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Merge pull request #13 from aunchagaonkar/main
Add Jupyter Lab Docker environment setup
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‎Environments/AIML Environments/JupyterLab-Development/.dockerignore‎

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# Ignore these files and directories when building the Docker image
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README.md
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.git
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.gitignore
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notebooks/*
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data/*
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scripts/*
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*.log
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.DS_Store
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Thumbs.db
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.env

‎Environments/AIML Environments/JupyterLab-Development/Dockerfile‎

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# Use the official Jupyter base image with Python 3.11
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FROM jupyter/scipy-notebook:latest
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# Set the working directory
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WORKDIR /home/user/work
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# Switch to root to install system packages
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USER root
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# Install additional system packages if needed
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RUN apt-get update && apt-get install -y \
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git \
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curl \
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wget \
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vim \
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&& apt-get clean \
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&& rm -rf /var/lib/apt/lists/*
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# Switch back to user user
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USER user
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# Install additional Python packages
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COPY requirements.txt /tmp/requirements.txt
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RUN pip install --no-cache-dir -r /tmp/requirements.txt
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# Create directories for notebooks and data
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RUN mkdir -p /home/user/work/notebooks \
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&& mkdir -p /home/user/work/data \
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&& mkdir -p /home/user/work/scripts
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# Expose the Jupyter Lab port
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EXPOSE 8888
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# Set environment variables
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ENV JUPYTER_ENABLE_LAB=yes
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ENV JUPYTER_TOKEN=""
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# Start Jupyter Lab
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CMD ["start-notebook.sh", "--NotebookApp.token=''", "--NotebookApp.password=''"]

‎Environments/AIML Environments/JupyterLab-Development/README.md‎

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# Jupyter Lab Docker Setup
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This directory contains a complete Docker setup for running Jupyter Lab with a comprehensive data science and machine learning environment.
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## What's Included
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- **Jupyter Lab** - Latest version with lab interface
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- **Python 3.11** - Latest stable Python version
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- **Data Science Libraries**:
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- NumPy, Pandas, Matplotlib, Seaborn, Plotly
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- Scikit-learn, TensorFlow, PyTorch
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- Scipy, Bokeh, Altair
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- **Development Tools**:
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- Git, Jupyter extensions
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- Black formatter, Flake8 linter, Pytest
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- **Utilities**: Requests, BeautifulSoup, Excel support
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## Quick Start
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### Using Docker Compose (Recommended)
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1. **Start Jupyter Lab**:
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```bash
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docker-compose up -d
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```
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2. **Access Jupyter Lab**:
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Open your browser and go to: http://localhost:8888
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3. **Stop the service**:
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```bash
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docker-compose down
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```
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### Using Docker directly
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1. **Build the image**:
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```bash
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docker build -t jupyter-lab-ml .
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```
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2. **Run the container**:
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```bash
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docker run -p 8888:8888 -v $(pwd)/notebooks:/home/user/work/notebooks jupyter-lab-ml
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```
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## Directory Structure
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```
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Python-ML-Development/
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├── Dockerfile # Docker image definition
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├── docker-compose.yml # Docker Compose configuration
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├── requirements.txt # Python package dependencies
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├── README.md # This file
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├── notebooks/ # Your Jupyter notebooks (mounted volume)
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├── data/ # Data files (mounted volume)
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└── scripts/ # Python scripts (mounted volume)
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```
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## Features
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- **No Authentication**: Set up for development use (no token/password required)
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- **Persistent Storage**: Your notebooks, data, and scripts are saved on your host machine
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- **Auto-restart**: Container restarts automatically unless stopped manually
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- **Full Lab Interface**: Modern Jupyter Lab interface with extensions
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- **Git Integration**: Git is pre-installed for version control
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## Security Note
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This setup is configured for development use with no authentication. Do not expose this to the internet or use in production without proper security measures.
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## Customization
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- **Add packages**: Edit `requirements.txt` and rebuild the image
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- **Change port**: Modify the port mapping in `docker-compose.yml`
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- **Add system packages**: Edit the `Dockerfile` to install additional system dependencies
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## Troubleshooting
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- **Port already in use**: Change the port in `docker-compose.yml` from `8888:8888` to `8889:8888`
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- **Permission issues**: Make sure Docker has access to the project directory
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- **Container won't start**: Check logs with `docker-compose logs jupyter-lab`

