- Custom Launchers
- Technical and Legal
- Contact and Mailing List
ContainDS Dashboards source code can be found on GitHub here.