File size: 4,269 Bytes
29db1dc f2bab5e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
---
language: en
license: mit
library_name: openpeerllm
tags:
- distributed-training
- cloud-computing
- language-model
- grid-computing
- openpeerllm
datasets:
- OpenPeerAI/OpenPeerLLM
pipeline_tag: distributed-training
mask: sequential
# Model Card: Cloud Agents for OpenPeerLLM
## Model Details
- **Model Type:** Distributed Training System for Language Models
- **Primary Purpose:** Training Large Language Models in a distributed environment
- **Framework:** PyTorch with Ray
- **Target Model:** [OpenPeerLLM](https://huggingface.co/OpenPeerAI/OpenPeerLLM)
- **License:** MIT
## Intended Use
### Primary Use
- Distributed training of large language models
- Grid computing/distributed computing-based learning for tensors
- Horizontal scaling of model training infrastructure
### Out-of-Scope Uses
- Production deployment of models
- Single-machine training
- Real-time inference
## System Architecture
### Components
1. **Distributed Agents**
- Lightweight worker nodes for distributed computing
- Automatic scaling based on workload
- Built-in fault tolerance and recovery
2. **CouchDB Coordination Layer**
- Job distribution and management
- State synchronization
- Agent discovery and registration
3. **Tensor Operations**
- Distributed gradient computation
- Efficient parameter updates
- Gradient averaging and clipping
4. **Training Orchestration**
- Automated model checkpoint management
- Dynamic load balancing
- Progress monitoring and reporting
## Performance
### Scaling Characteristics
- **Minimum Agents:** 2
- **Maximum Agents:** 10 (configurable)
- **Scale-up Threshold:** 80% utilization
- **Scale-down Threshold:** 30% utilization
- **Auto-scaling:** Yes, based on workload
### Resource Requirements
- **Per Agent:**
- CPU: 1 core minimum
- GPU: Optional, supports fractional GPU allocation
- Memory: Varies based on model size
- Network: Reliable connection to CouchDB and other agents
## Limitations
1. **Network Dependency**
- Requires stable network connectivity between agents
- CouchDB must be accessible to all agents
2. **Scaling Limits**
- Upper bound on number of concurrent agents
- Network latency can impact synchronization
3. **Resource Management**
- Requires careful monitoring of resource utilization
- GPU memory management crucial for large models
## Training Details
### Training Data
- Uses the same training data as OpenPeerLLM
- Supports distributed batch processing
- Configurable gradient accumulation steps
### Training Procedure
1. **Initialization**
- Model weights loaded from HuggingFace hub
- Agents register with coordinator
- Initial state distributed to all agents
2. **Training Loop**
- Distributed gradient computation
- Synchronized parameter updates
- Regular checkpointing
- Automatic agent scaling
### Hyperparameters
Configurable through environment variables:
- Batch size
- Gradient accumulation steps
- Number of epochs
- Learning rate
- Scaling thresholds
## Getting Started
1. **Installation**
```bash
pip install -r requirements.txt
```
2. **Configuration**
- Copy `.env.example` to `.env`
- Configure CouchDB connection
- Set desired training parameters
3. **Launch Training**
```bash
python -m cloud_agents.cli train --num-epochs 3 --steps-per-epoch 100
```
4. **Monitor Progress**
```bash
python -m cloud_agents.cli status
```
## Ethical Considerations
- Resource efficiency through intelligent scaling
- Environmental impact minimization via workload-based scaling
- Distributed approach reduces single-point-of-failure risks
## Maintenance
This system is maintained as an open-source project. Users are encouraged to:
- Report issues and bugs
- Suggest improvements
- Contribute to the codebase
- Share performance metrics and optimization strategies
## Citation
If you use this system in your research, please cite:
```bibtex
@software{cloud_agents_2025,
title = {Cloud Agents: Distributed Training System for OpenPeerLLM},
year = {2025},
author = {Andrew Magdy Kamal},
url = {hhttps://huggingface.co/OpenPeerAI/Cloud-Agents},
note = {Distributed computing framework for training large language models}
}
``` |