sweepexp.sweepexp.sweepexp

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sweepexp.sweepexp.sweepexp#

sweepexp.sweepexp.sweepexp(func: Callable, parameters: dict[str, list], mode: Literal['sequential', 'parallel', 'mpi'] = 'sequential', save_path: Path | str | None = None, **kwargs: dict) → se.SweepExp | se.SweepExpMPI | se.SweepExpParallel[source]#

Create a new instance of the SweepExp class.

Parameters#

funcCallable

The experiment function to run. The function should take the parameters as keyword arguments and return a dictionary with the return values.

parametersdict[str, list]

The parameters to sweep over. The keys are the parameter names and the values are lists of the parameter values.

mode“sequential” | “parallel” | “mpi”, default=”sequential”

The mode to run the experiments in.

save_pathPath | str | None (optional)

The path to save the results to. Supported file formats are: ‘.zarr’, ‘.nc’, ‘.cdf’, ‘.pkl’. The ‘.zarr’ and ‘.nc’ formats only support numeric and boolean data. Only the ‘.pkl’ format supports saving data of any type.

**kwargsdict

Additional settings: - timeit: bool, measure the duration of each experiment. - auto_save: bool, automatically save the results after each experiment. - enable_priorities: bool, run experiments with higher priority first. - pass_uuid: bool, pass a unique identifier to the experiment function.

Returns#

SweepExp | SweepExpMPI | SweepExpParallel

An instance of the appropriate SweepExp class based on the mode.

Examples#

from sweepexp import sweepexp

# Create a simple experiment function
def my_experiment(x: int, y: float) -> dict:
    return {"sum": x + y, "product": x * y}

# Initialize the sweepexp object
sweep = sweepexp(
    func=my_experiment,
    parameters={"x": [1, 2, 3], "y": [4, 5, 6]},
)

# Run the sweep
sweep.run()
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