HelloPython-AI-ML

Domain: AI/ML model development, data mining, and reproducible machine learning pipelines. Focused on MCP (Modal Context Protocol), A2A (Agent-to-Agent) workflows, and large language model acceleration with vLLM.

Project Overview: This repository contains Python and PyTorch example projects for AI/ML, demonstrating best practices for data preprocessing, model training, evaluation, and deployment. Designed to be clear, reproducible, and modular, supporting both local execution and cloud-based environments (e.g., Google Colab). MCP is applied for context-aware modeling and advanced AI workflows, enabling models to leverage modal context efficiently in both training and inference.

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Project Structure

HelloPython-AI-ML/
├── examples/                   # Example projects
│   ├── hello_world/            # Basic "Hello, World!" ML example
│   │   ├── main.py
│   │   └── README.md
│   ├── mcp_pipeline/           # Modal Context Protocol implementation example
│   │   ├── train.py
│   │   ├── inference.py
│   │   └── README.md
│   ├── a2a_agent/              # Agent-to-Agent workflow example
│   │   ├── agent.py
│   │   └── README.md
│   ├── vllm_example/           # Large language model acceleration
│   │   ├── llm_inference.py
│   │   └── README.md
│   └── ...                     # Additional examples
├── data/                       # Datasets for experiments
├── notebooks/                  # Jupyter / Colab notebooks
├── tools/                      # Utility scripts, preprocessing helpers
├── README.md                   # General repo overview
└── requirements.txt            # Python dependencies

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Build & Run Instructions

Prerequisites

• Python 3.10+: https://www.python.org/downloads/
• PyTorch: https://pytorch.org/get-started/locally/
• vLLM: https://vllm.ai/
• Jupyter/Colab (optional, for notebooks)
• pip / virtualenv: For managing dependencies

Setup

Create a virtual environment and install dependencies:

python -m venv venv
source venv/bin/activate      # Linux/macOS
venv\Scripts\activate         # Windows
pip install -r requirements.txt

Run Examples

Navigate to the example project:

cd examples/mcp_pipeline
python train.py
python inference.py

Or run notebooks directly in Google Colab:

# Upload notebooks/ to Colab or open directly via Google Drive

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Toolchain & Documentation

All relevant references for Python AI/ML development:

• Python Documentation: https://docs.python.org/3/
• PyTorch Documentation: https://pytorch.org/docs/stable/index.html
• PyTorch Tutorials: https://pytorch.org/tutorials/
• PyTorch Resources: https://pytorch.org/resources/
• vLLM Docs: https://vllm.ai/
• Data Mining Principles & Best Practices: https://www.kdnuggets.com/
• MCP (Modal Context Protocol) Concepts: internal design references for context-aware modeling and advanced AI workflows
• A2A (Agent-to-Agent) Concepts: design references for agent-based communication and autonomous workflow integration

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Contribution Guidelines

• Follow enterprise-grade coding principles (clarity, testability, maintainability).
• Keep examples modular, reproducible, and documented.
• Ensure consistent project structure and naming conventions.
• Add detailed comments for clarity and reproducibility.

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Example Usage

cd examples/vllm_example
python llm_inference.py

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