def _batch_trials(batch_run): proto_flag_vals = batch_run.batch_proto.get("flags") batch_run = batch_util.batch_run() max_trials = batch_run.get("max_trials") or DEFAULT_MAX_TRIALS random_seed = batch_run.get("random_seed") try: return skopt_util.random_trials_for_flags(proto_flag_vals, max_trials, random_seed) except skopt_util.MissingSearchDimension as e: skopt_util.missing_search_dim_error(proto_flag_vals) except skopt_util.InvalidSearchDimension as e: _search_dim_error(e)
def main(): batch_util.init_logging() batch = batch_util.batch_run() batch_flags = batch.get("flags") max_trials = batch.get("max_trials") or DEFAULT_MAX_TRIALS random_seed = batch.get("random_seed") trials = hyperopt.Trials() try: hyperopt.fmin( fn=_objective_fn(batch), space=_space_for_flags(batch), algo=_tpe_suggest, max_evals=max_trials, show_progressbar=False, rstate=np.random.RandomState(random_seed), trials=trials, ) except hyperopt.exceptions.AllTrialsFailed: pass else: _label_best(trials.best_trial)
def main(): batch_util.init_logging() batch_run = batch_util.batch_run() skopt_util.handle_seq_trials(batch_run, _suggest_x)
def main(): batch_util.init_logging() batch_run = batch_util.batch_run() trials = _batch_trials(batch_run) batch_util.handle_trials(batch_run, trials)
from guild import batch_util try: batch_run = batch_util.batch_run() except batch_util.CurrentRunNotBatchError: print("This script must be run as a Guild optimizer") else: proto_flags = batch_run.batch_proto.get("flags", {}) print("Tune using proto flags: %s" % sorted(proto_flags.items()))