Generator Specs

Used to specify the generator function, its inputs and outputs, and user data.

Can be constructed and passed to libEnsemble as a Python class or a dictionary.

class

...
import numpy as np
from libensemble import GenSpecs
from generator import gen_random_sample

...

gen_specs = GenSpecs(
    gen_f=gen_random_sample,
    outputs=[("x", float, (1,))],
    user={
        "lower": np.array([-3]),
        "upper": np.array([3]),
        "gen_batch_size": 5,
    },
)
...

pydantic model libensemble.specs.GenSpecs

Specifications for configuring a Generator Function.

Fields:

gen_f (Callable | None)
inputs (List[str] | None)
persis_in (List[str] | None)
outputs (List[Tuple[str, Any] | Tuple[str, Any, int | Tuple]] | None)
globus_compute_endpoint (str | None)
threaded (bool | None)
user (dict | None)

field gen_f: Callable | None = None: Python function matching the gen_f interface. Produces parameters for evaluation by a simulator function, and makes decisions based on simulator function output.

field globus_compute_endpoint: str | None = '': A Globus Compute (https://www.globus.org/compute) ID corresponding to an active endpoint on a remote system. libEnsemble’s workers will submit generator function instances to this endpoint instead of calling them locally.

field inputs: List[str] | None = [] (alias 'in'): List of field names out of the complete history to pass into the generator function upon calling.

field outputs: List[Tuple[str, Any] | Tuple[str, Any, int | Tuple]] | None = [] (alias 'out'): List of 2- or 3-tuples corresponding to NumPy dtypes. e.g. ("dim", int, (3,)), or ("path", str). Typically used to initialize an output array within the generator: out = np.zeros(100, dtype=gen_specs["out"]). Also used to construct libEnsemble’s history array.

field persis_in: List[str] | None = []: List of field names to send to a persistent generator function throughout the run, following initialization.

field threaded: bool | None = False: Instruct Worker process to launch user function to a thread.

field user: dict | None = {}: A user-data dictionary to place bounds, constants, settings, or other parameters for customizing the generator function

dict

...
import numpy as np
from generator import gen_random_sample

...

gen_specs = {
    "gen_f": gen_random_sample,
    "out": [("x", float, (1,))],
    "user": {
        "lower": np.array([-3]),
        "upper": np.array([3]),
        "gen_batch_size": 5,
    },
}