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ML DAY 2 (24MCR019) CREATE AN DOCKER IMAGE FOR ML PROJECT
Step1: Create an Folder Structure and download the DataSet from Iris dataset from Kaggle. Step2: Create an Dockerfile for creating an docker image. We can get the dependencies "FROM PYTHON:3.9" , it is used to get the dependencies. We can run the required tools for running of the projects in this we have use pandas,matplotlib, scikit-learn for docker image to be run. "RUN PIP INSTALL PANDAS MATPLOTLIB SCIKIT-LEARN" We have to use Command "COPY . .", we have used it for source and destination. We have used to start container by docker image command. "CMD[python,"sample.py"]" Step 3: Create an docker image using "docker build -t username/filename:latest ." Step 4: Run the Docker image using Step 5: Create an new branch in github repository and change that particular repository. Step 6: push the changes using add , commit , push to specific origin.
Step1:
Create an Folder Structure and download the DataSet from Iris dataset from Kaggle.
Step2:
Create an Dockerfile for creating an docker image.
We can get the dependencies "FROM PYTHON:3.9" , it is used to get the dependencies.
We can run the required tools for running of the projects in this we have use pandas,matplotlib, scikit-learn for docker image to be run.
"RUN PIP INSTALL PANDAS MATPLOTLIB SCIKIT-LEARN"
We have to use Command "COPY . .", we have used it for source and destination.
We have used to start container by docker image command.
"CMD[python,"sample.py"]"
Step 3:
Create an docker image using
"docker build -t username/filename:latest ."
Step 4:
Run the Docker image using
Step 5:
Create an new branch in github repository and change that particular repository.
Step 6:
push the changes using add , commit , push to specific origin.
