ivancho523 / sklearn_flask_docker

Deployment of ML models using Python's Scikit-Learn + Flask + Docker

From DS to MLOPs

From Data Science to MLOPs workshop

Dataset

Breast Cancer Wisconsin (Diagnostic) Data Set

For this workshop we are going to work with the following dataset:

https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. n the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

Attribute Information:

1. ID number
2. Diagnosis (M = malignant, B = benign) 3-32)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension ("coastline approximation" - 1)

Virtual Environment

Firt we need to create a virtual environment for the project, to keep track of every dependency, it is also useful to use and explicit version of Python

Install the package for creating a virtual environment: $ pip install virtualenv

Create a new virtual environment $ virtualenv venv

Activate virtual environment $ source venv/bin/activate

Python packages

Now with the virtual environment we can install the dependencies written in requirements.txt

$ pip install -r requirements.txt

Train

After we have install all the dependencies we can now run the script in code/train.py, this script takes the input data and outputs a trained model and a pipeline for our web service.

$ python code/train.py

Web application

Finally we can test our web application by running:

$ flask run -p 5000

Docker

Now that we have our web application running, we can use the Dockerfile to create an image for running our web application inside a container

$ docker build . -t from_ds_to_mlops

And now we can test our application using Docker

$ docker run -p 8000:8000 from_ds_to_mlops

Test!

Test by using the calls in tests/example_calls.txt from the terminal