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ToDo:

  • [x]

API: creation and deployment

API setup

Install all project requirements with pip:

pip install -r requirements.txt

How to run the API?

We'll be using Uvicorn, a fast ASGI server (it can run asynchronous code in a single process) to launch our application. Use the following command to start the server:

uvicorn app.api:app \
    --host 0.0.0.0 \
    --port 5000 \
    --reload \
    --reload-dir app \
    --reload-dir models

Or

uvicorn app.api:app  --host 0.0.0.0 --port 8000  --reload  --reload-dir deploy-GAISSA --reload-dir app

In detail:

  • uvicorn app.api:app is the location of app (app directory > api.py script > app object);
  • --reload makes the server reload every time we update;
  • --reload-dir app makes it only reload on updates to the app/ directory;
  • --reload-dir models makes it also reload on updates to the models/ directory;

API running.

Now you can test the app:

We can now test that the application is working. These are some of the possibilities:

  • Visit localhost:5000

  • Use curl

    curl -X GET http://localhost:5000/

  • Access the API programmatically, e.g.:

    import json
import requests

response = requests.get("http://localhost:5000/")
print (json.loads(response.text))

  • Use an external tool like Postman, which lets you execute and manage tests that can be saved and shared with others.

Visit Swagger UI in http://localhost:5000/docs for documentation endpoint and select one of the models. The documentation generated via Redoc is accessible at the /redoc endpoint.

API User Interface in localhost:5000/docs endpoint.

To make an inference, click on the "Try it out" button and click execute.

You should obtain a "200" code response after executing the POST method of the model:

API response on Swagger UI.

API response on terminal.

API creation

The API in this project is freely inspired by the Made with ML tutorial: "APIs for Machine Learning" and FastAPI Lab.

Follow the guide https://madewithml.com/courses/mlops/api/

How to add a new model in this API?

  1. Add new model class. File: ../app/models.py
    • Add a new class according to your new model, parent class is Model().
    • Make sure NewModel.predict() method is implemented according to the model.
    • Add ML_task
  2. Create a model schema. File: ../app/schemas.py
    • Create a schema with one example.
  3. Add endpoint. File: ../app/api.py
    • According to the new model information (Model Class and schema), add the endpoint to enable POST requests and make predictions using the model.