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Flow Nexus Neural Networks

Flow Nexus 神经网络

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.
在分布式E2B沙箱环境中部署、训练和管理神经网络。可使用多种架构（前馈网络、LSTM、GAN、Transformer）训练自定义模型，或使用市场中的预构建模板。

Prerequisites

前置条件

bash
undefined
bash
undefined

Add Flow Nexus MCP server

添加Flow Nexus MCP服务器

claude mcp add flow-nexus npx flow-nexus@latest mcp start
claude mcp add flow-nexus npx flow-nexus@latest mcp start

Register and login

注册并登录

npx flow-nexus@latest register npx flow-nexus@latest login
undefined
npx flow-nexus@latest register npx flow-nexus@latest login
undefined

Core Capabilities

核心功能

1. Single-Node Neural Training

1. 单节点神经网络训练

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
使用自定义架构和配置训练神经网络。
支持的网络架构：
  • feedforward
    - 标准全连接网络
  • lstm
    - 用于序列数据的长短期记忆网络
  • gan
    - 生成对抗网络
  • autoencoder
    - 降维网络
  • transformer
    - 基于注意力机制的模型
训练层级：
  • nano
    - 最小资源配置（速度快，资源有限）
  • mini
    - 小型模型
  • small
    - 标准模型
  • medium
    - 复杂模型
  • large
    - 大规模训练

Example: Train Custom Classifier

示例：训练自定义分类器

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"
})
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

示例：用于时间序列的LSTM模型

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"
})
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

示例：Transformer架构

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"
})
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

2. 模型推理

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"
}
在已训练模型上运行预测。
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"
})
响应：
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

3. 模板市场

Browse and deploy pre-trained models from the marketplace.
浏览并部署市场中的预训练模型。

List Available Templates

列出可用模板

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

部署模板

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"
})
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

4. 分布式训练集群

Train large models across multiple E2B sandboxes with distributed computing.
通过分布式计算在多个E2B沙箱中训练大型模型。

Initialize Cluster

初始化集群

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"
}
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
})
响应：
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

部署工作节点

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"]
})
javascript
// 部署参数服务器
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
})

// 部署工作节点
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
})

// 部署聚合器
mcp__flow-nexus__neural_node_deploy({
  cluster_id: "cluster_xyz789",
  node_type: "aggregator",
  model: "large",
  capabilities: ["gradient_aggregation", "model_synchronization"]
})

Connect Cluster Topology

连接集群拓扑

javascript
mcp__flow-nexus__neural_cluster_connect({
  cluster_id: "cluster_xyz789",
  topology: "mesh" // Override default if needed
})
javascript
mcp__flow-nexus__neural_cluster_connect({
  cluster_id: "cluster_xyz789",
  topology: "mesh" // 如需覆盖默认值
})

Start Distributed Training

启动分布式训练

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
})
javascript
mcp__flow-nexus__neural_train_distributed({
  cluster_id: "cluster_xyz789",
  dataset: "imagenet", // 或自定义数据集标识符
  epochs: 100,
  batch_size: 128,
  learning_rate: 0.001,
  optimizer: "adam", // sgd, rmsprop, adagrad
  federated: true // 启用联邦学习
})
联邦学习示例：
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, // 数据保留在本地节点
  aggregation_rounds: 50,
  min_nodes_per_round: 5
})

Monitor Cluster Status

监控集群状态

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

运行分布式推理

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
})
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

终止集群

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

5. Model Management

5. 模型管理

List Your Models

列出你的模型

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

检查训练状态

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

性能基准测试

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

创建验证工作流

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

6. 发布与市场

Publish Model as Template

将模型发布为模板

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"
})
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表示免费，或设置积分金额
  user_id: "your_user_id"
})

Rate a Template

为模板评分

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"
})
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

常见用例

Image Classification with CNN

基于CNN的图像分类

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"
})
javascript
// 初始化用于大规模图像训练的集群
const cluster = await mcp__flow-nexus__neural_cluster_init({
  name: "image-classification-cluster",
  architecture: "cnn",
  topology: "hierarchical",
  wasmOptimization: true
})

// 部署工作节点
await mcp__flow-nexus__neural_node_deploy({
  cluster_id: cluster.cluster_id,
  node_type: "worker",
  model: "large",
  capabilities: ["training", "data_augmentation"]
})

