Heroku Deployment

Heroku is a cloud platform as a service supporting several programming languages, including Python. Heroku is the default option for model deployment as a web service for whisk. In most cases, Heroku is free for proof-of-concept models and a low-cost, less complex approach compared to solutions like AWS Sagemaker in the long-run.

To deploy the web service residing in the app/ directory to Heroku, type whisk app create [NAME OF THE HEROKU APP].

Heroku ML Model Gotchas

Your project may require a couple of slight modifications to successfully deploy to Heroku. Below is a list of common issues.

Max Slug Size Limit

Heroku has a maximum slug size of 500 MB (after compression). If you project contains large dependencies (like Tensorflow), you could exceed this limit.

If your app exceeds the maximum slug size, try using cascading requirements.txt files similar to this Stack Overflow answer. For example:

|-- requirements
|   |-- common.txt   <- Contains requirements common to all environments.
|   |-- dev.txt      <- Specifies dev-only requirements and requires common.txt.
|   `-- prod.txt     <- Specifies Heroku-only requirements and requires common.txt.
`-- requirements.txt <- Requires requirements/prod.txt as Heroku looks for this file.


The Tensorflow library is greater than 500 MB and exceeds the Heroku slug size limit by itself. Use tensorflow-cpu as it consumes < 150 MB of disk space. Heroku also does not offer GPUs so there is loss in functionality.


If you are using NLTK, add a nltk.txt file to the project root directory with a list of corpora to download. See the Heroku NLTK docs for more information.


The project contains a .slugignore file that removes the data/ and notebooks/ directories before the buildpack runs. This reduces the slug size.

Python version

Specify a Python version to use on Heroku by adding a runtime.txt file to your project root. Learn more about specifying a Python runtime on Heroku.