Containerizing ML model — Task0 1

STEP 1: INSTALL DOCKER

STEP 2: START and check the SERVICE of Docker

systemctl start docker
systemctl status docker

STEP 3: PULL the Docker Image of centos

docker pull centos:latest

STEP 4: LAUNCH a docker container using the run command

docker run -it --name <name for your container> centos:latest

To Download:

→ Python

yum install python36

→ scikit-learn

pip3 install scikit-learn

→ git

yum install git -y

→ pandas

pip3 install pandas

STEP 5: Download your dataset in the docker container

git clone <your git repo url>

Dataset : salary.csv

result.py :

model.py :

STEP 6: RUN your python files

python3 model.py
python3 result.py

Finally, the model is trained and we got the Desired Result — Salary Prediction model !

OPTIONAL

docker commit <container name> <image name>:versiondocker login
docker tag <image name>:version <docker repo name>
docker push <docker repo name>

Successfully pushed now you can pull your own custom image and use the ML model whenever needed.

My docker image for Salary Prediction ML model:

docker pull 181930/salarypred:v1

THANKS FOR READING!

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Sangeeth Sahana D

Sangeeth Sahana D

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