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Getting Started#

Getting started with NePS involves a straightforward yet powerful process, centering around its three main components. This approach ensures flexibility and efficiency in evaluating different architecture and hyperparameter configurations for your problem.

NePS requires Python 3.10 or higher. You can install it via pip or from source.

pip install neural-pipeline-search

The 3 Main Components#

  1. Establish a pipeline_space=: 
    pipeline_space={
    "some_parameter": (0.0, 1.0),   # float
    "another_parameter": (0, 10),   # integer
    "optimizer": ["sgd", "adam"],   # categorical
    "epoch": neps.Integer(lower=1, upper=100, is_fidelity=True),
    "learning_rate": neps.Float(lower=1e-5, uperr=1, log=True),
    "alpha": neps.Float(lower=0.1, upper=1.0, prior=0.99, prior_confidence="high")
}
  2. Define an evaluate_pipeline() function:
def evaluate_pipeline(some_parameter: float,
                 another_parameter: float,
                 optimizer: str, epoch: int,
                 learning_rate: float, alpha: float) -> float:
    model = make_model(...)
    loss = eval_model(model)
    return loss
  1. Execute with neps.run():
neps.run(evaluate_pipeline, pipeline_space)

You can find a longer walk through in the reference!

Examples#

Discover the features of NePS through these practical examples: