Train ML Model inside Docker Container
What is Docker ?
Docker is a software platform for building applications based on containers — small and lightweight execution environments that make shared use of the operating system kernel but otherwise run in isolation from one another. While containers as a concept have been around for some time, Docker, an open source project launched in 2013, helped popularize the technology, and has helped drive the trend towards containerization and microservices in software development that has come to be known as cloud-native development.
What is Machine Learning ?
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Task Description 📄
👉 Pull the Docker container image of CentOS image from Docker Hub and create a new container
👉 Install the Python software on the top of docker container
👉 In Container you need to train machine learning model
Step-1: Install the Docker
- Configure yum for Docker :
Create a file “/etc/yum.repos.d/docker.repo”[docker]
name=docker repo baseurl=https://download.docker.com/linux/centos/7/x86_64/stable/
Then check with “yum repolist” Some additional software can be seen.
2. Install Docker and start service :
# To Install docker
yum install docker-ce — nobest -y# To Start Docker service
systemctl start docker# To Enable Docker service
systemctl enable docker
Step-2 : Pull centos image from docker hub
# pull the cenots image with latest version
docker pull cenots:latest
Step-3 : Launch a container
docker run -it --name <name_of_OS> centos:latest
Step-4 : Install required packages for task
# To install python
yum install python36
# installing required libraries for taskpip3 install pandas
pip3 install scikit-learn
Step-5: Create a Docker image with commit
docker commit <os_name> <image_name>:<version>
Step-6 : Launch a container using that image
docker run -it --name <name> <image_name:version>
To verify use “docker images” command
Step-7: Copy the dataset which we want to work on
docker cp <location_in_local> <docker_os_name>:<location_in_docker_container>
To verify is it copied or not check inside the docker container
Step-8: Create Linear Regression Model
Step-9: Let’s test our model
The Output of our Machine Learning Model is
Thanks For Spending Your Time Here,