Flow Nexus Neural Networks

Deploy, train, and manage neural networks in distributed E2B sandbox environments. Train custom models with multiple architectures (feedforward, LSTM, GAN, transformer) or use pre-built templates from the marketplace.

Prerequisites

# Add Flow Nexus MCP server
claude mcp add flow-nexus npx flow-nexus@latest mcp start

# Register and login
npx flow-nexus@latest register
npx flow-nexus@latest login

Core Capabilities

1. Single-Node Neural Training

Train neural networks with custom architectures and configurations.

Available Architectures:

• feedforward - Standard fully-connected networks
• lstm - Long Short-Term Memory for sequences
• gan - Generative Adversarial Networks
• autoencoder - Dimensionality reduction
• transformer - Attention-based models

Training Tiers:

• nano - Minimal resources (fast, limited)
• mini - Small models
• small - Standard models
• medium - Complex models
• large - Large-scale training

Example: Train Custom Classifier

mcp__flow -
  nexus__neural_train({
    config: {
      architecture: {
        type: 'feedforward',
        layers: [
          { type: 'dense', units: 256, activation: 'relu' },
          { type: 'dropout', rate: 0.3 },
          { type: 'dense', units: 128, activation: 'relu' },
          { type: 'dropout', rate: 0.2 },
          { type: 'dense', units: 64, activation: 'relu' },
          { type: 'dense', units: 10, activation: 'softmax' },
        ],
      },
      training: {
        epochs: 100,
        batch_size: 32,
        learning_rate: 0.001,
        optimizer: 'adam',
      },
      divergent: {
        enabled: true,
        pattern: 'lateral', // quantum, chaotic, associative, evolutionary
        factor: 0.5,
      },
    },
    tier: 'small',
    user_id: 'your_user_id',
  });

Example: LSTM for Time Series

mcp__flow -
  nexus__neural_train({
    config: {
      architecture: {
        type: 'lstm',
        layers: [
          { type: 'lstm', units: 128, return_sequences: true },
          { type: 'dropout', rate: 0.2 },
          { type: 'lstm', units: 64 },
          { type: 'dense', units: 1, activation: 'linear' },
        ],
      },
      training: {
        epochs: 150,
        batch_size: 64,
        learning_rate: 0.01,
        optimizer: 'adam',
      },
    },
    tier: 'medium',
  });

Example: Transformer Architecture

mcp__flow -
  nexus__neural_train({
    config: {
      architecture: {
        type: 'transformer',
        layers: [
          { type: 'embedding', vocab_size: 10000, embedding_dim: 512 },
          { type: 'transformer_encoder', num_heads: 8, ff_dim: 2048 },
          { type: 'global_average_pooling' },
          { type: 'dense', units: 128, activation: 'relu' },
          { type: 'dense', units: 2, activation: 'softmax' },
        ],
      },
      training: {
        epochs: 50,
        batch_size: 16,
        learning_rate: 0.0001,
        optimizer: 'adam',
      },
    },
    tier: 'large',
  });

2. Model Inference

Run predictions on trained models.

mcp__flow -
  nexus__neural_predict({
    model_id: 'model_abc123',
    input: [
      [0.5, 0.3, 0.2, 0.1],
      [0.8, 0.1, 0.05, 0.05],
      [0.2, 0.6, 0.15, 0.05],
    ],
    user_id: 'your_user_id',
  });

Response:

{
  "predictions": [
    [0.12, 0.85, 0.03],
    [0.89, 0.08, 0.03],
    [0.05, 0.92, 0.03]
  ],
  "inference_time_ms": 45,
  "model_version": "1.0.0"
}

3. Template Marketplace

Browse and deploy pre-trained models from the marketplace.

List Available Templates

mcp__flow -
  nexus__neural_list_templates({
    category: 'classification', // timeseries, regression, nlp, vision, anomaly, generative
    tier: 'free', // or "paid"
    search: 'sentiment',
    limit: 20,
  });

Response:

{
  "templates": [
    {
      "id": "sentiment-analysis-v2",
      "name": "Sentiment Analysis Classifier",
      "description": "Pre-trained BERT model for sentiment analysis",
      "category": "nlp",
      "accuracy": 0.94,
      "downloads": 1523,
      "tier": "free"
    },
    {
      "id": "image-classifier-resnet",
      "name": "ResNet Image Classifier",
      "description": "ResNet-50 for image classification",
      "category": "vision",
      "accuracy": 0.96,
      "downloads": 2341,
      "tier": "paid"
    }
  ]
}

Deploy Template

mcp__flow -
  nexus__neural_deploy_template({
    template_id: 'sentiment-analysis-v2',
    custom_config: {
      training: {
        epochs: 50,
        learning_rate: 0.0001,
      },
    },
    user_id: 'your_user_id',
  });

4. Distributed Training Clusters

Train large models across multiple E2B sandboxes with distributed computing.

