Publishing Machine Learning API with Python Flask

Flask is fun and easy to setup, as it says on Flask website. And that's true. This microframework for Python offers a powerful way of annotating Python function with REST endpoint. I’m using Flask to publish ML model API to be accessible by the 3rd party business applications.

This example is based on XGBoost.

For better code maintenance, I would recommend using a separate Jupyter notebook where ML model API will be published. Import Flask module along with Flask CORS:

Model is trained on Pima Indians Diabetes Database. CSV data can be downloaded from here. To construct Pandas data frame variable as input for model predict function, we need to define an array of dataset columns:

Previously trained and saved model is loaded using Pickle:

It is always a good practice to do a test run and check if the model performs well. Construct data frame with an array of column names and an array of data (using new data, the one which is not present in train or test datasets). Calling two functions — model.predict and model.predict_proba. Often I prefer model.predict_proba, it returns probability which describes how likely will be 0/1, this helps to interpret the result based on a certain range (0.25 to 0.75 for example). Pandas data frame is constructed with sample payload and then the model prediction is executed:

Flask API. Make sure you enable CORS, otherwise API call will not work from another host. Write annotation before the function you want to expose through REST API. Provide an endpoint name and supported REST methods (POST in this example). Payload data is retrieved from the request, Pandas data frame is constructed and model predict_proba function is executed:

Response JSON string is constructed and returned as a function result. I’m running Flask in Docker container, that's why using 0.0.0.0 as the host on which it runs. Port 5000 is mapped as external port and this allows calls from the outside.

While it works to start Flask interface directly in Jupyter notebook, I would recommend to convert it to Python script and run from command line as a service. Use Jupyter nbconvert command to convert to Python script:

jupyter nbconvert — to python diabetes_redsamurai_endpoint_db.ipynb

Python script with Flask endpoint can be started as the background process with PM2 process manager. This allows to run endpoint as a service and start other processes on different ports. PM2 start command:

pm2 start diabetes_redsamurai_endpoint_db.py

pm2 monit helps to display info about running processes:

ML model classification REST API call from Postman through endpoint served by Flask:

More info:

- GitHub repo with source code
- Previous post about XGBoost model training

TensorFlow Linear Regression Model Access with Custom REST API using Flask

In my previous post - TensorFlow - Getting Started with Docker Container and Jupyter Notebook I have described basics about how to install and run TensorFlow using Docker. Today I will describe how to give access to the machine learning model from outside of TensorFlow with REST. This is particularly useful while building JS UIs on top of TensorFlow (for example with Oracle JET).

TensorFlow supports multiple languages, but most common one is Python. I have implemented linear regression model using Python and now would like to give access to this model from the outside. For this reason I'm going to use Flask, micro-framework for Python to allow simple REST annotations directly in Python code.

To install Flask, enter into TensorFlow container:

docker exec -it RedSamuraiTensorFlowUI bash

Run Flask install:

pip install flask

Run Flask CORS install:

pip install -U flask-cors

I'm going to call REST from JS, this means TensorFlow should support CORS, otherwise request will be blocked. No worries, we can import Flask CORS support.

REST is enabled for TensorFlow model with Flask in these two lines of code:

As soon as Flask is imported and enabled we can annotate a method with REST operations and endpoint URL:

There is option to check what kind of REST operation is executed and read input parameters from POST request. This is useful to control model learning steps, for example:

If we want to collect all x/y value pairs and return in REST response, we can do that by collecting values into array:

And construct JSON response directly out of the array structure using Flask jsonify:

After we run TensorFlow model in Jupyter, it will print URL endpoint for REST service. For URL to be accessible outside TensorFlow Docker container, make sure to run TensorFlow model with 0.0.0.0 as in the screenshot below:

Here is example of TensorFlow model REST call from Postman. POST operation is executed payload and response:

All REST calls are logged in TensorFlow:

Download TensorFlow model enabled with REST from my GitHub.