DEHB 0.1.2

Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization

DEHB: Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization

Welcome to DEHB, an algorithm for Hyperparameter Optimization (HPO). DEHB uses Differential Evolution (DE) under-the-hood as an Evolutionary Algorithm to power the black-box optimization that HPO problems pose.

dehb is a python package implementing the DEHB algorithm. It offers an intuitive interface to optimize user-defined problems using DEHB.

Getting Started

Installation

pip install dehb

Using DEHB

DEHB allows users to either utilize the Ask & Tell interface for manual task distribution or leverage the built-in functionality (run) to set up a Dask cluster autonomously. The following snippet offers a small look in to how to use DEHB. For further information, please refer to our getting started examples in our documentation.

optimizer = DEHB(
    f=your_target_function,
    cs=config_space, 
    dimensions=dimensions, 
    min_fidelity=min_fidelity, 
    max_fidelity=max_fidelity)

##### Using Ask & Tell
# Ask for next configuration to run
job_info = optimizer.ask()

# Run the configuration for the given fidelity. Here you can freely distribute the computation to any worker you'd like.
result = your_target_function(config=job_info["config"], fidelity=job_info["fidelity"])

# When you received the result, feed them back to the optimizer
optimizer.tell(job_info, result)

##### Using run()
# Run optimization for 1 bracket. Output files will be saved to ./logs
traj, runtime, history = optimizer.run(brackets=1)

Running DEHB in a parallel setting

For a more in-depth look in how-to run DEHB in a parallel setting, please have a look at our documentation.

Tutorials/Example notebooks

• 00 - A generic template to use DEHB for multi-fidelity Hyperparameter Optimization
• 01.1 - Using DEHB to optimize 4 hyperparameters of a Scikit-learn's Random Forest on a classification dataset
• 01.2 - Using DEHB to optimize 4 hyperparameters of a Scikit-learn's Random Forest on a classification dataset using Ask & Tell interface
• 02 - Optimizing Scikit-learn's Random Forest without using ConfigSpace to represent the hyperparameter space
• 03 - Hyperparameter Optimization for MNIST in PyTorch

To run PyTorch example: (note additional requirements)

python examples/03_pytorch_mnist_hpo.py \
    --min_fidelity 1 \
    --max_fidelity 3 \
    --runtime 60 \
    --verbose

Documentation

For more details and features, please have a look at our documentation.

Contributing

Any contribution is greaty appreciated! Please take the time to check out our contributing guidelines

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To cite the paper or code

@inproceedings{awad-ijcai21,
  author    = {N. Awad and N. Mallik and F. Hutter},
  title     = {{DEHB}: Evolutionary Hyberband for Scalable, Robust and Efficient Hyperparameter Optimization},
  pages     = {2147--2153},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {ijcai.org},
  editor    = {Z. Zhou},
  year      = {2021}
}