---
name: cloud-neural
description: Neural network training and deployment in Flow Nexus cloud. Use for distributed ML training, model inference, and neural network lifecycle management.
version: 1.0.0
category: cloud
type: skill
capabilities:
  - neural_network_training
  - distributed_training
  - model_inference
  - cluster_management
  - template_deployment
  - model_validation
tools:
  - mcp__flow-nexus__neural_train
  - mcp__flow-nexus__neural_predict
  - mcp__flow-nexus__neural_cluster_init
  - mcp__flow-nexus__neural_node_deploy
  - mcp__flow-nexus__neural_cluster_connect
  - mcp__flow-nexus__neural_train_distributed
  - mcp__flow-nexus__neural_cluster_status
  - mcp__flow-nexus__neural_predict_distributed
  - mcp__flow-nexus__neural_cluster_terminate
  - mcp__flow-nexus__neural_list_templates
  - mcp__flow-nexus__neural_deploy_template
  - mcp__flow-nexus__neural_training_status
  - mcp__flow-nexus__neural_list_models
  - mcp__flow-nexus__neural_validation_workflow
  - mcp__flow-nexus__neural_publish_template
  - mcp__flow-nexus__neural_rate_template
  - mcp__flow-nexus__neural_performance_benchmark
related_skills:
  - cloud-swarm
  - cloud-sandbox
  - cloud-workflow
---

# Cloud Neural Network

> Train, deploy, and manage neural networks at scale using Flow Nexus cloud-powered distributed computing.

## Quick Start

```javascript
// Train a basic neural network
mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "feedforward",
      layers: [
        { type: "dense", units: 128, activation: "relu" },
        { type: "dropout", rate: 0.2 },
        { type: "dense", units: 10, activation: "softmax" }
      ]
    },
    training: { epochs: 100, batch_size: 32, learning_rate: 0.001 }
  },
  tier: "small"
})

// Run inference
mcp__flow-nexus__neural_predict({
  model_id: "trained_model_id",
  input: [[0.5, 0.3, 0.2]]
})
```

## When to Use

- Training neural networks for classification, regression, or generation tasks
- Deploying distributed training across multiple cloud sandboxes
- Running model inference on trained models
- Managing model lifecycle from training to production deployment
- Implementing federated learning or ensemble methods
- Fine-tuning pre-trained models for specific domains

## Prerequisites

- Flow Nexus account with active session
- MCP server `flow-nexus` configured
- Sufficient rUv credits for training tier selected

## Core Concepts

### Neural Architectures

| Type | Use Case |
|------|----------|
| **Feedforward** | Classification, regression |
| **LSTM/RNN** | Time series, NLP sequences |
| **Transformer** | Advanced NLP, multimodal |
| **CNN** | Computer vision, image processing |
| **GAN** | Data generation, augmentation |
| **Autoencoder** | Dimensionality reduction, anomaly detection |

### Training Tiers

| Tier | Resources | Cost |
|------|-----------|------|
| `nano` | Minimal, quick tests | Low |
| `mini` | Small models | Low |
| `small` | Standard training | Medium |
| `medium` | Large models | High |
| `large` | Production scale | Highest |

### Distributed Consensus Protocols

- **proof-of-learning**: Training contribution verification
- **byzantine**: Fault-tolerant distributed consensus
- **raft**: Leader-based coordination
- **gossip**: Decentralized information propagation

## MCP Tools Reference

### Single-Node Training

```javascript
mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "feedforward",  // lstm, gan, autoencoder, transformer
      layers: [
        { type: "dense", units: 128, activation: "relu" },
        { type: "dropout", rate: 0.2 },
        { type: "dense", units: 10, activation: "softmax" }
      ]
    },
    training: {
      epochs: 100,
      batch_size: 32,
      learning_rate: 0.001,
      optimizer: "adam"
    },
    divergent: {
      enabled: false,
      pattern: "lateral",  // quantum, chaotic, associative, evolutionary
      factor: 0.1
    }
  },
  tier: "small",            // nano, mini, small, medium, large
  user_id: "user_id"
})
```

