haipipe-nn-2-tuner

Standardize machine learning model tuning interfaces with data conversion, fitting, inference, and serialization.

Updated Feb 16, 2026

Search Tags:#python #mlops #data transformation #serialization #model tuning #algorithm wrapper

Ready to run

npx skills add https://github.com/jluo41/research-skills --skill haipipe-nn-2-tuner

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: haipipe-nn-2-tuner
Source: https://github.com/jluo41/research-skills/tree/main/skills/haipipe-nn-2-tuner
Command: npx skills add https://github.com/jluo41/research-skills --skill haipipe-nn-2-tuner

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a standardized interface for wrapping diverse machine learning algorithms, ensuring consistent data handling, training, inference, and model serialization across different libraries.

Core Features & Use Cases

Algorithm Agnosticism: Wraps any algorithm with a uniform contract, regardless of its underlying library.
Data Transformation: Handles conversion of input data into algorithm-specific formats.
Model Lifecycle Management: Manages training, inference, saving, and loading of models.
Use Case: Integrate a new time-series forecasting algorithm (e.g., from a new research paper) into an existing pipeline by creating a Tuner that adheres to this contract, without modifying the pipeline's orchestration logic.

Quick Start

Implement the get_tfm_data, fit, infer, save_model, and load_model methods within a new class inheriting from ModelTuner.

Frequently Asked Questions about haipipe-nn-2-tuner

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

FAQPage Schema

How do I standardize the interface for machine learning model tuning across different libraries?▼

Standardize the interface for machine learning model tuning by wrapping diverse algorithms with a uniform contract. This ensures consistent data handling, training, inference, and model serialization across different libraries.

Can I integrate a new time-series forecasting algorithm into an existing pipeline without modifying orchestration logic?▼

Integrate a new time-series forecasting algorithm into an existing pipeline by creating a Tuner that adheres to the algorithm wrapper contract. This prevents modifications to the pipeline's orchestration logic.

What methods do I need to implement to build a custom algorithm wrapper for model tuning?▼

Build a custom algorithm wrapper by inheriting from ModelTuner and implementing get_tfm_data, fit, infer, save_model, and load_model methods, alongside a standalone transform_fn for data conversion.

Does this model tuning wrapper handle data transformation and serialization for external libraries?▼

The model tuning wrapper handles data transformation by converting input data into algorithm-specific formats. It also manages model serialization for saving and loading models from external libraries.

Why do I need a standardized contract for managing machine learning model lifecycles?▼

A standardized contract for managing machine learning model lifecycles ensures consistent model fitting, inference, and serialization. It solves data handling discrepancies when using diverse external libraries.

What are the limitations of using a strict contract for wrapping machine learning algorithms?▼

The strict contract for wrapping machine learning algorithms requires adherence to abstract methods and a standalone transform_fn. This demands precise implementation to ensure algorithm agnosticism functions correctly.