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Create, deploy, and share dashboards and apps

Analyzing data with visualizations provides insights, and a dashboard stitches these insights into a meaningful story. There are many great open source dashboards tools out there that you can use to organize and display your data in an engaging and digestible way.

In this tutorial, you'll learn how to create a new dashboard with Panel within Nebari. You'll also learn how to share your newly created dashboard with other users using JHub App Launcher.


JHub App Launcher was added in Nebari version 2024.1.1. Until version 2023.7.1, Nebari used CDS Dashboards for dashboard sharing. This page has instructions for both tools. Since, CDS Dashboards is deprecated, the documentation will be removed soon.

Supported frameworks

This tutorials demonstrates a Panel dashboard built with HoloViews and Bokeh as the backend, but Nebari supports several other frameworks:

JHub App launcher supports Panel, Bokeh, Streamlit, Plotly Dash, Voila, Gradio, JupyterLab, Generic Python Command.

Create the dashboard

1. Create environment and notebook

Create a new environment in conda-store for your work with the libraries needed to run your notebook.

To use JHub Apps, your environment must include jhub-apps and the corresponding dashboard/app creation framework, in addition to other libraries required in the notebook.

Hence, for this tutorial:

- pandas
- panel
- holoviews
- bokeh
- jupyter_bokeh
- jhub-apps

Launch JupyterLab in Nebari, create a new Jupyter Notebook with a meaningful name (such as panel-trees-dashboard.ipynb), and select the environment your created for this notebook from the select kernel dropdown (this dropdown menu is located in the top right corner of your notebook).

2. Create a panel dashboard

Copy the code below into a code cell of your notebook:

import pandas as pd
import holoviews as hv
from bokeh.models import HoverTool
import panel as pn


# creating a sample dataset
data_trees = { 'species_name': ['live oak', 'pecan', 'bur oak', 'cedar elm'],
'avg_diameter_inch': [20, 30, 40, 35]

df = pd.DataFrame(data_trees)

# adding curve/line and bar plots
plot_bar = hv.Bars(df, 'species_name', 'avg_diameter_inch')
plot_curve = hv.Curve(df)

# creating hover tooltip
hover = HoverTool(tooltips=[("avg diameter", "@avg_diameter_inch"),
("species", "@species_name")])
# plot customization
combine_plot = plot_bar.opts(tools=[hover]) + plot_curve.opts(line_dash='dashed')

# creating a dashboard using panel
dashboard = pn.template.BootstrapTemplate(
site="About 🌳",
title="Species and more",

You can run all the cells in your notebook and view the Panel dashboard using the "Preview with Panel" button in the notebook toolbar:

`About 🌳 - Species and more` dashboard screenshot displaying a bar and line chart of avg_diameter_inch vs species_name

This interactive feature of Panel makes it possible to rapidly prototype and iterate on dashboards. Feel free to add more plots or different styles to your plots!

Deploy the dashboard with JHub App Launcher

  1. In the Nebari Home Page (in the top navigation, Nebari -> Hub Control Panel) click on "Create App" to create a new web application for your dashboard.

  2. In the app creation interface, enter or select the following:

    • Display Name - Provide meaningful name for your application
    • Description (optional) - Add addition information about the application
    • Thumbnail (optional) - Choose a meaningful thumbnail for your application. The default thumbnail is the application framework's logo.
    • Framework - Select the framework used by your application. For this tutorial, select Panel.
    • Filepath - Path (from root in JupyterLab) to your application code file. For this tutorial, path to the Jupyter Notebook.
    • Conda Environment - Same environment used while developing your notebook/script which has jhub-apps and the corresponding framework.
    • Spawner profile - Instance type (i.e. machines with CPU/RAM/GPU resources) required for running your application.
    • Allow Public Access - Toggle to share the application with your team.

  3. Click on Submit. JHub App Launcher deploy your app (which can take a few minutes to complete) and automatically redirect you to it.

Your dashboard app will be available in the Nebari Home page, under "My Apps". If you allowed public access, it will be available under "Shared Apps" for your team.

Deploy and share the dashboard with CDS Dashboards (Nebari v2023.7.1 or earlier)

In this section, you'll use CDS Dashboards to publish and share your newly created panel dashboard.


CDS Dashboards has been deprecated in 2023.9.1. Nebari 2023.7.1 is the last release that support CDS Dashboards.

To begin, click on the top left tab navigate to File -> Hub Control Panel -> Dashboards.

JupyterLab expanded File menu - Hub Control Panel is highlighted with a surrounding purple box

Click on the button New Dashboard. You will now be presented with a new window where you'll need to provide additional details for your dashboard (see image below for reference).

CDS dashboard configuration screenshot

  1. Give your dashboard a name, for example, Trees. This name will be the name of your shareable dashboard, so make sure to give this a meaningful name.
  2. Add a short description, for example, Insights and more.
  3. Set the correct user-access permission (optional). This setting allows you to share your dashboard with all the other users on your Nebari deployment or select specific users.
  4. Select the code source for your panel. For example, in this tutorial you created a new notebook panel-trees-dashboard.ipynb, but you can also point to a Git repository.
  5. Select the appropriate framework for your dashboard, in this example you'll have to select: panel.
  6. Select the conda environment for your dashboard, make sure it is same as the one you previously selected as your Jupyter notebook environment
  7. In the relative path box, copy your notebook's path (example: demo-dashboards/tutorial/panel-trees-dashboard.ipynb).
  8. Once you have provided all the details above click on the save button.

You will then be redirected to a new window where you will be able to select the compute resources for your dashboard.


The available compute instances might vary depending on the configuration and cloud provider of your Nebari instance.

Also, the best instance type for your dashboard will depend on your specific use case.

An example of available compute instances available within a Nebari instance is shown in the following image: Nebari Instance selection UI screenshot for the Trees Dashboard. The radio button for the `Small instance - Stable environment with 1 CPU / 4 GB ram is selected

For this particular tutorial, a small instance should be enough. Once you have made a selection you can click on the Save button at the bottom of the window. This will trigger the deployment of your dashboard, and you'll be presented with a screen displaying the of this process.

Nebari window displaying the progress of the Trees' dashboard deployment. This window displays a message reading "The dashboard is starting up"

If there are no errors encountered during this process, you will be automatically redirected to the dashboard!

Manage apps in Nebari

All applications are available on the Nebari home page. From JupyterLAb, you can click on the Nebari menu tab and select Hub Control Panel to go to the home page.

To manage an application, click on the three dots in the top right of the corresponding application card where you can:

  • Start the app is it's not running
  • Stop a running app
  • Edit the application details
  • Delete the app


While the dashboard is running, it will continue to consume resources. You should be mindful of the incurring ongoing costs while the dashboard is running, and stop it when not needed.

Dashboards and apps can be a very handy tool to share information and insights with colleagues and external customers or collaborators. You can use this basic dashboard to build more complex dashboards, add more dynamic features, and start sharing data insights with others.