zxh0916

zxh0916 / auto-hparam-tuning

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Automatically tune hyperparameters with OpenClaw

37
0
100% credibility
Found Mar 18, 2026 at 37 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An AI agent skill that automates hyperparameter tuning for machine learning projects by reading code, planning experiments, running tests asynchronously, analyzing results, and generating reports.

How It Works

1
🔍 Discover Smart Tuner

You hear about a helpful tool that lets an AI assistant automatically fine-tune settings in your machine learning project while you take a break.

2
📦 Add to AI Helper

You easily add this tuning ability to your AI assistant's collection of skills.

3
🗣️ Give Instructions

You simply tell your AI helper which project to tune and where to run the tests, like on your computer or a remote machine.

4
📖 AI Studies Project

Your assistant reads through your project, understands the key adjustable parts, and creates a smart plan for improvements.

5
🔄 Automatic Tests

It launches test runs one by one, checks the outcomes, and tweaks settings based on what works best.

6
📈 Learns from Results

With each test, it spots patterns like slowdowns or peaks, refines its strategy, and keeps going without your input.

🎉 Perfect Settings Ready

You get a clear report of the best combination, your model performs great, and you've saved tons of manual trial-and-error time.

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AI-Generated Review

What is auto-hparam-tuning?

Auto-hparam-tuning is a Python skill for OpenClaw that automates hyperparameter tuning—like automatically tuning PID controllers or towards automatically tuned deep neural networks—for Hydra-based deep learning projects. You tell an agent your project path and training command; it reads the codebase and configs, plans a strategy, launches async experiments via tmux (local or SSH), analyzes TensorBoard events, and iterates based on history until optimized. No more manual grid searches or babysitting overnight runs.

Why is it gaining traction?

Unlike black-box tools like Optuna, it brings researcher intuition: the agent inspects your model architecture and Hydra knobs to prioritize sensible ranges, avoiding blind sampling. Features like self-polling with cron reminders, structured reports comparing runs, and low-intrusion workflow (just add "- override" to configs) make it dead simple for iterative tuning. Developers dig the autonomy—set it and forget it while grabbing coffee.

Who should use this?

Deep learning researchers grinding Hydra projects on remote clusters, especially those tweaking models for tasks like automatically tuned model predictive control or neural nets where manual overrides kill productivity. Ideal for teams auto-syncing forks on GitHub or handling stale branches, wanting agent-driven tuning without rewriting pipelines.

Verdict

Promising niche tool for Hydra users, with clear quickstart and solid docs, but immature at 37 stars and 1.0% credibility—TODOs hint at broader platform support soon. Try it if you're in the ecosystem; skip for general auto-tuning needs.

(178 words)

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