### Distributed Cluster Training

```javascript
// Initialize distributed cluster
mcp__flow-nexus__neural_cluster_init({
  name: "training-cluster",
  architecture: "transformer",  // transformer, cnn, rnn, gnn, hybrid
  topology: "mesh",             // mesh, ring, star, hierarchical
  consensus: "proof-of-learning",
  daaEnabled: true,
  wasmOptimization: true
})

// Deploy worker nodes
mcp__flow-nexus__neural_node_deploy({
  cluster_id: "cluster_id",
  node_type: "worker",    // worker, parameter_server, aggregator, validator
  model: "base",          // base, large, xl, custom
  capabilities: ["training", "inference"],
  autonomy: 0.8
})

// Connect nodes based on topology
mcp__flow-nexus__neural_cluster_connect({
  cluster_id: "cluster_id",
  topology: "mesh"
})

// Start distributed training
mcp__flow-nexus__neural_train_distributed({
  cluster_id: "cluster_id",
  dataset: "dataset_id",
  epochs: 10,
  batch_size: 32,
  learning_rate: 0.001,
  optimizer: "adam",
  federated: false
})

// Check cluster status
mcp__flow-nexus__neural_cluster_status({ cluster_id: "cluster_id" })

// Terminate when done
mcp__flow-nexus__neural_cluster_terminate({ cluster_id: "cluster_id" })
```

### Inference

```javascript
// Single-node inference
mcp__flow-nexus__neural_predict({
  model_id: "model_id",
  input: [[0.5, 0.3, 0.2]],
  user_id: "user_id"
})

// Distributed inference
mcp__flow-nexus__neural_predict_distributed({
  cluster_id: "cluster_id",
  input_data: "[0.5, 0.3, 0.2]",
  aggregation: "mean"  // mean, majority, weighted, ensemble
})
```

### Template Management

```javascript
// List templates
mcp__flow-nexus__neural_list_templates({
  category: "classification",  // timeseries, regression, nlp, vision, anomaly, generative, reinforcement, custom
  tier: "free",               // free, paid
  search: "sentiment",
  limit: 20
})

// Deploy template
mcp__flow-nexus__neural_deploy_template({
  template_id: "template_id",
  custom_config: { epochs: 50 },
  user_id: "user_id"
})

// Publish your model as template
mcp__flow-nexus__neural_publish_template({
  model_id: "model_id",
  name: "Sentiment Analyzer",
  description: "LSTM-based sentiment analysis model",
  category: "nlp",
  price: 0,
  user_id: "user_id"
})

// Rate a template
mcp__flow-nexus__neural_rate_template({
  template_id: "template_id",
  rating: 5,
  review: "Excellent model, fast and accurate",
  user_id: "user_id"
})
```

### Model Management

```javascript
// List user models
mcp__flow-nexus__neural_list_models({
  user_id: "user_id",
  include_public: false
})

// Check training status
mcp__flow-nexus__neural_training_status({ job_id: "job_id" })

// Create validation workflow
mcp__flow-nexus__neural_validation_workflow({
  model_id: "model_id",
  validation_type: "comprehensive",  // performance, accuracy, robustness, comprehensive
  user_id: "user_id"
})

// Run performance benchmarks
mcp__flow-nexus__neural_performance_benchmark({
  model_id: "model_id",
  benchmark_type: "comprehensive"  // inference, throughput, memory, comprehensive
})
```

## Usage Examples

### Example 1: Classification Model Training

```javascript
// Train a feedforward classifier
const trainingJob = await mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "feedforward",
      layers: [
        { type: "dense", units: 256, activation: "relu" },
        { type: "batch_norm" },
        { type: "dropout", rate: 0.3 },
        { type: "dense", units: 128, activation: "relu" },
        { type: "dropout", rate: 0.2 },
        { type: "dense", units: 10, activation: "softmax" }
      ]
    },
    training: {
      epochs: 100,
      batch_size: 64,
      learning_rate: 0.001,
      optimizer: "adam"
    }
  },
  tier: "small"
});

// Monitor training
const status = await mcp__flow-nexus__neural_training_status({
  job_id: trainingJob.job_id
});

console.log(`Epoch: ${status.current_epoch}, Loss: ${status.loss}`);

// Run inference on trained model
const prediction = await mcp__flow-nexus__neural_predict({
  model_id: trainingJob.model_id,
  input: [[0.1, 0.2, 0.3, 0.4, 0.5]]
});
```

