Project Overview
Built from the ground up to address the specific needs of machine learning workloads in distributed environments, focusing on fault tolerance and efficient resource utilization.
Architecture
The framework uses a master-worker architecture with automatic load balancing and failure recovery mechanisms.
Performance Results
Benchmark tests showed significant improvements over existing solutions, particularly for iterative ML algorithms.
Future Plans
Planning to add support for GPU clusters and integration with popular ML frameworks like PyTorch and TensorFlow.