🐍 THE BIG HEAVY PYTHON DEVELOPMENT MONSTER

Ubuntu 24.04 Python Development Container with Perfect UID/GID Matching

This is the ultimate Python development container - a massive, comprehensive Ubuntu 24.04-based devcontainer that includes EVERYTHING you could possibly need for modern Python development, data science, machine learning, web development, and more, with perfect user ID matching to your host system.

🎯 Key Features

✅ Perfect User Management

• UID/GID matching with your host system (crucial for WSL file permissions)
• No more permission issues with files created in the container
• Proper sudo access without password prompts

🏗️ Based on Ubuntu 24.04

• Latest Ubuntu LTS with all the newest packages
• Built from scratch as a custom Dockerfile (not a pre-built image)
• Full control over every component

🐍 THE PYTHON MONSTER INCLUDES

Core Python Stack

• Multiple Python Versions (3.12, 3.11, 3.10) with pyenv
• Package Managers: pip, poetry, pipenv, conda/mamba
• Virtual Environment Tools: venv, virtualenv, pipenv, conda
• Python Build Tools: setuptools, wheel, build, twine, cython
• Code Quality: black, isort, flake8, pylint, mypy, bandit, ruff
• Testing Frameworks: pytest, unittest, tox, nox, hypothesis

Data Science & Machine Learning

• Core Libraries: numpy, pandas, matplotlib, seaborn, plotly
• Machine Learning: scikit-learn, tensorflow, pytorch, xgboost, lightgbm
• Deep Learning: keras, transformers, huggingface, accelerate
• Computer Vision: opencv, pillow, skimage, albumentations
• NLP: spacy, nltk, gensim, textblob
• Jupyter Stack: jupyterlab, notebook, ipywidgets, voila
• Data Processing: dask, polars, pyarrow, h5py, zarr

Web Development Frameworks

• FastAPI + uvicorn + gunicorn
• Django + Django REST Framework
• Flask + extensions ecosystem
• Streamlit for data apps
• Dash for analytics dashboards
• Celery for distributed task processing
• SQLAlchemy + Alembic for database work

Cloud & Infrastructure Tools

• AWS CLI + boto3 + CDK
• Azure CLI + azure-sdk-for-python
• Google Cloud SDK + client libraries
• Docker & Docker Compose
• Kubernetes (kubectl, helm, tilt)
• Terraform + Pulumi

Development & DevOps Tools

• Version Control: git + pre-commit hooks
• CI/CD: GitHub Actions tools, GitLab CI tools
• Monitoring: prometheus-client, grafana tools
• Logging: loguru, structlog, rich
• API Tools: httpx, requests, aiohttp, pydantic
• Database Tools: psycopg2, pymongo, redis-py, sqlite3

Modern CLI Experience

• Zsh with Oh My Zsh + plugins (autosuggestions, syntax highlighting)
• Starship prompt for beautiful shell
• Modern alternatives: eza, bat, ripgrep, fd, fzf, zoxide
• Python-specific tools: pipx, pyenv, poetry, ruff, uv
• 50+ useful aliases pre-configured

Additional Language Runtimes

• Node.js 20.x + npm (for web frontend, Jupyter extensions)
• Go (for performance tools)
• Rust (for performance tools, ruff, etc.)
• Java 21 (for big data tools like Spark)

VS Code Integration

• 20+ pre-installed extensions including Python, Jupyter, Copilot, Docker
• Optimized settings for Python development with intelligent code completion
• Port forwarding for all common development servers
• Integrated debugging for Python, Django, FastAPI, etc.

🚀 Getting Started

Option 1: VS Code (Recommended)

1. Open this folder in VS Code
2. When prompted, click "Reopen in Container"
3. ☕ Grab coffee (first build takes 15-20 minutes - it's a MONSTER!)
4. 🎉 Start coding in your Python monster environment!

Option 2: Manual Docker Compose

# Run the helper script to get your UID/GID
./start-monster.sh

# Or manually with docker-compose
export UID=$(id -u) GID=$(id -g) USER=$(whoami)
docker-compose -f .devcontainer/docker-compose.yml up -d --build

📦 What Gets Installed

Python Versions & Package Managers

Python 3.12 (default)      # Latest Python
Python 3.11                # Stable version
Python 3.10                # LTS support
pyenv                      # Python version management
pip                        # Standard package manager
poetry                     # Modern dependency management
pipenv                     # Virtual environments
conda/mamba                # Data science ecosystem
pipx                       # Install Python applications
uv                         # Ultra-fast Python package installer

Data Science & ML Stack

# Core Data Science
numpy pandas matplotlib seaborn plotly
jupyterlab notebook ipywidgets voila
scipy statsmodels sympy

# Machine Learning
scikit-learn xgboost lightgbm catboost
tensorflow pytorch keras
transformers accelerate datasets
huggingface-hub wandb mlflow

