JAHS-Bench-201

The first collection of surrogate benchmarks for Joint Architecture and Hyperparameter Search (JAHS), built to also support and facilitate research on multi-objective, cost-aware and (multi) multi-fidelity optimization algorithms.

Please see our documentation here.

Installation

Using pip

pip install git+https://github.com/automl/jahs_bench_201.git

Optionally, you can download the data required to use the surrogate benchmark ahead of time with

python -m jahs_bench.download --target surrogates

To test if the installation was successful, you can, e.g, run a minimal example with

python -m jahs_bench_examples.minimal

This should randomly sample a configuration, and display both the sampled configuration and the result of querying the surrogate for that configuration.

Using the Benchmark

Creating Configurations

Configurations in our Joint Architecture and Hyperparameter (JAHS) space are represented as dictionaries, e.g.,:

config = {
    'Optimizer': 'SGD',
    'LearningRate': 0.1,
    'WeightDecay': 5e-05,
    'Activation': 'Mish',
    'TrivialAugment': False,
    'Op1': 4,
    'Op2': 1,
    'Op3': 2,
    'Op4': 0,
    'Op5': 2,
    'Op6': 1,
    'N': 5,
    'W': 16,
    'Resolution': 1.0,
}

For a full description on the search space and configurations see our documentation.

Evaluating Configurations

import jahs_bench

benchmark = jahs_bench.Benchmark(task="cifar10", download=True)

# Query a random configuration
config = benchmark.sample_config()
results = benchmark(config, nepochs=200)

# Display the outputs
print(f"Config: {config}")  # A dict
print(f"Result: {results}")  # A dict

More Evaluation Options

The API of our benchmark enables users to either query a surrogate model (the default) or the tables of performance data, or train a configuration from our search space from scratch using the same pipeline as was used by our benchmark. However, users should note that the latter functionality requires the installation of jahs_bench_201 with the optional data_creation component and its relevant dependencies. The relevant data can be automatically downloaded by our API. See our documentation for details.

Benchmark Data

We provide documentation for the performance dataset used to train our surrogate models and further information on our surrogate models.

Experiments and Evaluation Protocol

See our experiments repository and our documentation.

Leaderboards

We maintain leaderboards for several optimization tasks and algorithmic frameworks.