Flow Nexus Neural

ruvnet/ruflo/v3/@claude-flow/cli/.claude/skills/flow-nexus-neural

作者 ruvnet5f709e36799274be6bb68fff69090bdbc8621220無授權條款74K 個星標收錄於 2026年10月9日更新於 2026年10月9日儲存庫今天更新

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

僅含說明AI & AgentsDevOps & Cloud
AI 產生的概覽

透過 Flow Nexus MCP 伺服器在分散式 E2B 沙箱中訓練、部署與管理神經網路。

功能
本技能說明如何使用 Flow Nexus MCP 工具訓練神經網路，支援前饋、LSTM、GAN、自編碼器與 Transformer 等架構，並可選擇不同資源等級。內容涵蓋執行推論、瀏覽與部署市集範本、初始化包含工作節點、參數伺服器與聚合節點的分散式訓練叢集、監控訓練狀態、效能基準測試、驗證工作流程，以及發佈或評價範本。這是一份僅含說明的指南，提供設定範例，並未附帶任何指令碼。
適用情境
當你需要透過 Flow Nexus 平台訓練、部署或維運神經網路模型時使用，包括跨沙箱的分散式或聯邦式訓練。它也適合挑選預建的市集範本、執行預測，或對現有模型進行基準測試與驗證。
執行需求
需透過 npx flow-nexus@latest mcp start 加入 Flow Nexus MCP 伺服器，並註冊與登入以執行分散式訓練等需要驗證的操作。需要連線至 Flow Nexus 服務的網路存取；未附帶任何指令碼。

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

bash
# Add Flow Nexus MCP serverclaude mcp add flow-nexus npx flow-nexus@latest mcp start
# Register and loginnpx flow-nexus@latest registernpx 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 servermcp__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 nodesmcp__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 aggregatormcp__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 trainingconst cluster = await mcp__flow-nexus__neural_cluster_init({  name: "image-classification-cluster",  architecture: "cnn",  topology: "hierarchical",  wasmOptimization: true})
// Deploy worker nodesawait mcp__flow-nexus__neural_node_deploy({  cluster_id: cluster.cluster_id,  node_type: "worker",  model: "large",  capabilities: ["training", "data_augmentation"]})
// Start trainingawait 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 templateconst deployment = await mcp__flow-nexus__neural_deploy_template({  template_id: "sentiment-analysis-v2",  custom_config: {    training: {      epochs: 30,      batch_size: 16    }  }})
// Run inferenceconst 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 modelconst 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 progressconst status = await mcp__flow-nexus__neural_training_status({  job_id: training.job_id})

Federated Learning for Privacy

javascript
// Initialize federated clusterconst 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 locationsfor (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

  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

javascript
// Check cluster statusconst status = await mcp__flow-nexus__neural_cluster_status({  cluster_id: "cluster_id"})
// Terminate and restart if neededawait 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

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

來源與署名

來源：ruvnet/ruflo位於v3/@claude-flow/cli/.claude/skills/flow-nexus-neural提交5f709e3

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