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Hyperparameter Tuning Kit

$29

Optuna/Ray Tune configs, search space definitions, pruning strategies, and distributed tuning setups.

📁 8 files🏷 v1.0.0
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📁 File Structure 8 files

hyperparameter-tuning-kit/ ├── LICENSE ├── README.md ├── config.example.yaml ├── docs/ │ ├── checklists/ │ │ └── pre-deployment.md │ ├── overview.md │ └── patterns/ │ └── pattern-01-multi-stage-tuning.md └── templates/ └── config.yaml

📖 Documentation Preview README excerpt

Hyperparameter Tuning Kit

Production-ready hyperparameter optimization configs using Optuna and Ray Tune. Includes search space definitions, pruning strategies, distributed tuning setups, and integration with experiment tracking.

What's Included

  • Optuna study configurations with various samplers
  • Ray Tune distributed hyperparameter search setups
  • Search space definition templates for common model types
  • Pruning strategy configs (MedianPruner, HyperbandPruner)
  • Distributed tuning on multi-node clusters
  • Integration with MLflow and Weights & Biases
  • Results analysis and visualization templates

Quick Start


# 1. Copy the example config
cp config.example.yaml config.yaml

# 2. Install dependencies
pip install optuna ray[tune]

# 3. Run the example tuning study
python -m tuning.run --config config.yaml

Prerequisites

  • Python 3.9+
  • Optuna 3.x or Ray Tune 2.x
  • (Optional) Distributed compute cluster for parallel trials

Contents


hyperparameter-tuning-kit/
  config.example.yaml
  docs/
    overview.md
    patterns/
      pattern-01-*.md
    checklists/
      pre-deployment.md
  templates/
    config.yaml

Support

For questions or issues, contact: megafolder122122@hotmail.com

License

MIT License - Copyright 2026 Jesse Mikkola. See LICENSE for details.

📄 Code Sample .yaml preview

config.example.yaml # Hyperparameter Tuning Kit - Example Configuration # Copy this file to config.yaml and update values for your environment tuning: framework: "optuna" # optuna or ray_tune n_trials: 50 timeout_seconds: 3600 direction: "maximize" # maximize or minimize metric: "val_accuracy" search_space: learning_rate: type: "log_uniform" low: 1e-5 high: 1e-1 batch_size: type: "categorical" choices: [16, 32, 64, 128] n_layers: type: "int" low: 1 high: 5 dropout: type: "uniform" low: 0.1 high: 0.5 pruning: enabled: true strategy: "median" # median, hyperband, percentile n_warmup_steps: 5 sampler: "tpe" # tpe, random, cmaes distributed: enabled: false n_workers: 4 backend: "optuna" # optuna (with storage) or ray logging: level: "INFO"
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