README.md

Hyperparameter Optimization for Deep Learning (HPO4DL)

HPO4DL is a framework for multi-fidelity (gray-box) hyperparameter optimization. The core optimizer in HPO4DL is DyHPO, a novel Bayesian Optimization approach to hyperparameter optimization tailored for deep learning. DyHPO dynamically determines the best hyperparameter configurations to train further by using a deep kernel for Gaussian Processes that captures the details of the learning curve and an acquisition function that incorporates multi-budget information.

Installation

To install the package:

pip install hpo4dl

Getting started

Following is a simple example to get you started:

from typing import List, Dict, Union
from hpo4dl.tuner import Tuner
from ConfigSpace import ConfigurationSpace


def objective_function(
    configuration: Dict,
    epoch: int,
    previous_epoch: int,
    checkpoint_path: str
) -> Union[Dict, List[Dict]]:
    x = configuration["x"]
    evaluated_info = [
        {'epoch': i, 'metric': (x - 2) ** 2}
        for i in range(previous_epoch + 1, epoch + 1)
    ]
    return evaluated_info


configspace = ConfigurationSpace({"x": (-5.0, 10.0)})

tuner = Tuner(
    objective_function=objective_function,
    configuration_space=configspace,
    minimize=True,
    max_budget=1000,
    optimizer='dyhpo',
    seed=0,
    max_epochs=27,
    num_configurations=1000,
    output_path='hpo4dl_results',
)

incumbent = tuner.run()

Key Parameters Explained:

• objective_function: The function you aim to optimize.

• configuration_space: The hyperparameter configuration space over which the optimization is performed.

• minimize: Boolean flag indicates whether the objective function should be minimized (True) or maximized (False).

• max_budget: The cumulative number of epochs the tuner will evaluate. This budget gets distributed across various hyperparameter configurations.

• optimizer: Specifies the optimization technique employed.

• seed: Random seed for reproducibility.

• max_epochs: Maximum number of epochs a single configuration is evaluated.

• num_configurations: Determines the number of configurations DyHPO reviews before selecting the next one for evaluation. Essentially, it guides the balance between exploration and exploitation in the optimization process.

• output_path: Designates the location to save the results and the checkpoint for the best hyperparameter optimization.

Objective function

def objective_function(
    configuration: Dict,
    epoch: int,
    previous_epoch: int,
    checkpoint_path: str
) -> Union[Dict, List[Dict]]

The objective function is tailored to support interrupted and resumed training processes. Specifically, it should continue training from a previous_epoch to the designated epoch.

The function should return a dictionary or a list of dictionaries upon completion. Every dictionary must include the epoch and metric keys. Here's a sample return value:

{
    “epoch”: 5,
    “metric”: 0.76
}

For optimal performance with DyHPO, ensure the metric is normalized.

Lastly, the checkpoint_path is allocated for saving any intermediate files produced during training pertinent to the current configuration. It facilitates storing models, logs, and other relevant data, ensuring that training can resume seamlessly.

Detailed Examples

For a detailed exploration of the HPO4DL framework, we've provided an in-depth example under: examples/timm_main.py

To execute the provided example, use the following command:

python examples/timm_main.py 
    --dataset torch/cifar100 
    --train-split train 
    --val-split validation 
    --optimizer dyhpo  
    --output-dir ./hpo4dl_results

Citation

@inproceedings{
wistuba2022supervising,
title={Supervising the Multi-Fidelity Race of Hyperparameter Configurations},
author={Martin Wistuba and Arlind Kadra and Josif Grabocka},
booktitle={Thirty-Sixth Conference on Neural Information Processing Systems},
year={2022},
url={https://openreview.net/forum?id=0Fe7bAWmJr}
}