Cloud Neural Network

Train, deploy, and manage neural networks at scale using Flow Nexus cloud-powered distributed computing.

Quick Start

// Train a basic neural network mcp__flow-nexus__neural_train({ config: { architecture: { type: "feedforward", layers: [ { type: "dense", units: 128, activation: "relu" }, { type: "dropout", rate: 0.2 }, { type: "dense", units: 10, activation: "softmax" } ] }, training: { epochs: 100, batch_size: 32, learning_rate: 0.001 } }, tier: "small" })

// Run inference mcp__flow-nexus__neural_predict({ model_id: "trained_model_id", input: [[0.5, 0.3, 0.2]] })

When to Use

• Training neural networks for classification, regression, or generation tasks

• Deploying distributed training across multiple cloud sandboxes

• Running model inference on trained models

• Managing model lifecycle from training to production deployment

• Implementing federated learning or ensemble methods

• Fine-tuning pre-trained models for specific domains

Prerequisites

• Flow Nexus account with active session

• MCP server flow-nexus configured

• Sufficient rUv credits for training tier selected

Core Concepts

Neural Architectures

Type Use Case

Feedforward Classification, regression

LSTM/RNN Time series, NLP sequences

Transformer Advanced NLP, multimodal

CNN Computer vision, image processing

GAN Data generation, augmentation

Autoencoder Dimensionality reduction, anomaly detection

Training Tiers

Tier Resources Cost

nano

Minimal, quick tests Low

mini

Small models Low

small

Standard training Medium

medium

Large models High

large

Production scale Highest

Distributed Consensus Protocols

• proof-of-learning: Training contribution verification

• byzantine: Fault-tolerant distributed consensus

• raft: Leader-based coordination

• gossip: Decentralized information propagation

MCP Tools Reference

Single-Node Training

mcp__flow-nexus__neural_train({ config: { architecture: { type: "feedforward", // lstm, gan, autoencoder, transformer layers: [ { type: "dense", units: 128, activation: "relu" }, { type: "dropout", rate: 0.2 }, { type: "dense", units: 10, activation: "softmax" } ] }, training: { epochs: 100, batch_size: 32, learning_rate: 0.001, optimizer: "adam" }, divergent: { enabled: false, pattern: "lateral", // quantum, chaotic, associative, evolutionary factor: 0.1 } }, tier: "small", // nano, mini, small, medium, large user_id: "user_id" })

Distributed Cluster Training

// Initialize distributed cluster mcp__flow-nexus__neural_cluster_init({ name: "training-cluster", architecture: "transformer", // transformer, cnn, rnn, gnn, hybrid topology: "mesh", // mesh, ring, star, hierarchical consensus: "proof-of-learning", daaEnabled: true, wasmOptimization: true })

// Deploy worker nodes mcp__flow-nexus__neural_node_deploy({ cluster_id: "cluster_id", node_type: "worker", // worker, parameter_server, aggregator, validator model: "base", // base, large, xl, custom capabilities: ["training", "inference"], autonomy: 0.8 })

// Connect nodes based on topology mcp__flow-nexus__neural_cluster_connect({ cluster_id: "cluster_id", topology: "mesh" })

// Start distributed training mcp__flow-nexus__neural_train_distributed({ cluster_id: "cluster_id", dataset: "dataset_id", epochs: 10, batch_size: 32, learning_rate: 0.001, optimizer: "adam", federated: false })

// Check cluster status mcp__flow-nexus__neural_cluster_status({ cluster_id: "cluster_id" })

// Terminate when done mcp__flow-nexus__neural_cluster_terminate({ cluster_id: "cluster_id" })

Inference

// Single-node inference mcp__flow-nexus__neural_predict({ model_id: "model_id", input: [[0.5, 0.3, 0.2]], user_id: "user_id" })

// Distributed inference mcp__flow-nexus__neural_predict_distributed({ cluster_id: "cluster_id", input_data: "[0.5, 0.3, 0.2]", aggregation: "mean" // mean, majority, weighted, ensemble })

Template Management

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

// Deploy template mcp__flow-nexus__neural_deploy_template({ template_id: "template_id", custom_config: { epochs: 50 }, user_id: "user_id" })

// Publish your model as template mcp__flow-nexus__neural_publish_template({ model_id: "model_id", name: "Sentiment Analyzer", description: "LSTM-based sentiment analysis model", category: "nlp", price: 0, user_id: "user_id" })

// Rate a template mcp__flow-nexus__neural_rate_template({ template_id: "template_id", rating: 5, review: "Excellent model, fast and accurate", user_id: "user_id" })

Model Management

// List user models mcp__flow-nexus__neural_list_models({ user_id: "user_id", include_public: false })

// Check training status mcp__flow-nexus__neural_training_status({ job_id: "job_id" })

// Create validation workflow mcp__flow-nexus__neural_validation_workflow({ model_id: "model_id", validation_type: "comprehensive", // performance, accuracy, robustness, comprehensive user_id: "user_id" })