### Example 2: Distributed Transformer Training

```javascript
// Initialize distributed cluster
const cluster = await mcp__flow-nexus__neural_cluster_init({
  name: "transformer-cluster",
  architecture: "transformer",
  topology: "mesh",
  consensus: "proof-of-learning",
  daaEnabled: true,
  wasmOptimization: true
});

// Deploy 4 worker nodes
for (let i = 0; i < 4; i++) {
  await mcp__flow-nexus__neural_node_deploy({
    cluster_id: cluster.cluster_id,
    node_type: "worker",
    model: "large",
    capabilities: ["training", "inference"]
  });
}

// Deploy parameter server
await mcp__flow-nexus__neural_node_deploy({
  cluster_id: cluster.cluster_id,
  node_type: "parameter_server",
  model: "base"
});

// Connect nodes
await mcp__flow-nexus__neural_cluster_connect({
  cluster_id: cluster.cluster_id
});

// Start distributed training
await mcp__flow-nexus__neural_train_distributed({
  cluster_id: cluster.cluster_id,
  dataset: "large_nlp_dataset",
  epochs: 50,
  batch_size: 128,
  learning_rate: 0.0001,
  optimizer: "adam"
});

// Monitor and validate
const clusterStatus = await mcp__flow-nexus__neural_cluster_status({
  cluster_id: cluster.cluster_id
});

// Cleanup
await mcp__flow-nexus__neural_cluster_terminate({
  cluster_id: cluster.cluster_id
});
```

### Example 3: Using Pre-built Templates

```javascript
// Find NLP templates
const templates = await mcp__flow-nexus__neural_list_templates({
  category: "nlp",
  tier: "free",
  search: "sentiment"
});

// Deploy the best-rated template
const deployment = await mcp__flow-nexus__neural_deploy_template({
  template_id: templates.templates[0].id,
  custom_config: {
    epochs: 25,
    learning_rate: 0.0005
  }
});

// Validate model performance
await mcp__flow-nexus__neural_validation_workflow({
  model_id: deployment.model_id,
  validation_type: "comprehensive"
});

// Benchmark performance
const benchmark = await mcp__flow-nexus__neural_performance_benchmark({
  model_id: deployment.model_id,
  benchmark_type: "comprehensive"
});

console.log(`Inference latency: ${benchmark.inference_latency_ms}ms`);
```

## Execution Checklist

- [ ] Design neural architecture for task requirements
- [ ] Select appropriate training tier based on model size
- [ ] Configure training hyperparameters
- [ ] Initialize training (single or distributed)
- [ ] Monitor training progress and metrics
- [ ] Validate model performance
- [ ] Run benchmarks for production readiness
- [ ] Deploy for inference or publish as template
- [ ] Cleanup cluster resources when complete

## Best Practices

1. **Start Small**: Begin with `nano` or `mini` tier for testing, scale up for production
2. **Proper Validation**: Always run validation workflow before production deployment
3. **Hyperparameter Tuning**: Use grid search or Bayesian optimization for best results
4. **Distributed Training**: Use for large models; single-node for smaller experiments
5. **Checkpoint Frequently**: Enable checkpointing for long training runs
6. **Monitor Drift**: Implement drift detection for production models

## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| `training_failed` | Invalid architecture config | Verify layer compatibility and types |
| `cluster_init_failed` | Invalid topology or architecture | Check supported combinations |
| `insufficient_credits` | Training tier exceeds balance | Reduce tier or add credits |
| `model_not_found` | Invalid model_id | Use `neural_list_models` to verify |
| `node_deploy_failed` | Cluster capacity reached | Terminate unused nodes |

## Metrics & Success Criteria

- **Training Convergence**: Loss decreasing over epochs
- **Validation Accuracy**: Target >90% for classification
- **Inference Latency**: <100ms for production
- **Memory Efficiency**: <80% resource utilization
- **Model Size**: Appropriate for deployment target

## Integration Points

### With Swarms

```javascript
// Deploy neural agent in swarm
await mcp__flow-nexus__agent_spawn({
  type: "analyst",
  name: "ML Analyst",
  capabilities: ["neural_training", "model_evaluation"]
});
```

### With Workflows

```javascript
// ML pipeline workflow
await mcp__flow-nexus__workflow_create({
  name: "ML Training Pipeline",
  steps: [
    { id: "preprocess", action: "data_prep" },
    { id: "train", action: "neural_train", depends: ["preprocess"] },
    { id: "validate", action: "neural_validate", depends: ["train"] },
    { id: "deploy", action: "neural_deploy", depends: ["validate"] }
  ]
});
```

### Related Skills

- [cloud-swarm](../cloud-swarm/SKILL.md) - Multi-agent orchestration
- [cloud-sandbox](../cloud-sandbox/SKILL.md) - Isolated execution environments
- [cloud-workflow](../cloud-workflow/SKILL.md) - Workflow automation

## References

- [Flow Nexus Neural Documentation](https://flow-nexus.ruv.io)
- [Distributed Training Best Practices](https://github.com/ruvnet/claude-flow)

## Version History

- **1.0.0** (2026-01-02): Initial release - converted from flow-nexus-neural agent