when-training-neural-networks-use-flow-nexus-neural

What this skill does

# Flow Nexus Neural Network Training SOP

```yaml
metadata:
  skill_name: when-training-neural-networks-use-flow-nexus-neural
  version: 1.0.0
  category: platform-integration
  difficulty: advanced
  estimated_duration: 45-90 minutes
  trigger_patterns:
    - "train neural network"
    - "machine learning model"
    - "distributed training"
    - "flow nexus neural"
    - "E2B sandbox training"
  dependencies:
    - flow-nexus MCP server
    - E2B account (optional for cloud)
    - Claude Flow hooks
  agents:
    - ml-developer (primary model architect)
    - flow-nexus-neural (platform coordinator)
    - cicd-engineer (deployment specialist)
  success_criteria:
    - Model training completes successfully
    - Validation accuracy meets requirements (>85%)
    - Performance benchmarks within thresholds
    - Cloud deployment verified
    - Documentation generated
```

## Overview

This SOP provides a systematic workflow for training and deploying neural networks using Flow Nexus platform with distributed E2B sandboxes. It covers architecture selection, distributed training, validation, and production deployment.

## Prerequisites

**Required:**
- Flow Nexus MCP server installed
- Basic understanding of neural network architectures
- Authentication credentials (if using cloud features)

**Optional:**
- E2B account for cloud sandboxes
- GPU resources for training
- Pre-trained model weights

**Verification:**
```bash
# Check Flow Nexus availability
npx flow-nexus@latest --version

# Verify MCP connection
claude mcp list | grep flow-nexus
```

## Agent Responsibilities

### ml-developer (Primary Model Architect)
**Role:** Design neural network architecture, select hyperparameters, optimize model performance

**Expertise:**
- Neural network architectures (Transformer, CNN, RNN, GAN, etc.)
- Training optimization and hyperparameter tuning
- Model evaluation and validation strategies
- Transfer learning and fine-tuning

**Output:** Model architecture design, training configuration, performance analysis

### flow-nexus-neural (Platform Coordinator)
**Role:** Coordinate distributed training across cloud infrastructure, manage resources

**Expertise:**
- Flow Nexus platform APIs and capabilities
- Distributed training coordination
- E2B sandbox management
- Resource optimization

**Output:** Training orchestration, resource allocation, deployment configuration

### cicd-engineer (Deployment Specialist)
**Role:** Deploy trained models to production, setup monitoring and scaling

**Expertise:**
- Model serving infrastructure
- Docker containerization
- CI/CD pipelines
- Monitoring and observability

**Output:** Deployment scripts, monitoring dashboards, production configuration

## Phase 1: Setup Flow Nexus

**Objective:** Authenticate with Flow Nexus platform and initialize neural training environment

**Evidence-Based Validation:**
- Authentication token obtained and verified
- MCP tools responding correctly
- Training environment initialized

**ml-developer Actions:**
```bash
# Pre-task coordination hook
npx claude-flow@alpha hooks pre-task --description "Setup Flow Nexus for neural training"

# Restore session context
npx claude-flow@alpha hooks session-restore --session-id "neural-training-$(date +%s)"
```

**flow-nexus-neural Actions:**
```bash
# Check authentication status
mcp__flow-nexus__auth_status { "detailed": true }

# If not authenticated, register/login
# mcp__flow-nexus__user_register { "email": "[email protected]", "password": "secure_pass" }
# mcp__flow-nexus__user_login { "email": "[email protected]", "password": "secure_pass" }

# Initialize neural training cluster
mcp__flow-nexus__neural_cluster_init {
  "name": "neural-training-cluster",
  "architecture": "transformer",
  "topology": "mesh",
  "daaEnabled": true,
  "wasmOptimization": true,
  "consensus": "proof-of-learning"
}

# Store cluster ID in memory
npx claude-flow@alpha memory store --key "neural/cluster-id" --value "[cluster_id]"
```

