Machine Learning on Docker

Summer Program — TASK 1

Task Description :

Run machine learning code written in python language on top of Docker container.

Let’s start with the task :

Step 1 : First get docker installed in Redhat or any Linux OS

command : yum install docker

Docker is installed

Step 2 : Pull CentOS image with Latest version

command : docker pull centos:latest

Image Pulled

Step 3 : Install and run a new container and Install python on Container

command : docker run -it — name=’machine2’ centos:latest

command : yum install python3

Installing python

Step 4 : Get the data set having .csv extension and write machine learning code in a file having .py extension (Not Necessary) . This both files should be there in same directory .

import pandas
dset = pandas.read_csv(‘marks.csv’)
dset = dset.values
predictors = dset[:,:-1]
targets = dset[:,-1]
from sklearn.linear_model import LinearRegression
model = LinearRegression()
x=model.fit(predictors,targets)
y=model.predict([[9,13,7]])
print(y)
import joblib
joblib.dump(model,’marks_predictor.pk1')

Step 4 : Run this file by python interpreter . If error come us showing some libraries missing then install them using pip3.

command : pip3 install pandas

command : pip3 install sklearn

Installing Pandas

Installing Sklearn

Step 5 : Now run file with python interpreter .

command : python3 marks.py

Output of above code