# Computer Vision & NLP
opencv-python pillow scikit-image
spacy nltk gensim textblob
albumentations torchvision

# Big Data & Performance
dask polars pyarrow
numba cupy (if GPU available)
spark (pyspark)

Web Development Stack

# Web Frameworks
fastapi uvicorn gunicorn
django djangorestframework
flask flask-restful flask-sqlalchemy
streamlit dash gradio

# Database & ORM
sqlalchemy alembic
psycopg2 pymongo redis
sqlite3 clickhouse-driver

# API & HTTP
httpx requests aiohttp
pydantic marshmallow
celery dramatiq

Development Tools

# Code Quality
black isort ruff
flake8 pylint mypy
bandit safety
pre-commit

# Testing
pytest unittest
tox nox
hypothesis factory-boy
coverage pytest-cov

# Debugging & Profiling
ipdb pdbpp
memory-profiler py-spy
line-profiler

Cloud & DevOps Tools

# Cloud SDKs
aws-cli boto3 aws-cdk-lib
azure-cli azure-sdk-for-python
google-cloud-sdk

# Container & Orchestration
docker docker-compose
kubectl helm kubernetes
terraform pulumi

# Monitoring & Logging
prometheus-client grafana
loguru structlog rich
opentelemetry-api

Modern CLI Tools

eza          # Better ls
bat          # Better cat with syntax highlighting  
ripgrep      # Faster grep
fd           # Better find
fzf          # Fuzzy finder
zoxide       # Smarter cd
starship     # Beautiful prompt
hyperfine    # Benchmarking
tokei        # Code statistics
just         # Command runner
gh           # GitHub CLI

🎯 Perfect for

• Data Science & Machine Learning projects
• Web Development (FastAPI, Django, Flask)
• Scientific Computing & research
• Automation & scripting
• ETL/Data Engineering pipelines
• API Development & microservices
• Cloud-native applications
• Jupyter-based data analysis
• MLOps & model deployment

⚡ Pre-configured Aliases

# Python shortcuts
py / py3 / python            # python3
pip / pip3                   # python -m pip
jupyter / lab                # jupyterlab
notebook / nb               # jupyter notebook

# Package management
poetry-install / pi         # poetry install
poetry-add / pa             # poetry add
poetry-shell / ps           # poetry shell

# Code quality
black-check / bc            # black --check
isort-check / ic            # isort --check
mypy-check / mc             # mypy .
flake8-check / fc           # flake8 .
test / t                    # pytest
coverage / cov              # pytest --cov

# Docker & Kubernetes  
d / dc / k                  # docker/docker-compose/kubectl
dps / kgp / kgs            # docker ps / kubectl get pods/services

# Modern CLI
ll / la / ls               # eza variants (better ls)
cat                        # bat (syntax highlighted)
find / grep                # fd / ripgrep (faster)
cd                         # zoxide (smarter)

🌐 Port Forwarding

Auto-forwarded ports: 3000, 5000, 8000, 8080, 8888, 8501, 9000

📁 Workspace Structure

~/workspace/
├── notebooks/       # Jupyter notebooks
├── src/            # Source code
├── data/           # Data files
├── models/         # ML models
├── tests/          # Test files
├── docs/           # Documentation
├── scripts/        # Utility scripts
├── docker/         # Docker files
├── kubernetes/     # K8s manifests
├── terraform/      # Infrastructure code
├── requirements/   # Requirements files
├── .env.example    # Environment template
└── pyproject.toml  # Project configuration

🔧 Container Specs

• Base: Ubuntu 24.04 LTS
• Size: ~12GB (it's a MONSTER!)
• Build time: 15-20 minutes first time
• Subsequent starts: <45 seconds
• User mapping: Perfect UID/GID match with host
• Python versions: 3.12 (default), 3.11, 3.10
• GPU support: CUDA-ready (if host GPU available)

⚠️ System Requirements

• Docker Desktop with WSL 2 backend
• 12GB+ RAM allocated to Docker (recommended 16GB)
• 30GB+ disk space for the image
• VS Code with Remote-Containers extension
• Optional: NVIDIA GPU + Docker GPU support for ML workloads

🎉 Why This Python Monster?

1. Everything Included - Stop installing packages, start coding
2. Perfect Permission Mapping - No more chown headaches in WSL
3. Multiple Python Versions - Test across versions with pyenv
4. Data Science Ready - All major ML/DS libraries pre-installed
5. Web Development Complete - FastAPI, Django, Flask, all ready
6. Cloud Native - AWS, Azure, GCP tools included
7. Modern Tooling - Latest formatters, linters, and CLI tools
8. Jupyter Integration - Full JupyterLab setup with extensions
9. WSL Optimized - Built specifically for WSL workflows
10. Team Consistency - Same environment for everyone

This is the Python container you use when you want EVERYTHING for Python development and don't want to think about setup ever again! 🐍🚀