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)
