{"id":27531053,"url":"https://github.com/amishidesai04/distributed-machine-learning","last_synced_at":"2026-04-30T09:34:43.133Z","repository":{"id":287947300,"uuid":"966329119","full_name":"AmishiDesai04/Distributed-Machine-Learning","owner":"AmishiDesai04","description":"A lightweight, scalable system that demonstrates model and data parallelism in machine learning using Dask, PyTorch, and Flask. 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It includes two primary implementations: a CNN-based inference system using model parallelism and a Linear Regression system using data parallelism with Dask.\n\n## Overview\n\nThe 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.\n\n## Key Features\n\n- Distributed CNN model using model parallelism\n- Linear Regression training using Dask and data parallelism\n- REST API built with Flask for initiating training and making predictions\n- Lightweight and runs on low-spec machines over LAN\n\n## Technologies Used\n\n- Python 3.10\n- PyTorch\n- Dask\n- Flask\n- Scikit-learn\n- NumPy, Pandas\n- TCP/UDP socket communication\n- Pickle for serialization\n\n\n## Project Structure\n\n- `master.py` – Controls model/data distribution, API server\n- `worker.py` – Handles assigned training or inference tasks\n- `flask_server.py` – Hosts Flask routes for `/train` and `/predict`\n- `utils/` – Utility functions for serialization and configuration\n- `model.pkl` – Sample CNN model (PyTorch)\n- `dataset.csv` – Input data for regression\n- `requirements.txt` – List of dependencies\n- `bankend_81.py` - Linear Regression Distributed Model\n\n## Usage\n\n- Use `/train` API endpoint to start distributed training.\n- Use `/predict` API endpoint to send input data and get predictions.\n\n## Contributors\n\n[@AmishiDesai04](https://github.com/AmishiDesai04), [@chahelgupta](https://github.com/chahelgupta), [@vpratham](https://github.com/vpratham)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famishidesai04%2Fdistributed-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famishidesai04%2Fdistributed-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famishidesai04%2Fdistributed-machine-learning/lists"}