Roadmap

Phase 1: Optimizing GPU Utilization for Inference

• Develop an automatic scheduling system to optimize idle GPU utilization across multiple Bittensor subnets.

• Design and implement a containerization strategy for AI models to enable deployment on individual GPU instances.

• Set up a container hub (e.g., Docker registry) for efficient distribution and deployment of AI models.

• Integrate the automatic scheduling system with the container hub to streamline model deployment and execution.

• Test and optimize the system to ensure efficient utilization of GPUs for various model inference tasks.

Phase 2: Implementing GPU Virtualization

• Research and evaluate Nvidia's vGPU technology and Kubernetes device plugins for GPU virtualization.

• Develop a proof-of-concept implementation of GPU virtualization using the selected technologies.

• Integrate GPU virtualization with the existing scheduling system to enable multiple tasks to run on a single GPU's vGPUs.

• Optimize the system to ensure stable and efficient execution of simultaneous tasks on a single GPU.

• Conduct thorough testing and performance benchmarking to validate the benefits of GPU virtualization.

Phase 3: Enabling Parallel Computing for Complex Tasks

• Analyze the requirements and design considerations for supporting complex tasks, including AI model training.

• Research and evaluate various partitioning strategies (e.g., by data, model, or layer) for parallel computing.

• Develop a framework for combining computing power from multiple sources to facilitate complex computing tasks.

• Implement the selected partitioning strategies and integrate them with the parallel computing framework.

• Extend the scheduling system to support the allocation of resources for complex tasks across multiple GPUs and nodes.

• Conduct extensive testing and optimization to ensure the stability, scalability, and performance of the parallel computing system.

Ongoing Enhancements and Maintenance

• Continuously monitor and analyze system performance to identify areas for improvement.

• Regularly update and optimize the scheduling algorithms to adapt to changing workloads and hardware advancements.

• Engage with the Bittensor community to gather feedback and incorporate new features and enhancements based on user requirements.

• Maintain and update the container hub to ensure compatibility with the latest AI models and frameworks.

• Collaborate with hardware vendors (e.g., Nvidia) to stay up-to-date with the latest GPU virtualization technologies and best practices.