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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.