Dockerize ML Project and Push to Docker Hub

This post is about building a Docker image for a simple machine learning project and pushing it to Docker Hub. It walks you through each step from writing a Dockerfile to verifying the image on Docker Hub with real command line examples and screenshots from the actual build and push process. Prerequisites Docker installed on your system. A Docker Hub account. A project with a Dockerfile (we’ll use a Python-based ML app in this case). Step 1: Create a Dockerfile In your project directory, create a file named Dockerfile (without extension) and add the following: FROM python:3.11-slim WORKDIR /app COPY . . RUN pip install pandas scikit-learn matplotlib CMD ["python", "your_script.py"] Make sure to replace your_script.py with your actual entry-point Python file. Step 2: Build the Docker Image Open your terminal and navigate to the project directory. Then run: docker build -t yourusername/imagename:tag . Example: docker build -t sathiya9944/24mcr094-ml:latest . This command builds the Docker image using the Dockerfile. Step 3: Login to Docker Hub Before pushing your image, log in to Docker Hub: docker login Enter your Docker Hub username and password when prompted. Step 4: Push the Image to Docker Hub Once logged in, push the image using: docker push yourusername/imagename:tag Example: docker push sathiya9944/24mcr094-ml:latest Screenshots Docker Build Output Docker Push Output Conclusion Now Docker image was built and pushed to Docker Hub using a real machine learning project. This image is now ready to be shared with others or deployed across different environments with ease.

Apr 25, 2025 - 17:56

Dockerize ML Project and Push to Docker Hub

This post is about building a Docker image for a simple machine learning project and pushing it to Docker Hub. It walks you through each step from writing a Dockerfile to verifying the image on Docker Hub with real command line examples and screenshots from the actual build and push process.

Prerequisites

Docker installed on your system.
A Docker Hub account.
A project with a Dockerfile (we’ll use a Python-based ML app in this case).

Step 1: Create a Dockerfile

In your project directory, create a file named Dockerfile (without extension) and add the following:

FROM python:3.11-slim

WORKDIR /app

COPY . .

RUN pip install pandas scikit-learn matplotlib

CMD ["python", "your_script.py"]

Make sure to replace your_script.py with your actual entry-point Python file.

Step 2: Build the Docker Image

Open your terminal and navigate to the project directory. Then run:

docker build -t yourusername/imagename:tag .

Example:

docker build -t sathiya9944/24mcr094-ml:latest .

This command builds the Docker image using the Dockerfile.

Step 3: Login to Docker Hub

Before pushing your image, log in to Docker Hub:

docker login

Enter your Docker Hub username and password when prompted.

Step 4: Push the Image to Docker Hub

Once logged in, push the image using:

docker push yourusername/imagename:tag

Example:

docker push sathiya9944/24mcr094-ml:latest

Screenshots

Docker Build Output

Docker Push Output

Conclusion

Now Docker image was built and pushed to Docker Hub using a real machine learning project. This image is now ready to be shared with others or deployed across different environments with ease.