# Compute Agents and Resource Management

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# Compute Agents and Resource Management

**Efficient Distributed Compute for AI Workloads**

Compute Agents are Koii’s specialized agents for managing heavy computational resources. They handle large-scale processing, model training, simulations, batch jobs, and intelligent resource allocation across local hardware, edge nodes, and the global infrastructure.

### What Are Compute Agents?

While Task Agents focus on goal-oriented execution, **Compute Agents** focus on **performance, efficiency, and resource optimization**. They act as intelligent schedulers and workload managers that ensure your AI computations run at maximum speed and minimum cost.

**Key Responsibilities:**
- Dynamic allocation of CPU, GPU, RAM, and storage
- Workload distribution across nodes
- Performance monitoring and auto-optimization
- Energy-efficient scheduling
- Fault tolerance and automatic recovery

### Core Features of Compute Agents

- **GPU/TPU Acceleration** — Automatic detection and utilization of available accelerators.
- **Distributed Training** — Split large model training across multiple nodes seamlessly.
- **Predictive Scaling** — Anticipates resource needs and scales proactively.
- **Cost Optimization** — Intelligently chooses the cheapest available resources while meeting performance SLAs.
- **Resource Quotas & Priorities** — Fine-grained control for teams and projects.

### How to Use Compute Agents

#### 1. Create a Compute Agent
```bash
koii agent create \
  --name "training-cluster" \
  --type compute \
  --goal "Train a 7B parameter model on the latest dataset with distributed training"
````

**2. Resource Configuration (compute-agent.yaml)**

YAML

```
agent:
  name: training-cluster
  type: compute
  resources:
    min_cpu: 16
    min_ram: 64GB
    min_gpu: 4
    gpu_type: NVIDIA_A100
  scaling:
    auto: true
    max_nodes: 32
    strategy: predictive
  priority: high
  timeout: 48h
```

**3. Run and Monitor**

Bash

```
# Start with live dashboard
koii agent run training-cluster --monitor

# View resource usage
koii resource status

# Scale manually if needed
koii resource scale training-cluster --nodes 16
```

#### Resource Management Dashboard Commands

Bash

```
# Global overview
koii resource list

# Detailed node status
koii resource nodes

# Release idle resources
koii resource optimize --aggressive

# Reserve resources for a project
koii resource reserve --project ai-research --gpu 8 --duration 24h
```

#### Resource Management Strategies

| Strategy                | Use Case                | Benefit                |
| ----------------------- | ----------------------- | ---------------------- |
| **On-Demand**           | Short experiments       | Fast startup           |
| **Predictive**          | Scheduled training jobs | Better utilization     |
| **Spot / Low-Priority** | Non-urgent batch jobs   | Cost savings up to 70% |
| **Reserved**            | Production workloads    | Guaranteed performance |
| **Edge-First**          | Real-time inference     | Low latency            |

#### Best Practices

1. Always specify resource requirements clearly.
2. Combine Compute Agents with Orchestration Agents for complex pipelines.
3. Monitor usage regularly with koii resource dashboard.
4. Use memory-efficient data loading for large datasets.
5. Enable auto-optimization for production environments.

**Koii Core (Free)** supports basic Compute Agents on a single node with up to 4 GPUs. **Koii Enterprise** unlocks full global resource federation, unlimited nodes, and advanced cost controls.


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