Using Plotly Dash

Faculty supports building Plotly Dash applications. Plotly Dash offers a flexible interface for building interactive dashboards entirely in Python (you don’t need to write any JavaScript). For additional examples, check out the Plotly Dash gallery and our own examples section.

Developing the application

When you are just starting out, you probably want to develop the application without exposing it to other people in your project.

The easiest way of doing this is to just create a Jupyter server, open a terminal in that server and run the following commands:

$ conda activate Python3
$ pip install dash dash-renderer dash-html-components dash-core-components plotly
$ sudo sv stop jupyter

This has stopped the Jupyter notebook running on that instance, freeing the port for our application.

Let’s start by creating a directory in the project workspace:

$ mkdir -p /project/dash-example

Let’s now write the code for our application. We will create an application that predicts whether someone is a cat-person or a dog-person based on their name. Create a file called in /project/dash-example, with the following contents:

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

app = dash.Dash(__name__)

app.layout = html.Div(children=[
    html.H1(children='Are you a cat person?'),
    html.Label('Your name: '),
    html.Div(id='output-div', children=[])

        Output(component_id='output-div', component_property='children'),
        [Input(component_id='input-div', component_property='value')]
def update_output(input_value):
    if input_value is None or not input_value:
        return ['You have not typed your name yet.']
    if input_value == 'Heisenberg':
        return ['You are a cat person.']
        return ['You are a dog person.']

if __name__ == '__main__':
    app.run_server(host='', port=8888, debug=True)

This code defines a minimal application that listens on port 8888, the port that we have freed by stopping Jupyter. Let’s now start our app:

$ cd /project/dash-example
$ python


Running the app with python runs the app with its development server in debug mode (see app.run_server() in the example code above), which provides nice features like automatic reloading of the app when the code is changed.

However, this is not suitable for use in deployed applications, where we instead use gunicorn, a production-ready Python HTTP server. If you want to run the app in the same way as it will be run in production, install gunicorn and gevent from pip (pip install gunicorn gevent) and run the app with:

$ gunicorn --workers 4 --worker-class gevent --bind app:app.server

If you now open your server from the servers section of the workspace:


You will see your application!


Carry on developing your app. When you save changes to your code, the app will automatically reload itself (unless you are running it with gunicorn, in which case you will need to first stop it by typing Ctrl-C in the terminal in which you started the app, and restart it by running the same command).

Deploying the application

You have now developed a great dashboard, and you want to let other members of your project access it. Faculty supports hosting Plotly Dash applications. Head to the Deployments page in Faculty, and in the Apps tab click the + button above the tab to create a new app. You will be prompted to enter a name and domain for your app. Select Plotly Dash for Type.


Click Create App. You will then be taken to the App Settings page.

You will need to make the following changes to the application settings:

  • Change the working directory to /project/dash-example.

  • Change the python module to app. This should be the name of the file containing the app, without .py.

  • Change the python object to app.server. This should be the name of the Python variable that gunicorn will serve.

Save your application by clicking the Save button, then click Start app to start your Plotly Dash server. After a few seconds, you will see the status of your app change to Running. At this point, a URL will appear. Select that URL and place it in your browser search bar. You will be taken to the application! Behind the scenes, Faculty verifies that you are at least an observer in the project that the app belongs to. While your app is deployed, you can monitor its logs in the Monitor tab.

Deploying Flask and Django applications

Plotly Dash applications are deployed using Gunicorn. You can leverage this to deploy any WSGI web framework. This lets you, in particular, deploy Flask and Django applications.

Flask applications will work out of the box. Your application will contain a file with a line like:

server = Flask(...)

When creating the deployment in Faculty, choose the Python module containing that line for python module. For instance, if that line is in a file called, the python module will be app. Choose server as the python object.

For Django applications, you will need to create an environment that installs django through pip. Your application should have a file called <project_name>/ containing the following lines:

from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()

When creating the deployment through the deployment tab, choose <project_name>.wsgi as the python module and application as the python object.

See the Django documentation on WSGI for more information.

Sharing your application

To share the application with a team member who is an observer in the project, just give them the URL of the application!

To invite people to your project, go to the Collaborators page, enter their Faculty username or the email that they used to sign up to Faculty, and assign them observer status.


For ideas on how to develop a great dashboard with Plotly Dash, check out our examples: