| license: mit | |
| datasets: | |
| - fka/awesome-chatgpt-prompts | |
| language: | |
| - en | |
| library_name: cloud-agents | |
| metrics: | |
| - accuracy | |
| - code_eval | |
| base_model: | |
| - OpenPeerAI/OpenPeerLLM | |
| tags: | |
| - agent | |
| - cloud | |
| - computing | |
| - distributed | |
| - distributed-learning | |
| - decentralized | |
| - grid | |
| - grid-computing | |
| - machine-learning | |
| - ml | |
| # Cloud Agents for Distributed Model Training | |
| A lightweight and horizontally scalable distributed computing system for training large language models, specifically designed for OpenPeerLLM. | |
| ## Features | |
| - Distributed tensor operations for model training | |
| - CouchDB-based coordination layer | |
| - Automatic agent discovery and load balancing | |
| - Horizontal scaling capabilities | |
| - Fault tolerance and recovery | |
| - Integration with OpenPeerAI's OpenPeerLLM | |
| ## Installation | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ## Configuration | |
| 1. Set up CouchDB instance | |
| 2. Copy `.env.example` to `.env` and configure your settings | |
| 3. Start the coordinator node | |
| 4. Launch agent nodes | |
| ## Quick Start | |
| ```bash | |
| # Start coordinator | |
| python -m cloud_agents.coordinator | |
| # Start agent (on each machine) | |
| python -m cloud_agents.agent | |
| ``` | |
| ## Architecture | |
| - `coordinator`: Manages job distribution and agent coordination | |
| - `agent`: Handles tensor operations and model training | |
| - `couchdb_client`: Interface for CouchDB communication | |
| - `tensor_ops`: Distributed tensor operations | |
| - `utils`: Helper functions and utilities | |
| ## License | |
| MIT |