flow-nexus-neural

# 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 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:**