Can I tune specific deep learning parameters?

Yes, it is pre-configured to handle common spaces like learning_rate, batch_size, weight_decay, warmup_ratio, and dropout.

How does it help manage compute costs?

The skill includes budget setting features such as maximum runs, total time limits, and early stopping configurations to kill underperforming trials.

Which search strategies does this skill support?

It supports Grid search for exhaustive small-scale tests, Random search for standard optimization, and Bayesian search for expensive runs with smooth objectives.

What kind of analysis does the skill provide after a sweep?

It generates best configuration metrics, parameter importance visualizations, and sensitivity analysis to recommend your next steps.

Hyperparameter Sweep

Name: Hyperparameter Sweep
Author: rHedBull

byrHedBull

Data Science & ML

Systematically optimizes machine learning model performance by searching for the ideal hyperparameter configurations.

About

The hyperparameter-sweep skill automates the rigorous process of finding the best settings for machine learning models, such as learning rates, batch sizes, and weight decay. It guides developers through defining search spaces, selecting search strategies like Grid, Random, or Bayesian, and managing execution budgets with early stopping mechanisms. This skill is essential for ML engineers who need to improve model accuracy and efficiency through structured experimentation and detailed sensitivity analysis.

Key Features

Real-time progress tracking and reporting of the best performing configurations
Support for Grid, Random, and Bayesian search strategies
Post-sweep sensitivity analysis and parameter importance visualization
Built-in budget management with early stopping for resource efficiency
Automated search space definition for learning rate, batch size, and weight decay
0 GitHub stars

Use Cases

Fine-tuning learning rates and dropout ratios for deep learning models
Systematically comparing model performance across different batch sizes
Optimizing regularization parameters to prevent model overfitting

About

Key Features

Real-time progress tracking and reporting of the best performing configurations
Support for Grid, Random, and Bayesian search strategies
Post-sweep sensitivity analysis and parameter importance visualization
Built-in budget management with early stopping for resource efficiency
Automated search space definition for learning rate, batch size, and weight decay
0 GitHub stars

Use Cases

Fine-tuning learning rates and dropout ratios for deep learning models
Systematically comparing model performance across different batch sizes
Optimizing regularization parameters to prevent model overfitting