This project demonstrates a distributed machine learning system that leverages model and data parallelism across multiple machines. It includes two primary implementations: a CNN-based inference system using model parallelism and a Linear Regression system using data parallelism with Dask.

The architecture is designed to run over a local area network. A master node coordinates tasks, distributes models or data, and aggregates results from multiple worker nodes. Communication is handled using UDP for worker registration and TCP for data exchange.

• Distributed CNN model using model parallelism
• Linear Regression training using Dask and data parallelism
• REST API built with Flask for initiating training and making predictions
• Lightweight and runs on low-spec machines over LAN

• Python 3.10
• PyTorch
• Dask
• Flask
• Scikit-learn
• NumPy, Pandas
• TCP/UDP socket communication
• Pickle for serialization

• master.py – Controls model/data distribution, API server
• worker.py – Handles assigned training or inference tasks
• flask_server.py – Hosts Flask routes for /train and /predict
• utils/ – Utility functions for serialization and configuration
• model.pkl – Sample CNN model (PyTorch)
• dataset.csv – Input data for regression
• requirements.txt – List of dependencies
• bankend_81.py - Linear Regression Distributed Model

• Use /train API endpoint to start distributed training.
• Use /predict API endpoint to send input data and get predictions.

@chahelgupta, @AmishiDesai04, @vpratham