Initialize Cluster

mcp__flow -
  nexus__neural_cluster_init({
    name: 'large-model-cluster',
    architecture: 'transformer', // transformer, cnn, rnn, gnn, hybrid
    topology: 'mesh', // mesh, ring, star, hierarchical
    consensus: 'proof-of-learning', // byzantine, raft, gossip
    daaEnabled: true, // Decentralized Autonomous Agents
    wasmOptimization: true,
  });

Response:

{
  "cluster_id": "cluster_xyz789",
  "name": "large-model-cluster",
  "status": "initializing",
  "topology": "mesh",
  "max_nodes": 100,
  "created_at": "2025-10-19T10:30:00Z"
}

Deploy Worker Nodes

// Deploy parameter server
mcp__flow -
  nexus__neural_node_deploy({
    cluster_id: 'cluster_xyz789',
    node_type: 'parameter_server',
    model: 'large',
    template: 'nodejs',
    capabilities: ['parameter_management', 'gradient_aggregation'],
    autonomy: 0.8,
  });

// Deploy worker nodes
mcp__flow -
  nexus__neural_node_deploy({
    cluster_id: 'cluster_xyz789',
    node_type: 'worker',
    model: 'xl',
    role: 'worker',
    capabilities: ['training', 'inference'],
    layers: [
      { type: 'transformer_encoder', num_heads: 16 },
      { type: 'feed_forward', units: 4096 },
    ],
    autonomy: 0.9,
  });

// Deploy aggregator
mcp__flow -
  nexus__neural_node_deploy({
    cluster_id: 'cluster_xyz789',
    node_type: 'aggregator',
    model: 'large',
    capabilities: ['gradient_aggregation', 'model_synchronization'],
  });

Connect Cluster Topology

mcp__flow -
  nexus__neural_cluster_connect({
    cluster_id: 'cluster_xyz789',
    topology: 'mesh', // Override default if needed
  });

Start Distributed Training

mcp__flow -
  nexus__neural_train_distributed({
    cluster_id: 'cluster_xyz789',
    dataset: 'imagenet', // or custom dataset identifier
    epochs: 100,
    batch_size: 128,
    learning_rate: 0.001,
    optimizer: 'adam', // sgd, rmsprop, adagrad
    federated: true, // Enable federated learning
  });

Federated Learning Example:

mcp__flow -
  nexus__neural_train_distributed({
    cluster_id: 'cluster_xyz789',
    dataset: 'medical_images_distributed',
    epochs: 200,
    batch_size: 64,
    learning_rate: 0.0001,
    optimizer: 'adam',
    federated: true, // Data stays on local nodes
    aggregation_rounds: 50,
    min_nodes_per_round: 5,
  });

Monitor Cluster Status

mcp__flow -
  nexus__neural_cluster_status({
    cluster_id: 'cluster_xyz789',
  });

Response:

{
  "cluster_id": "cluster_xyz789",
  "status": "training",
  "nodes": [
    {
      "node_id": "node_001",
      "type": "parameter_server",
      "status": "active",
      "cpu_usage": 0.75,
      "memory_usage": 0.82
    },
    {
      "node_id": "node_002",
      "type": "worker",
      "status": "active",
      "training_progress": 0.45
    }
  ],
  "training_metrics": {
    "current_epoch": 45,
    "total_epochs": 100,
    "loss": 0.234,
    "accuracy": 0.891
  }
}

Run Distributed Inference

mcp__flow -
  nexus__neural_predict_distributed({
    cluster_id: 'cluster_xyz789',
    input_data: JSON.stringify([
      [0.1, 0.2, 0.3],
      [0.4, 0.5, 0.6],
    ]),
    aggregation: 'ensemble', // mean, majority, weighted, ensemble
  });

