Decentralized Machine Learning
Objective:
The primary goal is to enable large-scale machine learning model training across decentralized edge infrastructure using bare-metal orchestration on NuNet. Based on findings regarding performance inefficiencies in containerized and virtualized environments (such as Docker and Kubernetes), this project will focus on leveraging Hivemind for distributed training without virtualization layers, optimizing for performance and resource usage. However, Docker will be selectively used only for single-instance compute requests to ensure lightweight containerization where distributed orchestration is not necessary. Additionally, Gradio and JupyterLab will be integrated to provide decentralized user interfaces and computational notebooks for seamless interaction with models and workflows.
Key Points:
- Bare-metal orchestration for distributed machine learning tasks, avoiding Docker and Kubernetes for multi-node training to prevent performance degradation and enhance resource utilization.
- Integration of Hivemind for decentralized orchestration of machine learning tasks, using its native LibP2P framework for peer-to-peer communication and distributed training across NuNet nodes.
- Docker will only be utilized for single-instance compute requests, where isolated, lightweight containerization is needed but full distribution across nodes is not required, ensuring efficient handling of simpler tasks.
- Gradio will provide interactive, decentralized user interfaces for machine learning models, enabling user access without centralizing infrastructure.
- JupyterLab will offer decentralized computational notebook access, allowing users to perform data analysis, model training, and machine learning experiments on NuNet’s distributed resources.
- Leverage NuNet's decentralized infrastructure for dynamic resource allocation and task distribution based on network load and available capacity, minimizing overhead by limiting container use to single-instance tasks.
- Address issues related to network-bound applications (such as TensorFlow) by reducing network overhead through bare-metal orchestration, ensuring performance is maintained even in complex and data-heavy environments.
Deliverables:
- A research document detailing the integration of Hivemind with NuNet’s infrastructure for bare-metal orchestration, and how Gradio and JupyterLab are deployed for decentralized ML interfaces and computation, including performance benchmarks and comparisons to containerized environments.
- A working prototype demonstrating bare-metal orchestration of machine learning tasks using Hivemind, with decentralized access via Gradio for model interaction and JupyterLab for notebook-based workflows, alongside Docker deployment for single-instance compute requests.
- Code and documentation for orchestrating decentralized machine learning tasks and providing access via Gradio and JupyterLab, with a focus on resource efficiency, user interaction, and minimal network overhead.
- Clear guidelines on when to use Docker for single-instance tasks versus using bare-metal orchestration for distributed tasks across multiple nodes.
Timeline:
- Research and Planning Phase: Every 3 days (Monday to Wednesday) with progress updates.
- Development and Integration Phase: Timeline to be determined based on research outcomes and system setup.
Additional Notes:
- Regular updates and comprehensive documentation are crucial for the success of the project.
- Collaboration with team members, community stakeholders, and decentralized infrastructure experts to ensure smooth integration and optimization of Gradio, JupyterLab, Hivemind, and selective Docker usage within NuNet.
- The project scope and direction should be updated regularly based on findings, performance evaluations, and community feedback during development.
References: