AxMultitaskGenerator

class optimas.generators.AxMultitaskGenerator(varying_parameters, objectives, lofi_task, hifi_task, analyzed_parameters=None, use_cuda=False, gpu_id=0, dedicated_resources=False, save_model=True, model_save_period=5, model_history_dir='model_history')

Multitask Bayesian optimization using the Ax developer API.

Two tasks need to be provided: one for low-fidelity evaluations and another for high-fidelity evaluations. The objective will be optimized by maximizing (or minimizing) its high-fidelity value. Only one objective can be provided.

Parameters:

varying_parameterslist of VaryingParameter

List of input parameters to vary. One them should be a fidelity.

objectiveslist of Objective

List of optimization objectives. Only one objective is supported.

lofi_task, hifi_taskTask

The low- and high-fidelity tasks.

analyzed_parameterslist of Parameter, optional

List of parameters to analyze at each trial, but which are not optimization objectives. By default None.

use_cudabool, optional

Whether to allow the generator to run on a CUDA GPU. By default False.

gpu_idint, optional

The ID of the GPU in which to run the generator. By default, 0.

dedicated_resourcesbool, optional

Whether to allocated dedicated resources (e.g., the GPU) for the generator. These resources will not be available to the simulation workers. By default, False.

save_modelbool, optional

Whether to save the optimization model (in this case, the Ax experiment) to disk. By default True.

model_save_periodint, optional

Periodicity, in number of evaluated Trials, with which to save the model to disk. By default, 5.

model_history_dirstr, optional

Name of the directory in which the model will be saved. By default, 'model_history'.

Methods

ask_trials(n_trials)	Ask the generator to suggest the next n_trials to evaluate.
attach_trials(trial_data[, ...])	Manually add a list of trials to the generator.
get_gen_specs(sim_workers, run_params, sim_max)	Get the libEnsemble gen_specs.
get_libe_specs()	Get the libEnsemble libe_specs.
get_trial(trial_index)	Get trial by index.
ignore_trials(trials)	Ignore trials as determined by the generator.
incorporate_history(history)	Incorporate past history into the generator.
ingest(results)	Incorporate evaluated trials into experiment.
mark_trial_as_failed(trial_index)	Mark an already evaluated trial as failed.
save_model_to_file()	Save model to file.
suggest(num_points)	Request the next set of points to evaluate.
tell_trials(trials[, allow_saving_model])	Give trials back to generator once they have been evaluated.
update_parameter(parameter)	Update a varying parameter of the generator.

Attributes

analyzed_parameters	Get the list of analyzed parameters.
constraints	Get the list of constraints.
dedicated_resources	Get whether the generator has dedicated resources allocated.
gpu_id	Get the ID of the GPU used by the generator.
n_completed_trials	Get the number of successfully evaluated trials.
n_evaluated_trials	Get the number of evaluated trials.
n_failed_trials	Get the number of unsuccessfully evaluated trials.
n_given_trials	Get the number of trials given for evaluation.
n_queued_trials	Get the number of trials queued for evaluation.
objectives	Get the list of objectives.
use_cuda	Get whether the generator can use CUDA.
varying_parameters	Get the list of varying parameters.