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hint-mode

Extract hint annotations from code into SPACE_CONFIG and META_INFO for hyperparameter tuning.

258|48|Updated Jun 22, 2020
Search Tags:#automation#code-analysis#tuning#hyperparameter#hint#parameter-space#space_config
One-click install
npx skills add https://github.com/mindspore-ai/akg --skill hint-mode-mindspore-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hint-mode
Source: https://github.com/mindspore-ai/akg/tree/main/akg_agents/python/akg_agents/op/resources/skills/designer/hint-mode
Command: npx skills add https://github.com/mindspore-ai/akg --skill hint-mode-mindspore-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Hint mode converts task descriptions into a structured parameter space (space_config) that can be used for automatic tuning, reducing manual guesswork and speeding up optimization.

Core Features & Use Cases

  • Identifies parameter hints from code comments to build a SPACE_CONFIG dict compatible with tuners.
  • Supports standard and compatibility hint formats, converting them into a unified SPACE_CONFIG and META_INFO.
  • Use Case: When preparing an ML training pipeline, extract hints to generate ready-to-run configuration and input constructors.

Quick Start

Parse a Python source file containing hint annotations to generate SPACE_CONFIG and META_INFO suitable for a hyperparameter tuner.

Frequently Asked Questions about hint-mode

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

FAQPage Schema
How do I convert code comments into a parameter space for hyperparameter tuning?▼

Hint mode annotations are extracted from your source code to generate a machine-readable SPACE_CONFIG dictionary and META_INFO. It supports standard and compatibility hint formats, converting them into unified outputs for automatic tuning.

How do I generate a SPACE_CONFIG from Python source files?▼

You parse a Python source file containing hint annotations to generate SPACE_CONFIG and META_INFO suitable for a hyperparameter tuner. This creates a ready-to-run configuration and input constructors for ML pipelines.

Does hint mode support compatibility hint formats for machine learning pipelines?▼

Yes, it supports both standard and compatibility hint formats for ML pipelines. It converts these annotations into a unified SPACE_CONFIG and META_INFO structure compatible with tuners.

What is the best way to automate hyperparameter tuning using developer-provided hints?▼

The best way is to use hint mode extraction to build a structured SPACE_CONFIG from developer-provided hints. This reduces manual guesswork by converting task descriptions and code comments into a tuner-ready parameter space.

Can I use hint mode for software tasks outside of ML training pipelines?▼

Yes, the extraction applies across typical ML and software tasks where parameter optimization is guided by developer-provided hints. It generates a ready-to-run SPACE_CONFIG dictionary and param_names list for any compatible tuner.

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