Terminate Cluster

mcp__flow -
  nexus__neural_cluster_terminate({
    cluster_id: 'cluster_xyz789',
  });

5. Model Management

List Your Models

mcp__flow -
  nexus__neural_list_models({
    user_id: 'your_user_id',
    include_public: true,
  });

Response:

{
  "models": [
    {
      "model_id": "model_abc123",
      "name": "Custom Classifier v1",
      "architecture": "feedforward",
      "accuracy": 0.92,
      "created_at": "2025-10-15T14:20:00Z",
      "status": "trained"
    },
    {
      "model_id": "model_def456",
      "name": "LSTM Forecaster",
      "architecture": "lstm",
      "mse": 0.0045,
      "created_at": "2025-10-18T09:15:00Z",
      "status": "training"
    }
  ]
}

Check Training Status

mcp__flow -
  nexus__neural_training_status({
    job_id: 'job_training_xyz',
  });

Response:

{
  "job_id": "job_training_xyz",
  "status": "training",
  "progress": 0.67,
  "current_epoch": 67,
  "total_epochs": 100,
  "current_loss": 0.234,
  "estimated_completion": "2025-10-19T12:45:00Z"
}

Performance Benchmarking

mcp__flow -
  nexus__neural_performance_benchmark({
    model_id: 'model_abc123',
    benchmark_type: 'comprehensive', // inference, throughput, memory, comprehensive
  });

Response:

{
  "model_id": "model_abc123",
  "benchmarks": {
    "inference_latency_ms": 12.5,
    "throughput_qps": 8000,
    "memory_usage_mb": 245,
    "gpu_utilization": 0.78,
    "accuracy": 0.92,
    "f1_score": 0.89
  },
  "timestamp": "2025-10-19T11:00:00Z"
}

Create Validation Workflow

mcp__flow -
  nexus__neural_validation_workflow({
    model_id: 'model_abc123',
    user_id: 'your_user_id',
    validation_type: 'comprehensive', // performance, accuracy, robustness, comprehensive
  });

6. Publishing and Marketplace

Publish Model as Template

mcp__flow -
  nexus__neural_publish_template({
    model_id: 'model_abc123',
    name: 'High-Accuracy Sentiment Classifier',
    description: 'Fine-tuned BERT model for sentiment analysis with 94% accuracy',
    category: 'nlp',
    price: 0, // 0 for free, or credits amount
    user_id: 'your_user_id',
  });

Rate a Template

mcp__flow -
  nexus__neural_rate_template({
    template_id: 'sentiment-analysis-v2',
    rating: 5,
    review: 'Excellent model! Achieved 95% accuracy on my dataset.',
    user_id: 'your_user_id',
  });

Common Use Cases

Image Classification with CNN

// Initialize cluster for large-scale image training
const cluster =
  (await mcp__flow) -
  nexus__neural_cluster_init({
    name: 'image-classification-cluster',
    architecture: 'cnn',
    topology: 'hierarchical',
    wasmOptimization: true,
  });

// Deploy worker nodes
(await mcp__flow) -
  nexus__neural_node_deploy({
    cluster_id: cluster.cluster_id,
    node_type: 'worker',
    model: 'large',
    capabilities: ['training', 'data_augmentation'],
  });

// Start training
(await mcp__flow) -
  nexus__neural_train_distributed({
    cluster_id: cluster.cluster_id,
    dataset: 'custom_images',
    epochs: 100,
    batch_size: 64,
    learning_rate: 0.001,
    optimizer: 'adam',
  });

NLP Sentiment Analysis

// Use pre-built template
const deployment =
  (await mcp__flow) -
  nexus__neural_deploy_template({
    template_id: 'sentiment-analysis-v2',
    custom_config: {
      training: {
        epochs: 30,
        batch_size: 16,
      },
    },
  });

// Run inference
const result =
  (await mcp__flow) -
  nexus__neural_predict({
    model_id: deployment.model_id,
    input: ['This product is amazing!', 'Terrible experience.'],
  });

Time Series Forecasting

// Train LSTM model
const training =
  (await mcp__flow) -
  nexus__neural_train({
    config: {
      architecture: {
        type: 'lstm',
        layers: [
          { type: 'lstm', units: 128, return_sequences: true },
          { type: 'dropout', rate: 0.2 },
          { type: 'lstm', units: 64 },
          { type: 'dense', units: 1 },
        ],
      },
      training: {
        epochs: 150,
        batch_size: 64,
        learning_rate: 0.01,
        optimizer: 'adam',
      },
    },
    tier: 'medium',
  });

