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name: flow-nexus-neural description: Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus version: 1.0.0 category: ai-ml tags:

neural-networks
distributed-training
machine-learning
deep-learning
flow-nexus
e2b-sandboxes

requires_auth: true mcp_server: flow-nexus hooks: pre: | echo "🧠 Flow Nexus Neural activated" if [ -d "/workspaces/ruvector/.claude/intelligence" ]; then cd /workspaces/ruvector/.claude/intelligence INTELLIGENCE_MODE=treatment node cli.js pre-edit "$FILE" 2>/dev/null || true fi post: | echo "✅ Flow Nexus Neural complete" if [ -d "/workspaces/ruvector/.claude/intelligence" ]; then cd /workspaces/ruvector/.claude/intelligence INTELLIGENCE_MODE=treatment node cli.js post-edit "$FILE" "true" 2>/dev/null || true fi

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.

Self-Learning Intelligence

Integrates with RuVector's Q-learning and vector memory for improved performance. CLI: node .claude/intelligence/cli.js stats

Prerequisites

bash

# 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

javascript

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

javascript

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

javascript

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.

javascript

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:

json

{
  "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

javascript

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

Response:

json

{
  "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

javascript

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

javascript

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:

json

{
  "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

javascript

// 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

javascript

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

Start Distributed Training

javascript

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:

javascript

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

javascript

mcp__flow-nexus__neural_cluster_status({
  cluster_id: "cluster_xyz789"
})

Response:

json

{
  "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

javascript

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

javascript

mcp__flow-nexus__neural_cluster_terminate({
  cluster_id: "cluster_xyz789"
})

5. Model Management

List Your Models

javascript

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

Response:

json

{
  "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

javascript

mcp__flow-nexus__neural_training_status({
  job_id: "job_training_xyz"
})

Response:

json

{
  "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

javascript

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

Response:

json

{
  "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

javascript

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

javascript

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

javascript

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

javascript

// 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

javascript

// 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

javascript

// 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

javascript

// 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

javascript

{
  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

javascript

{
  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

javascript

{
  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

javascript

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

Autoencoders

Best for: Dimensionality reduction, anomaly detection

javascript

{
  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

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

Troubleshooting

Training Stalled

javascript

// 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

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

All versions