‎Environments/AIML Environments/JupyterLab-Development/docker-compose.yml‎

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version: '3.8'
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services:
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jupyter-lab:
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build: .
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container_name: jupyter-lab-ml
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ports:
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- "8888:8888"
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volumes:
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- ./notebooks:/home/user/work/notebooks
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- ./data:/home/user/work/data
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- ./scripts:/home/user/work/scripts
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environment:
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- JUPYTER_ENABLE_LAB=yes
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- JUPYTER_TOKEN=""
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- GRANT_SUDO=yes
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restart: unless-stopped
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working_dir: /home/user/work
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command: start-notebook.sh --NotebookApp.token='' --NotebookApp.password='' --NotebookApp.allow_root=True

‎Environments/AIML Environments/JupyterLab-Development/notebooks/Getting_Started.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "9ad8eae8",
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"metadata": {},
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"source": [
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"# Welcome to Jupyter Lab Docker Environment\n",
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"\n",
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"This is a sample notebook to help you get started with your new Jupyter Lab environment."
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]
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},
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{
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"cell_type": "markdown",
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"id": "f7f48161",
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"metadata": {},
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"source": [
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"## 1. Testing Python Environment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "417706d5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"print(f\"Python version: {sys.version}\")\n",
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"print(f\"Python executable: {sys.executable}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c2e241b1",
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"metadata": {},
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"source": [
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"## 2. Testing Data Science Libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e06ce4a6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import essential libraries\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"print(\"NumPy version:\", np.__version__)\n",
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"print(\"Pandas version:\", pd.__version__)\n",
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"print(\"Matplotlib and Seaborn imported successfully\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0e8e326e",
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"metadata": {},
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"source": [
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"## 3. Simple Data Visualization Example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6f253828",
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(42)\n",
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"data = {\n",
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" 'x': np.random.randn(100),\n",
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" 'y': np.random.randn(100),\n",
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" 'category': np.random.choice(['A', 'B', 'C'], 100)\n",
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"}\n",
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"df = pd.DataFrame(data)\n",
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"\n",
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"# Create a simple plot\n",
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"plt.figure(figsize=(10, 6))\n",
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"sns.scatterplot(data=df, x='x', y='y', hue='category')\n",
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"plt.title('Sample Scatter Plot')\n",
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"plt.show()\n",
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"\n",
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"print(\"Visualization working correctly!\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5e866e79",
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"metadata": {},
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"source": [
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"## 4. Testing Machine Learning Libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2dd1055b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.datasets import make_classification\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.metrics import accuracy_score\n",
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"\n",
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"X, y = make_classification(n_samples=1000, n_features=4, n_classes=2, random_state=42)\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
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"\n",
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"model = RandomForestClassifier(random_state=42)\n",
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"model.fit(X_train, y_train)\n",
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"\n",
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"predictions = model.predict(X_test)\n",
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"accuracy = accuracy_score(y_test, predictions)\n",
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"\n",
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"print(f\"Model accuracy: {accuracy:.3f}\")\n",
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"print(\"Scikit-learn working correctly!\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e60a9faf",
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"metadata": {},
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"source": [
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"## 5. Environment Information"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2dc9777d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import subprocess\n",
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"import sys\n",
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"\n",
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"result = subprocess.run([sys.executable, '-m', 'pip', 'list'], \n",
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" capture_output=True, text=True)\n",
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"print(\"Installed packages:\")\n",
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"print(result.stdout)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a10236fb",
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"metadata": {},
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"source": [
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"## Next Steps\n",
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"\n",
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"Your Jupyter Lab environment is ready! Here's what you can do:\n",
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"\n",
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"1. **Create new notebooks** in the `notebooks/` directory\n",
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"2. **Add data files** to the `data/` directory\n",
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"3. **Write Python scripts** in the `scripts/` directory\n",
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"4. **Install additional packages** by adding them to `requirements.txt` and rebuilding the Docker image\n",
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"\n",
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"Happy coding! 🚀"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Environments/AIML Environments/JupyterLab-Development/requirements.txt‎

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# Core data science packages
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numpy==1.24.3
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pandas==2.0.3
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matplotlib==3.7.2
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seaborn==0.12.2
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plotly==5.15.0
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scipy==1.11.1
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# Machine learning libraries
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scikit-learn==1.3.0
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tensorflow==2.13.0
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torch==2.0.1
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torchvision==0.15.2
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# Jupyter extensions and tools
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jupyterlab==4.0.5
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jupyter-widgets==8.0.7
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ipywidgets==8.0.7
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jupyterlab-git==0.42.0
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# Data processing and utilities
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requests==2.31.0
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beautifulsoup4==4.12.2
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openpyxl==3.1.2
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xlrd==2.0.1
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# Visualization and interactive tools
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bokeh==3.2.2
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altair==5.0.1
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# Development tools
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black==23.7.0
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flake8==6.0.0
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pytest==7.4.0

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