gnn-ml-integration

name: gnn-ml-integration description: GNN machine learning integration and model training. Use when training ML models on GNN data, checking ML framework availability, or integrating GNN pipeline outputs with machine learning workflows.

GNN Machine Learning Integration (Step 14)

Purpose

Integrates machine learning capabilities with the GNN pipeline, enabling feature extraction from parsed models, ML model training on GNN data, and framework availability checking.

Key Commands

# Run ML integration
python src/14_ml_integration.py --target-dir input/gnn_files --output-dir output --verbose

# As part of pipeline
python src/main.py --only-steps 14 --verbose

API

from ml_integration import process_ml_integration, check_ml_frameworks

# Process ML integration step (used by pipeline)
result = process_ml_integration(target_dir, output_dir, verbose=True)

# Check available ML frameworks
frameworks = check_ml_frameworks()
# Returns: {'pytorch': {'available': True, 'version': '2.x'}, 
#           'tensorflow': {'available': False},
#           'jax': {'available': True, 'version': '0.4.x'},
#           'sklearn': {'available': True, 'version': '1.x'}}

Key Exports

• process_ml_integration — main pipeline processing function
• check_ml_frameworks — checks availability of PyTorch, TensorFlow, JAX, scikit-learn

Supported ML Frameworks

Dependencies

# ML framework deps
uv sync --extra ml-ai

# Includes: torch, transformers, datasets

Output

• ML results in output/14_ml_integration_output/
• Framework availability reports

MCP Tools

This module registers tools with the GNN MCP server (see mcp.py):

• check_ml_frameworks
• get_ml_module_info
• list_ml_integration_targets
• process_ml_integration

References

• AGENTS.md — Module documentation
• README.md — Usage guide
• SPEC.md — Module specification

Documentation

• README: Module Overview
• AGENTS: Agentic Workflows
• SPEC: Architectural Specification
• SKILL: Capability API