DEV Community: karthik santhosh

🚀 How to Create a Docker Image and Push It to DockerHub

karthik santhosh — Mon, 28 Apr 2025 16:26:36 +0000

Hey folks! 👋

In this post, I'll show you how to create a Docker image for a simple Machine Learning Python program and push it to DockerHub. 🚀

We’ll create a small ML project using the Iris dataset, logistic regression, and model saving.

Let’s get started! 🐳

1️⃣ Create a Python Program

First, create a Python file named hello_world_ml.py with the following code:

from pandas import read_csv
from matplotlib import pyplot
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import joblib

# Step 1: Load dataset
filename = "Iris.csv"
data = read_csv(filename)

# Step 2: Display data shape and preview
print("Shape of the dataset:", data.shape)
print("First 20 rows:\n", data.head(20))

# Step 3: Plot and save histograms silently
data.hist()
pyplot.savefig("histograms.png")
pyplot.close()

# Step 4: Plot and save density plots silently
data.plot(kind='density', subplots=True, layout=(3,3), sharex=False)
pyplot.savefig("density_plots.png")
pyplot.close()

# Step 5: Convert to NumPy array and extract features/labels
array = data.values
X = array[:, 1:5]  # Features
Y = array[:, 5]    # Labels

# Step 6: Split data into training and testing
test_size = 0.33
seed = 7
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)

# Step 7: Create and train logistic regression model
model = LogisticRegression(max_iter=200)
model.fit(X_train, Y_train)

# Step 8: Evaluate and display accuracy
result = model.score(X_test, Y_test)
print("Accuracy: {:.2f}%".format(result * 100))

# Step 9: Save the trained model to a file
joblib.dump(model, "logistic_model.pkl")

✅ Make sure you also have the Iris.csv dataset in the same folder.

2️⃣ Create a Dockerfile

Next, create a file named Dockerfile in the same directory:

# Use a lightweight Python image
FROM python:3.12-slim

# Set the working directory
WORKDIR /app

# Copy all files into the container
COPY . .

# Install required libraries
RUN pip install pandas matplotlib scikit-learn joblib

# Define the command to run the app
CMD ["python", "hello_world_ml.py"]

Explanation:

Base image: Python 3.12 slim.
Working directory: /app.
Installs all necessary Python libraries.
Runs the hello_world_ml.py script when container starts.

3️⃣ Build the Docker Image

Open your terminal, navigate to your project folder, and run:

docker build -t your-dockerhub-username/ml-docker-app:latest .

Example:

docker build -t myusername/ml-docker-app:latest .

✅ This command builds the Docker image and tags it.

4️⃣ Login to DockerHub

If not already logged in, run:

docker login

Enter your DockerHub username and password when prompted.

5️⃣ Push the Docker Image to DockerHub

After building and logging in, push the image:

docker push your-dockerhub-username/ml-docker-app:latest

Example:

docker push myusername/ml-docker-app:latest

✅ This uploads your image to your DockerHub repository.

6️⃣ Pull and Run the Image from Anywhere

Anyone can now pull and run your ML app like this:

docker pull myusername/ml-docker-app:latest
docker run myusername/ml-docker-app:latest

🎯 The container will load the Iris dataset, train a logistic regression model, save graphs, and display model accuracy!

Example output:

Shape of the dataset: (150, 6)
First 20 rows:
    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
0   1            5.1           3.5            1.4           0.2  Iris-setosa
...
Accuracy: 94.00%

—

🔥 Final Tips

Add .dockerignore to avoid copying unnecessary files.
Keep your Docker images lightweight.
Make sure your Iris.csv is included while copying files (COPY . . does this).
You can extend this with Flask API for serving predictions!

—

📢 That's it!

You now know how to Dockerize a Machine Learning Python app and share it worldwide via DockerHub. 🐳✨

If you found this helpful, smash that ❤️ and share it with your dev friends!

Until next time, Happy Coding! 🚀

🚀 Level Up with These 10+ Useful Git Commands

karthik santhosh — Fri, 25 Apr 2025 20:54:14 +0000

By Karthik Santhosh

Git is a powerful tool for developers — not just for saving your code, but for managing versions, collaborating, and resolving issues like a pro. In this post, let’s walk through some essential and advanced Git commands that can boost your workflow.

1. 🔍 `git diff`

Purpose: Show changes between files, commits, or branches.

Use Case: Compare your working directory with the index or a previous commit.

git diff

2. 📜 `git log`

Purpose: Display the full commit history.

Use Case: Track changes, find specific commits, or understand project history.

git log

3. 📥 `git clone`

Purpose: Clone a remote repository to your local machine.

git clone https://github.com/karthikio/24MCR050

4. ⬇️ `git pull`

Purpose: Fetch and merge changes from the remote repo to your current branch.

git pull origin main

5. ⬆️ `git push`

Purpose: Push your local commits to the remote repository.

git push origin main

6. 🕵️ `git blame`

Purpose: Show who last modified each line of a file and when.

Use Case: Great for debugging or understanding change history.

git blame ML.txt

7. ⚔️ Merge Conflicts (Concept)

Note: Merge conflicts happen when two branches modify the same part of a file.

To simulate a merge conflict:

git checkout -b new-branch
# Make changes, commit them
git checkout main
# Make conflicting changes
git merge new-branch
# Conflict occurs here

You'll need to manually resolve the conflict in the files and commit the resolution.

8. 🌿 `git branch`

Purpose: List, create, or delete branches.

git branch           # List branches  
git branch feature   # Create a branch  
git branch -d feature  # Delete a branch

9. 🚀 `git checkout -b`

Purpose: Create and switch to a new branch in one step.

git checkout -b advanced-git-commands

10. 📁 `.gitignore`

Purpose: Tell Git which files or folders to ignore in version control.

Steps to set up:

Create a .gitignore file in your root directory.
Add patterns of files or folders to ignore.

Example:

*.log
node_modules/
.env

No command is needed — Git will automatically skip tracking files that match the patterns.

📦 Bonus: GitHub Repo

You can check out the repository related to this guide here:

👉 GitHub - 24MCR050

💡 Final Thoughts

These Git commands may look simple, but mastering them gives you better control, cleaner workflows, and confidence when collaborating with teams. Whether you're debugging, exploring history, or working on features — Git's got your back.

If you liked this post, drop a ❤️ or comment your favorite Git command below!

🧑‍💻 Written by Karthik Santhosh

Full-Stack Developer | React & Firebase | Open Source Learner

🚀 Step-by-Step Git Commands for First-Time Repo Setup (with GitHub)

karthik santhosh — Wed, 23 Apr 2025 16:51:47 +0000

1. 📁 Initialize a Git repository

git init

Creates a new local Git repository in your current directory (e.g., 24MCR001).

2. ➕ Add a file to the staging area

git add 24MCR050.txt

Stages 24MCR050.txt to be included in the next commit.

3. ✅ Commit the staged file

git commit -m "Added Personal Details"

Creates a commit with a clear message describing the changes.

4. 📋 Check the Git status

git status

Shows the current status of your working directory and staging area — useful to confirm what’s staged or modified.

5. 🕓 View commit history

git log

Displays a list of commits, starting with the most recent.

6. 🌐 Link your local repo to GitHub

git remote add origin https://github.com/karthikio/24MCR050.git

Connects your local repository to a remote GitHub repository.

7. 🌿 Check the current branch

git branch

By default, Git creates a branch named master.

8. 🔀 Rename branch to `main` (recommended)

git branch -M main

Renames master to main — the new standard for default branches.

9. 👤 Set your global Git config

git config --global user.email "developer.karthiksanthosh@gmail.com"
git config --global user.name "karthikio"

Sets your identity globally for Git commits.

10. 🚀 Push your code to GitHub

git push -u origin main

Pushes the main branch to the remote repository and sets the upstream so you can use git push and git pull without arguments next time.

🔄 Updating Files Later

Once you've done the initial setup, any time you modify or add files, follow these steps again:

git add .
git commit -m "Your commit message"
git push origin main

This will stage all changes, commit them with a message, and push to the main branch on GitHub.