🚀 Dockerizing a Machine Learning Model 🧠🐳Creating Dockerfile 📄Building Image 🏗️ and Pushing to Docker Hub ☁️📦

SATHISH — Sat, 26 Apr 2025 04:16:41 +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 sathishs021/24mcr093:latest .

2️⃣ Log in to DockerHub
docker login

3️⃣ Push the Image
docker push sathishs021/24mcr093: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

GITHUB COMMANDS

SATHISH — Thu, 24 Apr 2025 08:13:54 +0000

Initialize a Git repository:
git init
Creates a new Git repository in the folder 24MCR093.

Add a file to staging area:
git add 24MCR093.txt
Adds 24MCR093.txt to the staging area.

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 24MCR093.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/sathish-s704/24MCR093.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 "sathishsathish75363@gmail.com"

git config --global user.name "sathish-s704"

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

DEV Community: SATHISH

🚀 Dockerizing a Machine Learning Model 🧠🐳Creating Dockerfile 📄Building Image 🏗️ and Pushing to Docker Hub ☁️📦

GITHUB COMMANDS