sklearn_fastapi_docker

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sklearn_fastapi_docker

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

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# ScikitLearn + FastAPI + Docker
Deployment of ML models using Python's Scikit-Learn + FastAPI + Docker

# 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:

`$ uvicorn main:app`

# 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 sklearn_fastapi_docker`

And now we can test our application using Docker

`$ docker run -p 8000:8000 sklearn_fastapi_docker`

# Test!

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

sklearn_fastapi_docker

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