Using Plotly Dash

SherlockML 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:

$ source 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:

from flask import Flask

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

server = Flask('my app')
app = dash.Dash('Plotly Dash on SherlockML', server=server, url_base_pathname='/', csrf_protect=False)

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:server

If you now go onto your server through the servers page:


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. SherlockML supports hosting Plotly Dash applications. Head to the Apps tab in SherlockML, click New, followed by Plotly Dash. You will be prompted to enter a name for your app.


Choose a name and 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 server. This should be the name of the Python variable that Gunicorn will serve. Look for a line like server = Flask() in your source file.

Save your application by clicking the Save button, then click Start app to actually 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, SherlockML verifies that you are an observer in the project that the app belongs to.

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 SherlockML username or the email that they used to sign up to SherlockML, and assign them observer status.



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