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

Usage Examples

Example 1: Classification Model Training

// Train a feedforward classifier const trainingJob = await mcp__flow-nexus__neural_train({ config: { architecture: { type: "feedforward", layers: [ { type: "dense", units: 256, activation: "relu" }, { type: "batch_norm" }, { type: "dropout", rate: 0.3 }, { type: "dense", units: 128, activation: "relu" }, { type: "dropout", rate: 0.2 }, { type: "dense", units: 10, activation: "softmax" } ] }, training: { epochs: 100, batch_size: 64, learning_rate: 0.001, optimizer: "adam" } }, tier: "small" });

// Monitor training const status = await mcp__flow-nexus__neural_training_status({ job_id: trainingJob.job_id });

console.log(Epoch: ${status.current_epoch}, Loss: ${status.loss});

// Run inference on trained model const prediction = await mcp__flow-nexus__neural_predict({ model_id: trainingJob.model_id, input: [[0.1, 0.2, 0.3, 0.4, 0.5]] });

Example 2: Distributed Transformer Training

// Initialize distributed cluster const cluster = await mcp__flow-nexus__neural_cluster_init({ name: "transformer-cluster", architecture: "transformer", topology: "mesh", consensus: "proof-of-learning", daaEnabled: true, wasmOptimization: true });

// Deploy 4 worker nodes for (let i = 0; i < 4; i++) { await mcp__flow-nexus__neural_node_deploy({ cluster_id: cluster.cluster_id, node_type: "worker", model: "large", capabilities: ["training", "inference"] }); }

// Deploy parameter server await mcp__flow-nexus__neural_node_deploy({ cluster_id: cluster.cluster_id, node_type: "parameter_server", model: "base" });

// Connect nodes await mcp__flow-nexus__neural_cluster_connect({ cluster_id: cluster.cluster_id });

// Start distributed training await mcp__flow-nexus__neural_train_distributed({ cluster_id: cluster.cluster_id, dataset: "large_nlp_dataset", epochs: 50, batch_size: 128, learning_rate: 0.0001, optimizer: "adam" });

// Monitor and validate const clusterStatus = await mcp__flow-nexus__neural_cluster_status({ cluster_id: cluster.cluster_id });

// Cleanup await mcp__flow-nexus__neural_cluster_terminate({ cluster_id: cluster.cluster_id });

Example 3: Using Pre-built Templates

// Find NLP templates const templates = await mcp__flow-nexus__neural_list_templates({ category: "nlp", tier: "free", search: "sentiment" });

// Deploy the best-rated template const deployment = await mcp__flow-nexus__neural_deploy_template({ template_id: templates.templates[0].id, custom_config: { epochs: 25, learning_rate: 0.0005 } });

// Validate model performance await mcp__flow-nexus__neural_validation_workflow({ model_id: deployment.model_id, validation_type: "comprehensive" });

// Benchmark performance const benchmark = await mcp__flow-nexus__neural_performance_benchmark({ model_id: deployment.model_id, benchmark_type: "comprehensive" });

console.log(Inference latency: ${benchmark.inference_latency_ms}ms);

Execution Checklist

• Design neural architecture for task requirements

• Select appropriate training tier based on model size

• Configure training hyperparameters

• Initialize training (single or distributed)

• Monitor training progress and metrics

• Validate model performance

• Run benchmarks for production readiness

• Deploy for inference or publish as template

• Cleanup cluster resources when complete

Best Practices

• Start Small: Begin with nano or mini tier for testing, scale up for production

• Proper Validation: Always run validation workflow before production deployment

• Hyperparameter Tuning: Use grid search or Bayesian optimization for best results

• Distributed Training: Use for large models; single-node for smaller experiments

• Checkpoint Frequently: Enable checkpointing for long training runs

• Monitor Drift: Implement drift detection for production models

Error Handling

Error Cause Solution

training_failed

Invalid architecture config Verify layer compatibility and types

cluster_init_failed

Invalid topology or architecture Check supported combinations

insufficient_credits

Training tier exceeds balance Reduce tier or add credits

model_not_found

Invalid model_id Use neural_list_models to verify

node_deploy_failed

Cluster capacity reached Terminate unused nodes

Metrics & Success Criteria

• Training Convergence: Loss decreasing over epochs

• Validation Accuracy: Target >90% for classification

• Inference Latency: <100ms for production

• Memory Efficiency: <80% resource utilization

• Model Size: Appropriate for deployment target

Integration Points

With Swarms

// Deploy neural agent in swarm await mcp__flow-nexus__agent_spawn({ type: "analyst", name: "ML Analyst", capabilities: ["neural_training", "model_evaluation"] });

With Workflows

// ML pipeline workflow await mcp__flow-nexus__workflow_create({ name: "ML Training Pipeline", steps: [ { id: "preprocess", action: "data_prep" }, { id: "train", action: "neural_train", depends: ["preprocess"] }, { id: "validate", action: "neural_validate", depends: ["train"] }, { id: "deploy", action: "neural_deploy", depends: ["validate"] } ] });

Related Skills

• cloud-swarm - Multi-agent orchestration

• cloud-sandbox - Isolated execution environments

• cloud-workflow - Workflow automation

References

• Flow Nexus Neural Documentation

• Distributed Training Best Practices

Version History

• 1.0.0 (2026-01-02): Initial release - converted from flow-nexus-neural agent