Scalable AI Optimization with X-ScaleAI and DeepHyper Integration

The development of complex Deep Learning (DL) architectures, such as Large Language Models (LLMs) and advanced Computer Vision (CV) models, demands significant computational resources for pre-training, fine-tuning, and deployment. As these models grow in complexity, efficiently managing workloads across various infrastructures has become increasingly challenging. While distributed frameworks like X-ScaleAI excel at handling training and deployment, they lack integrated Hyper Parameter Optimization (HPO) capabilities, which are critical for achieving optimal model performance. The expanded search space in advanced models significantly increases the computational demands of hyperparameter tuning. Although tools like DeepHyper automate this tuning process, their lack of seamless integration with training frameworks complicates their application to diverse workloads, making optimization a cumbersome task. X-ScaleSolutions proposes integrating DeepHyper, a state-of-the-art distributed hyperparameter optimization tool (developed under the DOE ASCR program), into the X-ScaleAI framework, resulting in a unified platform called X-ScaleAI-Hyper. DeepHyper supports established optimization techniques such as Grid Search, Random Search, Bayesian Optimization, and Aging Evolution, while X-ScaleAI enables efficient distributed DL training and inference covering many parallelism schemes for a wide range of models defined in PyTorch or HuggingFace. This integration will equip X-ScaleAI-Hyper with advanced hyperparameter optimization capabilities, empowering users with a streamlined workflow for tuning and training models on diverse infrastructures. The specific tasks for this effort include: (1) Integration of DeepHyper’s Exhaustive Search Methods; (2) Integration of Iterative Search Methods; (3) Developing a Unified X-ScaleAI-Hyper API; (4) Containerization and Smart Allocation of On-premise and Cloud Resources; (5) Codesigning a Two-Tier Scalability Scheme for HPO and Distributed DL; (6) Integration of Distributed Decentralized Bayesian Optimization; (7) Co-designing Multi-Level Checkpointing with SCR-Exa; (8) Co-designing an Automated HPO Selection Strategy; (9) Implementing µTransfer for Large-Scale LLMs; and (10) Extensive Testing and Benchmarking of X-ScaleAI-Hyper. Items 1, 2 and parts of items 3 and 10 will be carried out in Phase I of this project. The remaining items will be completed in Phase II. X-ScaleAI-Hyper will deliver an end-to-end solution that supports the efficient training, fine-tuning, and hyperparameter optimization of largescale DL models. This is particularly valuable to organizations deploying AI workloads at scale, such as enterprises in healthcare, finance, agriculture, and other industries. The cloud compatibility makes it suitable for organizations of all sizes, fostering greater accessibility to cutting-edge AI technologies. The public benefit lies in democratizing AI capabilities, improving productivity, and expanding the reach of AI-driven innovation across various industries.