Inferno Inference 🔥

Introduction

Inferno Inference is a high-performance, distributed inference engine designed to efficiently handle multiple machine learning models hosted via APIs. This Proof of Concept (PoC) demonstrates the core functionality of orchestrating, managing, and executing API-based models in a scalable and secure manner. The goal is to provide a minimal, yet powerful foundation for building and extending inference capabilities, tailored to meet the growing demands of the machine learning community.

Core Features (PoC) 🛠️

• Model Orchestration via API: Load and manage multiple ML models hosted as APIs, with the ability to execute them sequentially.
• Distributed Execution: Utilize a lightweight message queue to distribute inference tasks across multiple nodes.
• Batch Processing: Support for batch API requests to maximize throughput.
• Concurrency: Handle concurrent API requests with Rust's async/await, optimizing for low latency.
• Performance Monitoring: Collect and display basic metrics for API calls, including latency and success rates.
• Security: Ensure secure API communication with HTTPS and basic access control mechanisms.

Future Roadmap 🛤️

Phase 1: Enhanced Scalability & Flexibility 🚀

• Advanced Orchestration: Parallel and conditional model execution workflows.
• Dynamic Model Loading: On-the-fly loading/unloading of models.
• Auto-scaling: Automatically adjust node count based on workload.

Phase 2: Performance Optimization ⚡

• GPU/TPU Support: Accelerate inference using hardware accelerators.
• Model Caching: Reduce redundant API calls with result caching.
• Optimized Communication: Lower latency through high-performance networking.

Phase 3: Advanced Monitoring & Management 📊

• Comprehensive Monitoring: Real-time dashboards and alerting.
• Model Performance Analytics: Detailed analysis of model performance metrics.
• Automated Model Tuning: Integrate automated hyperparameter tuning.

Phase 4: Security & Compliance 🛡️

• End-to-End Encryption: Secure all data and API communication.
• Compliance Tools: Add audit logging and GDPR compliance features.

How to Contribute 🤝

Contributions are welcome! Here’s how you can help:

1. Fork the Repository: Start by forking the repo to your GitHub account.
2. Clone Your Fork: Clone your fork locally.

   git clone https://github.com/your-username/inferno-inference.git

3. Create a Branch: Create a new branch for your feature or bug fix.

   git checkout -b feature/your-feature-name

4. Make Changes: Implement your changes in the relevant files.
5. Commit Your Changes: Commit your changes with a descriptive message.

   git commit -m "Add feature {your feature}"

6. Push Your Branch: Push your branch to GitHub.

   git push origin feature/your-feature-name

7. Open a Pull Request: Open a pull request from your branch to the main branch of this repository.

This is a long term project anticipating the need for orchestration of model calls.