// 启动训练
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

NLP情感分析

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."]
})
javascript
// 使用预构建模板
const deployment = await mcp__flow-nexus__neural_deploy_template({
  template_id: "sentiment-analysis-v2",
  custom_config: {
    training: {
      epochs: 30,
      batch_size: 16
    }
  }
})

// 运行推理
const result = await mcp__flow-nexus__neural_predict({
  model_id: deployment.model_id,
  input: ["This product is amazing!", "Terrible experience."]
})

Time Series Forecasting

时间序列预测

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
})
javascript
// 训练LSTM模型
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"
})

// 监控进度
const status = await mcp__flow-nexus__neural_training_status({
  job_id: training.job_id
})

Federated Learning for Privacy

面向隐私的联邦学习

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
})
javascript
// 初始化联邦集群
const cluster = await mcp__flow-nexus__neural_cluster_init({
  name: "federated-medical-cluster",
  architecture: "transformer",
  topology: "mesh",
  consensus: "proof-of-learning",
  daaEnabled: true
})

// 在不同位置部署节点
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
  })
}

// 使用联邦学习训练（数据永远不会离开节点）
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

架构模式

Feedforward Networks

前馈网络

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" }
  ]
}
最适合：分类、回归、简单模式识别
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

LSTM网络

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 }
  ]
}
最适合：时间序列、序列数据、预测
javascript
{
  type: "lstm",
  layers: [
    { type: "lstm", units: 128, return_sequences: true },
    { type: "lstm", units: 64 },
    { type: "dense", units: 1 }
  ]
}

Transformers

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" }
  ]
}
最适合：NLP、注意力机制、大规模文本
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: 128, activation: "relu" },
    { type: "dense", units: 2, activation: "softmax" }
  ]
}

GANs

GANs

Best for: Generative tasks, image synthesis
javascript
{
  type: "gan",
  generator_layers: [...],
  discriminator_layers: [...]
}
最适合：生成任务、图像合成
javascript
{
  type: "gan",
  generator_layers: [...],
  discriminator_layers: [...]
}

Autoencoders

自动编码器

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" }
  ]
}
最适合：降维、异常检测
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

最佳实践

  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
  1. 从小规模开始：实验阶段使用
    nano
    或
    mini
    层级
  2. 使用模板：针对常见任务利用市场模板
  3. 监控训练：定期检查状态以尽早发现问题
  4. 模型基准测试：部署到生产环境前务必进行基准测试
  5. 分布式训练：针对大型模型（>10亿参数）使用集群
  6. 联邦学习：针对隐私敏感数据使用
  7. 模型版本化：将成功模型发布为模板以便复用
  8. 充分验证：部署前使用验证工作流

Troubleshooting

故障排除

Training Stalled

训练停滞

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"
})
javascript
// 检查集群状态
const status = await mcp__flow-nexus__neural_cluster_status({
  cluster_id: "cluster_id"
})

// 如有需要，终止并重启
await mcp__flow-nexus__neural_cluster_terminate({
  cluster_id: "cluster_id"
})

Low Accuracy

准确率低

  • Increase epochs
  • Adjust learning rate
  • Add regularization (dropout)
  • Try different optimizer
  • Use data augmentation
  • 增加训练轮数
  • 调整学习率
  • 添加正则化（如dropout）
  • 尝试不同优化器
  • 使用数据增强

Out of Memory

内存不足

  • Reduce batch size
  • Use smaller model tier
  • Enable gradient accumulation
  • Use distributed training
  • 减小批量大小
  • 使用更小的模型层级
  • 启用梯度累积
  • 使用分布式训练

Related Skills

相关技能

  • flow-nexus-sandbox
    - E2B sandbox management
  • flow-nexus-swarm
    - AI swarm orchestration
  • flow-nexus-workflow
    - Workflow automation
  • flow-nexus-sandbox
    - E2B沙箱管理
  • flow-nexus-swarm
    - AI集群编排
  • flow-nexus-workflow
    - 工作流自动化

Resources

资源

  • 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
.
  • Flow Nexus文档：https:/$flow-nexus.ruv.io$docs
  • 神经网络指南：https:/$flow-nexus.ruv.io$docs$neural
  • 模板市场：https:/$flow-nexus.ruv.io$templates
  • API参考：https:/$flow-nexus.ruv.io$api
注意：分布式训练需要身份验证。请访问https:/$flow-nexus.ruv.io注册，或使用
npx flow-nexus@latest register
。