def testGridSearchAndEval(self): trials = generate_trials({ "run": "PPO", "config": { "qux": lambda spec: 2 + 2, "bar": grid_search([True, False]), "foo": grid_search([1, 2, 3]), }, }) trials = list(trials) self.assertEqual(len(trials), 6) self.assertEqual(trials[0].config, {"bar": True, "foo": 1, "qux": 4}) self.assertEqual(trials[0].experiment_tag, "0_bar=True,foo=1,qux=4")
def testDependentLambda(self): trials = generate_trials({ "run": "PPO", "config": { "x": grid_search([1, 2]), "y": lambda spec: spec.config.x * 100, }, }) trials = list(trials) self.assertEqual(len(trials), 2) self.assertEqual(trials[0].config, {"x": 1, "y": 100}) self.assertEqual(trials[1].config, {"x": 2, "y": 200})
def testDependentGridSearch(self): trials = generate_trials({ "run": "PPO", "config": { "x": grid_search([ lambda spec: spec.config.y * 100, lambda spec: spec.config.y * 200 ]), "y": lambda spec: 1, }, }) trials = list(trials) self.assertEqual(len(trials), 2) self.assertEqual(trials[0].config, {"x": 100, "y": 1}) self.assertEqual(trials[1].config, {"x": 200, "y": 1})
def f(): run_experiments( {"foo": { "run": grid_search("invalid grid search"), }})
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) # !!! Example of using the ray.tune Python API !!! if __name__ == '__main__': runner = TrialRunner() spec = { 'stop': { 'mean_accuracy': 0.99, 'time_total_s': 600, }, 'config': { 'script_file_path': os.path.abspath(__file__), 'script_min_iter_time_s': 1, 'activation': grid_search(['relu', 'elu', 'tanh']), }, } # These arguments are only for testing purposes. parser = argparse.ArgumentParser() parser.add_argument('--fast', action='store_true', help='Run minimal iterations.') args, _ = parser.parse_known_args() if args.fast: spec['stop']['training_iteration'] = 2 for trial in generate_trials(spec): runner.add_trial(trial)