agahkarakuzu / dash_heroku

Deploy a Dash application to Heroku through actions.

Deploy your Plotly-Dash app to Heroku in 5 steps 🚀

1. Fork this repository

2. Create a free Heroku account

3. Generate an API key on your account settings

4. Create an encrypted secret for your repo with the name HEROKU_API and value from step-3

5. Do the following changes in the GitHub Actions configuration:

 name: Deploy

 on:
   push:
     branches:
-      - none
+      - master

 jobs:
   build:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v2
       - uses: akhileshns/[email protected] # This is the action
         with:
           heroku_api_key: ${{secrets.HEROKU_API}}
-          heroku_app_name: "dash-brainhack"
+          heroku_app_name: "your-app-name"
-          heroku_email: "[email protected]"
+          heroku_email: "[email protected]" 

❗ Warning
Your heroku_app_name must be a unique name. If there exists a herokuapp with that name, the deployment will fail. You can check for availability here.

That's it!

Check the Actions tab of your repository, if the run is successful, your app will be available at:

https://your-app-name.herokuapp.com

Each time you push a commit to the master, action will be triggered to deploy the new version.

❗ Warning
Do not push commits to master one after another too frequently, it may break the deployment.

How to develop and debug your Dash app?

Assuming that you have a the datvis36 conda environment, you can follow these steps:

1. Clone your (forked) repository to your computer

git clone https://github.com/your-github-handle/dash_heroku.git

2. Activate datvis36 environment and navigate into the dash_heroku

source activate datvis36
cd /dash_heroku

3. Make sure that you've completed the first 5 steps to configure your deployment.

4. Modify your project as you like. Assuming that the app.py is your main entrypoint calling the app.run_server(debug=True) method, run the following in your terminal:

python app.py

This will start a local development server and your application will be accessible through an IP address (something like http://127.0.0.1:8050/) from your web browser.

It'll give you the following warning:

   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.

but don't worry, this is exactly what we are doing with gunicorn, Heroku and GitHub Actions :)

Each time you change app.py (or whatever_entry_point.py) and save the script, the Dashboard will update itself in debug mode. This is really useful to trace errors if your app crashes after your changes.

5. When you are ready to push your changes

git add .
git commit -m "Commit message" 
git push

If your push refers to master, Github Actions will be triggered and deploy the latest version of your app to Heroku!

☝️ Please use GitHub Actions fairly
It is not only a bad development practice to modify your app.py on GitHub editor and see if it works on production (https://your-app-name.herokuapp.com) each time you commit, but also a wasteful use of public compute resources.

⏱ Heroku gives you 550hrs per month for free
Quite generous, is not it! When your app does not receive web traffic for 30 minutes, it sleeps. If a sleeping web dyno receives web traffic, it will become active again after a short delay. Free web dynos do not consume free dyno hours while sleeping 🎉. You can find further details about free dyno hours here.

Multi-page Dash apps are possible!

Real-time fMRI dashboard by @jsheunis is a great example. If a single page is not enough for your purpose, you can use the structure of this project as a reference.

In rtfmri repo, pages is a module (simply a directory that contains __init.py__) that contains individual Dash apps to be displayed on separate pages. These pages are accessed from a front page, which is defined by index.py script placed at the root of the repository. This script serves as a main entry point, in which app.run_server() call is made. Sub-pages do not call this function, but just describe their layout and callbacks. They are imported into the index.py:

from pages import home, page1, page2, page3, page4

You can inspect the rest of the index.py to grasp how to handle multi-page layouts.

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Note that the differences in Procfile: here, it contains web: gunicorn app:server. Whereas on the rtfmri repo it contains web: gunicorn index:server.

The Procfile configuration is depends on where we make the following call:

if __name__ == '__main__':
    app.run_server(debug=True)

In our repo, this call is made in app.py, whereas in rtfmri it is in index.py. Bottomline, if you'd like to create a multi-page dash application pay attention to this detail before you let GitHub actions deploy your application.

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More examples to be inspired by

More examples are available by Plotly Dash, including a brain viewer.