Machine Learning on Docker

Hitesh Rathod
2 min readMay 27, 2021


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 pandasdset = pandas.read_csv(‘marks.csv’)dset = dset.valuespredictors = dset[:,:-1]targets = dset[:,-1]from sklearn.linear_model import LinearRegressionmodel = LinearRegression(),targets)y=model.predict([[9,13,7]])print(y)import joblibjoblib.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

Output of above code