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Deploying on Databricks

Databricks is a standard platform for data scientists and big data engineers. It can provide a web-based development environment connected to your data source within your cloud provider. Nevertheless, Databricks does not provide a direct path to developing a web application. This documentation will show you how to adapt your infrastructure so that you can create a Taipy application in your Databricks environment.


Taipy is currently only available with Databricks Standard runtimes.


We recommend using Databricks deployments for testing or demonstration purposes only.

In the current section, we consider the following as prerequisites:

  • Knowledge of Databricks.
  • Knowledge of SSH.
  • A Databricks cluster in operation.
  • A Linux-based machine that can communicate with your local machine and Databricks.
  • Running a Taipy application


If you don't have a Linux-based machine, you can use a cloud provider like Microsoft Azure to create one.

Running your application on Databricks

The first step consists in running your Taipy application on Databricks.
Let's consider the following application:

import taipy as tp

if __name__ == "__main__":
    gui = tp.Gui(page="# Getting started with *Taipy*")
    rest = tp.Rest(), rest, title="Taipy application")

Connect to Databricks and run the previous code in a Databricks Notebook. You should obtain an output similar to the following:

[2023-06-12 17:17:25,611][Taipy][INFO] Running in 'single_client' mode in notebook environment
INFO:Taipy:Running in 'single_client' mode in notebook environment
[2023-06-12 17:17:25,912][Taipy][INFO]  * Server starting on
INFO:Taipy: * Server starting on

As you can see on the output, the Taipy application is running on Databricks and listening on localhost (, which is not accessible from your browser. The next section shows you how to expose your application.

Exposing your application

Here is the global architecture of the solution. Global architecture

Since we can't connect to Databricks directly, we'll use an SSH tunnel between a proxy machine and the Databricks machine, enabling them to communicate. We'll also install Nginx on the Linux-based machine to route the request from your browser to your application.

In the end, the browser sends a request to Nginx on the Linux-based machine; Nginx will redirect this request to the SSH tunnel, which will redirect to the Taipy application.

Here is the technical architecture of the solution: Technical architecture


The Linux-based machine should be accessible from your network and from Databricks.

1- Configure the proxy using Nginx

We'll install and configure Nginx on the Linux-based machine to enable browser-based communication. Once configured, Nginx will redirect all incoming requests from the default HTTP port (80) to port 8080.

First, install Nginx. Then onfigure the redirection by copying the following content in /etc/nginx/sites-enabled/default:

server {
    listen 80;
    location / {
        proxy_pass http://localhost:8080;

        proxy_read_timeout          36000s;
        proxy_http_version          1.1;
        proxy_set_header            Upgrade $http_upgrade;
        proxy_set_header            Connection $connection_upgrade;
        proxy_set_header            Host $host;
        proxy_request_buffering     off;

And restart Nginx: systemctl restart nginx.

2 - Connect Databricks to the proxy machine

Open Databricks in a first tab, and press "Compute" and select your cluster.


Go to the "Apps" panel and select "Web Terminal". It opens a new tab with a shell.


In this shell, enter the following command, replacing username and machine-ip with your values: ssh -R 8080: <username>@<machine-ip>


If your SSH authentication is based on certificates, remember to upload them on Databricks.

You are now on the Linux-based machine, and the communication is verified. You may notice the option -R 8080: This option starts port forwarding from the machine to Databricks. Specifically, it forwards all packets from port 8080 to port 5000 on your Databricks notebook.

Running curl localhost:8080 should get your application's output while running on your Databricks notebook!


3 - Access your application

While your first Databricks tab is open, you should be able to access your application. On another tab of your browser, make sure you can access the application at http://<machine-ip>