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gnn-ml-integration

Extracts GNN features and trains/evaluates ML models across multiple frameworks.

30|3|Updated Apr 2, 2023
Search Tags:#pipeline#framework-detection#model-training#machine-learning#feature-extraction#ml-integration#gnn
One-click install
npx skills add https://github.com/ActiveInferenceInstitute/GeneralizedNotationNotation --skill gnn-ml-integration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gnn-ml-integration
Source: https://github.com/ActiveInferenceInstitute/GeneralizedNotationNotation/tree/main/src/ml_integration
Command: npx skills add https://github.com/ActiveInferenceInstitute/GeneralizedNotationNotation --skill gnn-ml-integration

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Integrates machine learning with GNN pipelines by extracting features from GNN models, orchestrating ML model training, and verifying framework availability to streamline end-to-end experiments.

Core Features & Use Cases

  • Feature extraction: derives real GNN features from model markdown files to feed ML processes.
  • Model training & evaluation: trains and evaluates ML models across multiple frameworks (scikit-learn, PyTorch, TensorFlow, JAX) and reports performance.
  • Framework availability checks: detects installed ML frameworks and versions to guide pipeline execution.
  • Use Case: In Active Inference workflows, ML integration wires GNN outputs into training, evaluation, and deployment tasks.

Quick Start

Run the ML integration step to process GNN files and generate ML-ready outputs.

Frequently Asked Questions about gnn-ml-integration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract features from GNN models for machine learning training?▼

To extract features from GNN models for machine learning training, you can automate the derivation of real GNN features from model markdown files to generate ML-ready datasets for downstream pipelines.

Can I train ML models across multiple frameworks like scikit-learn, PyTorch, and TensorFlow?▼

Yes, you can train and evaluate ML models across multiple frameworks like scikit-learn, PyTorch, TensorFlow, and JAX, with the pipeline reporting performance results and outputting artifacts to a defined directory.

What is the best way to automate ML integration with GNN pipelines?▼

The best way to automate ML integration with GNN pipelines is by orchestrating end-to-end experiments that check framework availability, extract GNN features, and train models to streamline Active Inference research workflows.

Does the pipeline check for installed ML frameworks and versions before training?▼

Yes, the pipeline checks for installed ML frameworks and detects their versions before execution, ensuring framework availability is verified to guide the training pipeline and prevent environment errors.

How do I convert GNN specifications into ML-ready datasets?▼

You convert GNN specifications into ML-ready datasets by processing model markdown files to extract real GNN features, which are then wired directly into machine learning training and evaluation tasks.

Why do I need framework detection for GNN-derived data training?▼

Framework detection for GNN-derived data training is needed to identify installed ML frameworks and versions, guiding pipeline execution so that training and evaluation run successfully without missing dependency errors.

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