hyperoptax 0.1.6

Tuning hyperparameters with JAX

Hyperoptax: Parallel hyperparameter tuning with JAX

⛰️ Introduction

Hyperoptax is a lightweight toolbox for parallel hyperparameter optimization of pure JAX functions. It provides a concise API that lets you wrap any JAX-compatible loss or evaluation function and search across spaces in parallel – all while staying in pure JAX.

🏗️ Installation

pip install hyperoptax

If you want to use the notebooks:

pip install hyperoptax[notebooks]

If you do not yet have JAX installed, pick the right wheel for your accelerator:

# CPU-only
pip install --upgrade "jax[cpu]"
# or GPU/TPU – see the official JAX installation guide

🥜 In a nutshell

Hyperoptax offers a simple API to wrap pure JAX functions for hyperparameter search and making use of parallelization (vmap only currently). See the notebooks for more examples.

from hyperoptax.bayesian import BayesianOptimizer
from hyperoptax.spaces import LogSpace, LinearSpace

@jax.jit
def train_nn(learning_rate, final_lr_pct):
    ...
    return val_loss

search_space = {"learning_rate": LogSpace(1e-5,1e-1, 100),
                "final_lr_pct": LinearSpace(0.01, 0.5, 100)}

search = BayesianOptimizer(search_space, train_nn)
best_params = search.optimize(n_iterations=100, 
                              n_parallel=10, 
                              maximize=False,
                              )

🔪 The Sharp Bits

Since we are working in pure JAX the same sharp bits apply. Some consequences of this for hyperoptax:

1. Parameters that change the length of an evaluation (e.g: epochs, generations...) can't be optimized in parallel.
2. Neural network structures can't be optimized in parallel either.
3. Strings can't be used as hyperparameters.

🫂 Contributing

We welcome pull requests! To get started:

1. Open an issue describing the bug or feature.
2. Fork the repository and create a feature branch (git checkout -b my-feature).
3. Install dependencies:

pip install -e ".[all]"

4. Run the test suite:

python -m unittest discover -s tests

5. Ensure the notebooks still work.
6. Format your code with ruff.
7. Submit a pull request.

Roadmap

I'm developing this both as a passion project and for my work in my PhD. I have a few ideas on where to go with this libary:

• Sample hyperparameter configurations on the fly rather than generate a huge grid at initialisation.
• Switch domain type from a list of arrays to a PyTree.
• Callbacks!
• Inspired by wandb's sweeps, use a linear grid for all parameters and apply transformations at sample time.
• We are currently redoing the kernel calculation at each iteration when only the last row/column is actually needed. JAX requires sizes to be constant, so we need to do something clever...
• Documentation!
• Need to find a way to share the GP across workers on pmap for Bayesian.
• length scale tuning of kernel tuned during optimization (as done in other implementations).

📝 Citation

If you use Hyperoptax in academic work, please cite:

@misc{hyperoptax2025,
  author = {Theo Wolf},
  title = {{Hyperoptax}: Parallel hyperparameter tuning with JAX},
  year = {2025},
  url = {https://github.com/TheodoreWolf/hyperoptax}
}