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

| 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

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

Start Small: Begin with nano or mini tier for testing, scale up for production
Proper Validation: Always run validation workflow before production deployment
Hyperparameter Tuning: Use grid search or Bayesian optimization for best results
Distributed Training: Use for large models; single-node for smaller experiments
Checkpoint Frequently: Enable checkpointing for long training runs
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

// 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

Version History

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