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

Federated Learning for Privacy

// Initialize federated cluster
const cluster =
  (await mcp__flow) -
  nexus__neural_cluster_init({
    name: 'federated-medical-cluster',
    architecture: 'transformer',
    topology: 'mesh',
    consensus: 'proof-of-learning',
    daaEnabled: true,
  });

// Deploy nodes across different locations
for (let i = 0; i < 5; i++) {
  (await mcp__flow) -
    nexus__neural_node_deploy({
      cluster_id: cluster.cluster_id,
      node_type: 'worker',
      model: 'large',
      autonomy: 0.9,
    });
}

// Train with federated learning (data never leaves nodes)
(await mcp__flow) -
  nexus__neural_train_distributed({
    cluster_id: cluster.cluster_id,
    dataset: 'medical_records_distributed',
    epochs: 200,
    federated: true,
    aggregation_rounds: 100,
  });

Architecture Patterns

Feedforward Networks

Best for: Classification, regression, simple pattern recognition

{
  type: "feedforward",
  layers: [
    { type: "dense", units: 256, activation: "relu" },
    { type: "dropout", rate: 0.3 },
    { type: "dense", units: 128, activation: "relu" },
    { type: "dense", units: 10, activation: "softmax" }
  ]
}

LSTM Networks

Best for: Time series, sequences, forecasting

{
  type: "lstm",
  layers: [
    { type: "lstm", units: 128, return_sequences: true },
    { type: "lstm", units: 64 },
    { type: "dense", units: 1 }
  ]
}

Transformers

Best for: NLP, attention mechanisms, large-scale text

{
  type: "transformer",
  layers: [
    { type: "embedding", vocab_size: 10000, embedding_dim: 512 },
    { type: "transformer_encoder", num_heads: 8, ff_dim: 2048 },
    { type: "global_average_pooling" },
    { type: "dense", units: 2, activation: "softmax" }
  ]
}

GANs

Best for: Generative tasks, image synthesis

{
  type: "gan",
  generator_layers: [...],
  discriminator_layers: [...]
}

Autoencoders

Best for: Dimensionality reduction, anomaly detection

{
  type: "autoencoder",
  encoder_layers: [
    { type: "dense", units: 128, activation: "relu" },
    { type: "dense", units: 64, activation: "relu" }
  ],
  decoder_layers: [
    { type: "dense", units: 128, activation: "relu" },
    { type: "dense", units: input_dim, activation: "sigmoid" }
  ]
}

Best Practices

1. Start Small: Begin with nano or mini tiers for experimentation
2. Use Templates: Leverage marketplace templates for common tasks
3. Monitor Training: Check status regularly to catch issues early
4. Benchmark Models: Always benchmark before production deployment
5. Distributed Training: Use clusters for large models (>1B parameters)
6. Federated Learning: Use for privacy-sensitive data
7. Version Models: Publish successful models as templates for reuse
8. Validate Thoroughly: Use validation workflows before deployment

Troubleshooting

Training Stalled

// Check cluster status
const status =
  (await mcp__flow) -
  nexus__neural_cluster_status({
    cluster_id: 'cluster_id',
  });

// Terminate and restart if needed
(await mcp__flow) -
  nexus__neural_cluster_terminate({
    cluster_id: 'cluster_id',
  });

Low Accuracy

• Increase epochs
• Adjust learning rate
• Add regularization (dropout)
• Try different optimizer
• Use data augmentation

Out of Memory

• Reduce batch size
• Use smaller model tier
• Enable gradient accumulation
• Use distributed training

Related Skills

• flow-nexus-sandbox - E2B sandbox management
• flow-nexus-swarm - AI swarm orchestration
• flow-nexus-workflow - Workflow automation

Resources

• Flow Nexus Docs: https://flow-nexus.ruv.io/docs
• Neural Network Guide: https://flow-nexus.ruv.io/docs/neural
• Template Marketplace: https://flow-nexus.ruv.io/templates
• API Reference: https://flow-nexus.ruv.io/api

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Note: Distributed training requires authentication. Register at https://flow-nexus.ruv.io or use npx flow-nexus@latest register.