The DEEPaaS API enables a user friendly interaction with the underlying Deep Learning modules and can be used both for training models and doing inference with services.

For a detailed up-to-date documentation please refer to the official DEEPaaS documentation.

Integrate your model with the API

To make your Deep Learning model compatible with the DEEPaaS API you have to:

1. Define the API methods for your model

Create a Python file (named for example inside your package. In this file you can define any of the API methods. You don’t need to define all the methods, just the ones you need. Every other method will return a NotImplementError when queried from the API. For example:

  • Enable prediction: implement get_predict_args and predict.
  • Enable training: implement get_train_args and train.
  • Enable model weights preloading: implement warm.
  • Enable model info: implement get_metadata.

Here is an example of the implementation of the methods. You can also browse our github repository for more examples.

2. Define the entrypoints to your model

You must define the entrypoints pointing to this file in the setup.cfg as following:

deepaas.v2.model =
    pkg_name = pkg_name.deep_api

Here is an example of the entrypoint definition in the setup.cfg file.


When developing a model with the DEEP Data Science template, the Python file with the API methods will automatically be created at pkg_name/models/, as well as the entrypoints pointing to it. This path can of course be modified.

Running the API

To start the API run:

deepaas-run --listen-ip

and go to You will see a nice UI with all the methods: