DEV Community: Praveen Kumar

🧠 Dockerize and Share Your ML Model: Logistic Regression with Iris Dataset

Praveen Kumar — Fri, 25 Apr 2025 16:56:32 +0000

📦 Project Structure
Here’s what our folder looks like:

ml-docker-project/
├── Dockerfile
├── model.py
└── Iris.csv

🐳 Dockerfile Breakdown
Here’s the Dockerfile we’re using:

# Use the official Python image as a base
FROM python:3.12-slim

# Set working directory inside the container
WORKDIR /app

# Copy all files from the local machine to the container
COPY . .

# Install required Python packages
RUN pip install --no-cache-dir pandas scikit-learn matplotlib

# Command to run your Python script
CMD ["python", "model.py"]

This will:

Use a slim Python base image
Copy your local files
Install all dependencies
Run model.py on container start

🧪 What model.py Does

The script:

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()  # Close the plot so it doesn't show up in prompt

# 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: Sepal/Petal measurements
Y = array[:, 5]    # Target: Species

# Step 6: Split data into training (67%) and testing (33%)
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")

Visualizes the data (and saves plots)

Trains a Logistic Regression model

Evaluates it

Saves the model as logistic_model.pkl

It’s a full training pipeline, ready to be reproduced in any environment that runs Docker. 💪

🔨 Step-by-Step: Build, Tag, and Push to DockerHub

1️⃣ Build the Docker Image
From your project directory:
docker build -t viratpk18/24mcr078:latest .

2️⃣ Log in to DockerHub
docker login

3️⃣ Push the Image
docker push viratpk18/24mcr078:latest

4️⃣ Then create the Dockerfile

📚 Resources

🐳 Docker for ML Projects
https://docs.docker.com/language/python/
🤖 scikit-learn Docs
https://scikit-learn.org/stable/
📊 matplotlib Gallery
https://matplotlib.org/stable/gallery/index.html

Mastering Git : Advanced Commands Every Developer Should Know

Praveen Kumar — Fri, 25 Apr 2025 16:42:01 +0000

🖋 Introduction

Git is the heart of modern development workflows. While most developers are familiar with git add, git commit, and git push, there's a whole world of advanced Git commands that can save time, prevent disasters, and help you debug like a pro. Let’s level up!

🔍 Why Go Beyond the Basics?

Understanding advanced Git operations helps:

Clean up messy histories

Fix mistakes without panic

Navigate and debug efficiently

Collaborate more effectively

⚙️ Advanced Git Commands

git reflog – Your Time Machine
bash
Copy
Edit
git reflog
Shows a log of where your HEAD and branch references have been. Useful for recovering lost commits!
git cherry-pick – Pick and Choose Commits
bash
Copy
Edit
git cherry-pick <commit-hash>
Apply specific commits from one branch into another. Great for patching bugs without merging entire branches.
git bisect – Debugging Power Tool
bash
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Edit
git bisect start
git bisect bad
git bisect good <commit>
Use binary search to find the exact commit that introduced a bug. Super handy in large codebases.
git stash – But Make It Fancy
bash
Copy
Edit
git stash save "WIP: fixing navbar"
git stash list
git stash pop
Temporarily shelve your changes. You can even apply them selectively using stash@{n}.
git rebase -i – Rewrite History Like a Boss
bash
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Edit
git rebase -i HEAD~3
Interactive rebasing lets you squash, edit, or reorder commits. Use it to clean up your commit history before a PR.
git reset – Know Your Levels
bash
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Edit
git reset --soft HEAD~1
git reset --mixed HEAD~1
git reset --hard HEAD~1
Roll back commits with precision. Soft keeps your changes, hard doesn’t.
git clean – Remove the Untracked
bash
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Edit
git clean -fd
Clears untracked files and directories. Be careful—it’s permanent!
git blame – Find Who Changed What
bash
Copy
Edit
git blame <file>
Annotates each line in the file with the commit and author. Useful for tracking bugs or changes.
git log – But Prettier
bash
Copy
Edit
git log --oneline --graph --decorate --all
See a visual, colorful graph of your commit history. Great for understanding merges and branches.

🚀 Pro Tips
Use aliases for long commands (git config --global alias.lg "log --oneline --graph --decorate")

Combine git stash with git checkout to switch branches safely

Before a pull request, use git rebase -i to squash commits

📚 Conclusion
These commands will give you superpowers in managing Git repositories. Don’t fear the terminal — embrace it. The more fluent you get with Git, the smoother your dev life will be.

🔗 Further Reading

📘 Pro Git Book
https://git-scm.com/book/en/v2

📝 Git Cheatsheet by GitHub Education
https://education.github.com/git-cheat-sheet-education.pdf

🧠 Learn Git Branching (Interactive Visual Tool)
https://learngitbranching.js.org

Git-Hub Commands

Praveen Kumar — Thu, 24 Apr 2025 04:53:03 +0000

Initialize a Git repository:
git init
Creates a new Git repository in the folder 24MCR078.

Add a file to staging area:
git add 24MCR078.txt

Adds 24MCR078.txt Commit the file:
git commit -m "Added Personal Details"
Creates a commit with the message "Added Personal Details".

Check Git status:
git status
Shows that 24MCR078.txt has been modified but not staged.

View commit log:
git log
Displays the commit history (one commit at this point).

Add remote GitHub repository:
git remote add origin https://github.com/viratpk18/24MCR078.git
Links the local repository to a remote GitHub repo.

Check current branch:
git branch
Shows the current branch is master.

Rename branch from master to main:
git branch -M main
Renames the current branch to main.

Set Git global config for email and username:
git config --global user.email "praveenkumar2k4.t@gmail.com"
git config --global user.name "viratpk18"
Sets your global Git identity.

Push code to remote repo for the first time:
git push -u origin main
Pushes the main branch to GitHub and sets upstream tracking

Then , Again We will Modify or add new file means we need to follow the same steps, like

git add .
git commit -m “message”
git push origin main