jahs-bench-201 0.1.0
The first collection of surrogate benchmarks for Joint Architecture and Hyperparameter Search.
License: MIT
Requires Python: >=3.7.1,<=3.10
Keywords: Joint Architecture and Hyperparameter Search,Neural Architecture Search,Hyperparameter Optimization,Benchmark,Deep Learning

# 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.


![Python versions](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-informational)
[![License](https://img.shields.io/badge/license-MIT-informational)](LICENSE)

Please see our [documentation here](https://automl.github.io/jahs_bench_201/).


## Installation

Using pip

```bash
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
```bash
python -m jahs_bench.download --target surrogates
```

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

```python
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](https://automl.github.io/jahs_bench_201/search_space).


### Evaluating Configurations

```python
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](https://automl.github.io/jahs_bench_201/usage) for details.

## Benchmark Data

We provide [documentation for the performance dataset](https://automl.github.io/jahs_bench_201/performance_dataset) used to train our surrogate models and [further information on our surrogate models](https://automl.github.io/jahs_bench_201/surrogate).


## Experiments and Evaluation Protocol

See [our experiments repository](https://github.com/automl/jahs_bench_201_experiments) and our [documentation](https://automl.github.io/jahs_bench_201/evaluation_protocol).

## Leaderboards

We maintain [leaderboards](https://automl.github.io/jahs_bench_201/leaderboards) for several optimization tasks and algorithmic frameworks.