Flow Nexus Neural

by ruvnet60de638630abNo license74K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

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

Instructions onlyAI & Agents DevOps & Cloud

AI-generated overview

Trains, deploys and manages neural networks in distributed E2B sandboxes through the Flow Nexus MCP server.

What it does: This skill documents how to use Flow Nexus MCP tools to train neural networks with architectures such as feedforward, LSTM, GAN, autoencoder and transformer, at selectable resource tiers. It covers running inference, browsing and deploying marketplace templates, initializing distributed training clusters with worker, parameter-server and aggregator nodes, monitoring training status, benchmarking, validation workflows, and publishing or rating templates. It is an instruction-only guide with configuration examples and no bundled scripts.
When to use it: Use it when you need to train, deploy or operate neural network models through the Flow Nexus platform, including distributed or federated training across sandboxes. It also fits selecting pre-built marketplace templates, running predictions, or benchmarking and validating existing models.
Requirements: The Flow Nexus MCP server added via npx flow-nexus@latest mcp start, plus registration and login for authenticated operations such as distributed training. Network access to the Flow Nexus service is required; no scripts are shipped.

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

# 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"})

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"})

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"})

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"})

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"}

{  "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})

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"    }  ]}

{  "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"})

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})

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"}

{  "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"]})

// 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})

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})

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})

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

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  }}

{  "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})

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

5. Model Management

List Your Models

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"    }  ]}

{  "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

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"}

{  "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})

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"}

{  "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})

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"})

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"})

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"})

// 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."]})

// 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})

// 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})

// 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" }  ]}

{  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 }  ]}

{  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" }  ]}

{  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

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" }  ]}

{  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 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"})

// 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

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.

Source and attribution

Source:ruvnet/rufloin.claude/skills/flow-nexus-neuralat commit60de638

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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Flow Nexus Neural

Flow Nexus Neural Networks

Prerequisites

Core Capabilities

1. Single-Node Neural Training

Example: Train Custom Classifier

Example: LSTM for Time Series

Example: Transformer Architecture

2. Model Inference

3. Template Marketplace

List Available Templates

Deploy Template

4. Distributed Training Clusters

Initialize Cluster

Deploy Worker Nodes

Connect Cluster Topology

Start Distributed Training

Monitor Cluster Status

Run Distributed Inference

Terminate Cluster

5. Model Management

List Your Models

Check Training Status

Performance Benchmarking

Create Validation Workflow

6. Publishing and Marketplace

Publish Model as Template

Rate a Template

Common Use Cases

Image Classification with CNN

NLP Sentiment Analysis

Time Series Forecasting

Federated Learning for Privacy

Architecture Patterns

Feedforward Networks

LSTM Networks

Transformers

GANs

Autoencoders

Best Practices

Troubleshooting

Training Stalled

Low Accuracy

Out of Memory

Related Skills

Resources

Source and attribution

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