GPU Optimizer for ML Models

Welcome to the GPU Optimizer for ML Models! This project aims to optimize GPU performance for machine learning models, leveraging advanced techniques and a wide array of technologies.

Table of Contents

• Introduction
• Features
• Project Structure
• Installation
• Usage

Introduction

The GPU Optimizer for ML Models is a comprehensive platform designed to improve GPU performance for training and deploying machine learning models. This platform features a web-based interface for managing and monitoring models, an API for integration, and robust backend services for data processing and model optimization.

Features

• GPU Performance Optimization: Improve GPU utilization for training ML models using advanced scheduling and resource management.
• Model Management: Upload, manage, and monitor ML models through a web-based interface.
• Data Processing: Use Spark, Hadoop, and other big data technologies for data transformation and analysis.
• Real-time Monitoring: Monitor GPU utilization and performance in real-time.
• Secure API: Securely manage models and GPU resources via a robust API.

Project Structure

The GPU Optimizer for ML Models project is organized into several directories and files, each serving a specific purpose. Below is a detailed breakdown of the project structure:

MLGpuOptimizer/
├── backend_api/
│   ├── config/
│   │   └── config.py
│   ├── controllers/
│   │   ├── model_controller.py
│   │   └── gpu_controller.py
│   ├── models/
│   │   └── model.py
│   ├── routes/
│   │   └── api_routes.py
│   ├── services/
│   │   ├── gpu_service.py
│   │   └── model_service.py
│   ├── utils/
│   │   └── optimization.py
│   ├── app.py
│   └── Dockerfile
├── data_processing/
│   ├── spark_jobs/
│   │   ├── data_transformation.py
│   │   ├── data_aggregation.py
│   │   └── data_analysis.py
│   ├── hadoop_jobs/
│   │   └── hadoop_config.py
│   ├── utils/
│   │   └── spark_utils.py
│   └── Dockerfile
├── web_interface/
│   ├── public/
│   │   └── index.html
│   ├── src/
│   │   ├── components/
│   │   │   ├── Header.js
│   │   │   ├── Footer.js
│   │   │   ├── ModelUpload.js
│   │   │   ├── ModelMonitor.js
│   │   │   └── GpuStats.js
│   │   ├── pages/
│   │   │   ├── HomePage.js
│   │   │   ├── UploadPage.js
│   │   │   └── MonitorPage.js
│   │   ├── services/
│   │   │   └── api.js
│   │   ├── App.js
│   │   ├── index.js
│   │   └── App.css
│   └── Dockerfile
├── db_init/
│   └── init.sql
├── scripts/
│   └── deploy.sh
├── README.md

Installation

Prerequisites

• Docker
• Node.js and npm
• Python and pip
• MySQL

Steps

1. Clone the Repository:

   git clone https://github.com/yourusername/MLGpuOptimizer.git
   cd MLGpuOptimizer

2. Build and Run Backend: cd backend_api docker build -t gpu_optimizer_backend . docker run -d -p 5000:5000 --name gpu_optimizer_backend gpu_optimizer_backend
3. Build and Run Web Interface: cd ../web_interface docker build -t gpu_optimizer_frontend . docker run -d -p 3000:3000 --name gpu_optimizer_frontend gpu_optimizer_frontend
4. Inititalize Database: docker exec -i mysql_container mysql -u root -p < db_init/init.sql

Usage

This guide provides instructions on how to use the GPU Optimizer for ML Models.

Main Dashboard

1. Access the Web Interface:

   • Open your browser and navigate to http://localhost:3000.

2. Navigate the Dashboard:

   • Use the navigation menu to access different sections of the application.

Uploading and Monitoring Models

1. Upload Model:

   • Navigate to the "Upload Model" section.
   • Click the "Choose File" button and select the model file to upload.
   • Click the "Upload" button to upload the model.

2. Monitor Models:

   • Navigate to the "Monitor Models" section.
   • View the list of uploaded models and their status.
   • Each model entry shows the model name and its current status (e.g., uploaded, optimized).

Viewing GPU Stats

1. GPU Stats:
   • Navigate to the "GPU Stats" section.
   • View real-time GPU utilization and memory statistics.
   • The stats include GPU utilization percentage, total memory, free memory, and used memory.

Example Usage Scenarios

Scenario 1: Optimizing a New Model

1. Upload Your Model:

   • Go to the "Upload Model" section.
   • Select your model file (e.g., a PyTorch model file) and upload it.
   • Wait for the upload to complete and check the model status in the "Monitor Models" section.

2. Monitor Optimization:

   • Once the model is uploaded, the system automatically starts optimizing the model.
   • You can monitor the optimization process in the "Monitor Models" section.
   • The status will change from "uploaded" to "optimized" once the optimization is complete.

Scenario 2: Checking GPU Performance

1. Access GPU Stats:
   • Go to the "GPU Stats" section.
   • View real-time statistics of GPU performance, including utilization and memory usage.
   • Use this information to ensure that your GPUs are being utilized efficiently and identify any potential bottlenecks.

For more detailed instructions and troubleshooting, refer to the FAQ section below.

FAQ

How do I reset the database?

• To reset the database, you can re-run the database initialization script:

  docker exec -i mysql_container mysql -u root -p<password> < db_init/init.sql

What types of model files are supported?

• Currently, the platform supports PyTorch model files. Support for other model types can be added by extending the backend services.

How can I contribute to this project?

• Contributions are welcome!

If you have any further questions or need assistance, feel free to reach out to the project maintainers.

Happy coding!

✅ Verified Quickstart (Local, no Docker)

These steps were verified in a clean environment without Docker by running the backend locally and using SQLite for persistence.

python -m pip install -r backend_api/requirements.txt
./scripts/run_backend.sh

Then visit:

• API base: http://localhost:5000/api
• GPU stats: http://localhost:5000/api/gpu/stats

To run the React web interface locally (requires Node.js + npm with registry access):

cd web_interface
npm install
npm start

Smoke Test

Run a basic API verification:

./scripts/smoke_test.sh

✅ Verified Offline Demo (No External Dependencies)

If you need a fully runnable, dependency-free demo (no Docker, no pip, no npm), use the built-in demo server:

./scripts/run_demo.sh

Then open http://localhost:8080 to access the demo dashboard with upload, monitor, and GPU stats panels.

To verify the demo server automatically:

./scripts/smoke_test_demo.sh

The demo server uses the same API paths (/api/model/upload, /api/model/monitor, /api/gpu/stats) and stores data in backend_api/demo.db.

Troubleshooting

• Docker not available: If docker --version fails, use the Verified Quickstart above to run locally.
• No GPU / nvidia-smi missing: The /api/gpu/stats endpoint returns a zeroed placeholder with a note.
• Torch/TensorRT not installed: Model optimization falls back to a safe copy and still marks the model as optimized.
• Database connection errors: By default, the backend uses SQLite at backend_api/gpu_optimizer.db. You can override with DATABASE_URL or SQLALCHEMY_DATABASE_URI.
• No dependency environment: Use ./scripts/run_demo.sh to run a full demo without Docker, pip, or npm.