from advisor_client.model import TrialMetric from advisor_client.client import AdvisorClient client = AdvisorClient() # Create Study name = "Study" study_configuration = { "goal": "MAXIMIZE", "maxTrials": 5, "maxParallelTrials": 1, "params": [{ "parameterName": "hidden1", "type": "INTEGER", "minValue": 40, "maxValue": 400, "scalingType": "LINEAR" }] } study = client.create_study(name, study_configuration) print(study) print(client.list_studies()) trials = client.get_suggestions(study.id, 3) print(trials) print(client.list_trials(study.id))
def list_studies(args): client = AdvisorClient() print_studies(client.list_studies())
def list_studies(args): client = AdvisorClient() print_studies(client.list_studies())
from advisor_client.model import TrialMetric from advisor_client.client import AdvisorClient client = AdvisorClient() # Create Study name = "Study" study_configuration = { "goal": "MAXIMIZE", "maxTrials": 5, "maxParallelTrials": 1, "params": [{ "parameterName": "hidden1", "type": "INTEGER", "minValue": 40, "maxValue": 400, "scallingType": "LINEAR" }] } study = client.create_study(name, study_configuration) print(study) print(client.list_studies()) trials = client.get_suggestions(study.id, 3) print(trials) print(client.list_trials(study.id))