cloud-neural（云神经）

Cloud Neural Network

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

Quick Start

// 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

Training Tiers

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

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

// 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

// 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

// 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

// 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

// 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

// 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

// 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

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

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

With Workflows

// 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 - Multi-agent orchestration
• cloud-sandbox - Isolated execution environments
• cloud-workflow - Workflow automation

References

• Flow Nexus Neural Documentation
• Distributed Training Best Practices

Version History

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