**cicd-engineer Actions:**
```bash
# Prepare deployment environment
mkdir -p neural/{models,configs,scripts,tests}

# Initialize configuration
cat > neural/configs/training.json << 'EOF'
{
  "cluster": {
    "topology": "mesh",
    "maxNodes": 8,
    "autoScale": true
  },
  "training": {
    "batchSize": 32,
    "epochs": 100,
    "learningRate": 0.001,
    "optimizer": "adam"
  },
  "validation": {
    "splitRatio": 0.2,
    "minAccuracy": 0.85
  }
}
EOF

# Post-edit hook
npx claude-flow@alpha hooks post-edit --file "neural/configs/training.json" --memory-key "neural/config"
```

**Success Criteria:**
- [ ] Flow Nexus authenticated successfully
- [ ] Neural cluster initialized
- [ ] Configuration files created
- [ ] Memory context established

**Memory Persistence:**
```bash
# Store phase completion
npx claude-flow@alpha memory store \
  --key "neural/phase1-complete" \
  --value "{\"status\": \"complete\", \"cluster_id\": \"[id]\", \"timestamp\": \"$(date -Iseconds)\"}"
```

## Phase 2: Configure Neural Network

**Objective:** Design network architecture, select hyperparameters, prepare training configuration

**Evidence-Based Validation:**
- Architecture validated against task requirements
- Hyperparameters optimized for dataset
- Configuration tested with sample data

**ml-developer Actions:**
```bash
# Retrieve cluster information
CLUSTER_ID=$(npx claude-flow@alpha memory retrieve --key "neural/cluster-id" | jq -r '.value')

# List available templates for reference
mcp__flow-nexus__neural_list_templates {
  "category": "classification",
  "limit": 10
}

# Design custom architecture
cat > neural/configs/architecture.json << 'EOF'
{
  "type": "transformer",
  "layers": [
    {
      "type": "embedding",
      "inputDim": 10000,
      "outputDim": 512
    },
    {
      "type": "transformer-encoder",
      "numHeads": 8,
      "dimModel": 512,
      "dimFeedforward": 2048,
      "numLayers": 6,
      "dropout": 0.1
    },
    {
      "type": "dense",
      "units": 256,
      "activation": "relu"
    },
    {
      "type": "dropout",
      "rate": 0.3
    },
    {
      "type": "dense",
      "units": 10,
      "activation": "softmax"
    }
  ],
  "optimizer": {
    "type": "adam",
    "learningRate": 0.001,
    "beta1": 0.9,
    "beta2": 0.999
  },
  "loss": "categorical_crossentropy",
  "metrics": ["accuracy", "precision", "recall"]
}
EOF

# Post-edit hook
npx claude-flow@alpha hooks post-edit --file "neural/configs/architecture.json" --memory-key "neural/architecture"

# Notify coordination
npx claude-flow@alpha hooks notify --message "Neural architecture configured: Transformer with 6 encoder layers"
```

**flow-nexus-neural Actions:**
```bash
# Deploy neural nodes to cluster
mcp__flow-nexus__neural_node_deploy {
  "cluster_id": "$CLUSTER_ID",
  "node_type": "worker",
  "model": "large",
  "template": "nodejs",
  "autonomy": 0.8,
  "capabilities": ["training", "inference", "validation"]
}

# Deploy parameter server
mcp__flow-nexus__neural_node_deploy {
  "cluster_id": "$CLUSTER_ID",
  "node_type": "parameter_server",
  "model": "xl",
  "template": "nodejs",
  "autonomy": 0.9,
  "capabilities": ["parameter_sync", "gradient_aggregation"]
}

# Deploy validator nodes
for i in {1..2}; do
  mcp__flow-nexus__neural_node_deploy {
    "cluster_id": "$CLUSTER_ID",
    "node_type": "validator",
    "model": "base",
    "template": "nodejs",
    "autonomy": 0.7,
    "capabilities": ["validation", "benchmarking"]
  }
done

# Connect nodes based on mesh topology
mcp__flow-nexus__neural_cluster_connect {
  "cluster_id": "$CLUSTER_ID",
  "topology": "mesh"
}

# Store node information
npx claude-flow@alpha memory store --key "neural/nodes-deployed" --value "4"
```

**cicd-engineer Actions:**
```bash
# Create training script
cat > neural/scripts/train.py << 'EOF'
#!/usr/bin/env python3
import json
import sys
from datetime import datetime

def load_config(path):
    with open(path, 'r') as f:
        return json.load(f)

def prepare_dataset(config):
    # Data