Make cutting-edge machine learning accessible to organizations by efficiently utilizing heterogeneous compute resources.
Develop a self-managing system that automatically handles resource discovery, workload distribution, fault tolerance, and scaling with minimal configuration requirements and administrative overhead.
Provide a reliable, high-performance inference backbone to support real-world ML applications with the scale, latency, and reliability requirements of production systems.
Build a secure, maintainable, and observable system that meets enterprise requirements for encryption, resilience, repeatable deployment, and comprehensive logging.
Want to help improve Hypha and its capabilities for distributed training and inference? We encourage contributions of all kinds, from bug fixes and feature enhancements to documentation improvements. Hypha aims to provide a robust platform for efficient and scalable machine learning workflows, and your contributions can help make it even better. Consult CONTRIBUTING.md for detailed instructions on how to contribute effectively.