ray/test/trial_runner_test.py
Eric Liang 79ea205b3e [rllib] Initial work on integrating hyperparameter search tool (#1107)
* clean up train

* update

* update train script

* add tuned examples

* add agent catalog

* add tune lib

* update

* fix

* testS

* remove

* train docs

* comments

* todo

* fix resource parsing

* fix cr test

* add test

* try to fix travis test
2017-10-13 16:18:16 -07:00

200 lines
6.5 KiB
Python

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import ray
from ray.tune.trial import Trial, Resources
from ray.tune.trial_runner import TrialRunner
from ray.tune.config_parser import parse_to_trials
class ConfigParserTest(unittest.TestCase):
def testParseToTrials(self):
trials = parse_to_trials({
"tune-pong": {
"env": "Pong-v0",
"alg": "PPO",
"num_trials": 2,
"config": {
"foo": "bar"
},
},
})
self.assertEqual(len(trials), 2)
self.assertEqual(trials[0].env_name, "Pong-v0")
self.assertEqual(trials[0].config, {"foo": "bar"})
self.assertEqual(trials[0].alg, "PPO")
self.assertEqual(trials[0].agent_id, "0")
self.assertEqual(trials[0].local_dir, "/tmp/ray/tune-pong")
self.assertEqual(trials[1].agent_id, "1")
def testEval(self):
trials = parse_to_trials({
"tune-pong": {
"env": "Pong-v0",
"config": {
"foo": {
"eval": "2 + 2"
},
},
},
})
self.assertEqual(len(trials), 1)
self.assertEqual(trials[0].config, {"foo": 4})
self.assertEqual(trials[0].agent_id, "0_foo=4")
def testGridSearch(self):
trials = parse_to_trials({
"tune-pong": {
"env": "Pong-v0",
"num_trials": 6,
"config": {
"bar": {
"grid_search": [True, False]
},
"foo": {
"grid_search": [1, 2, 3]
},
},
},
})
self.assertEqual(len(trials), 6)
self.assertEqual(trials[0].config, {"bar": True, "foo": 1})
self.assertEqual(trials[0].agent_id, "0_bar=True_foo=1")
self.assertEqual(trials[1].config, {"bar": False, "foo": 1})
self.assertEqual(trials[1].agent_id, "1_bar=False_foo=1")
self.assertEqual(trials[2].config, {"bar": True, "foo": 2})
self.assertEqual(trials[3].config, {"bar": False, "foo": 2})
self.assertEqual(trials[4].config, {"bar": True, "foo": 3})
self.assertEqual(trials[5].config, {"bar": False, "foo": 3})
def testGridSearchAndEval(self):
trials = parse_to_trials({
"tune-pong": {
"env": "Pong-v0",
"num_trials": 1,
"config": {
"qux": {
"eval": "2 + 2"
},
"bar": {
"grid_search": [True, False]
},
"foo": {
"grid_search": [1, 2, 3]
},
},
},
})
self.assertEqual(len(trials), 1)
self.assertEqual(trials[0].config, {"bar": True, "foo": 1, "qux": 4})
self.assertEqual(trials[0].agent_id, "0_bar=True_foo=1_qux=4")
class TrialRunnerTest(unittest.TestCase):
def tearDown(self):
ray.worker.cleanup()
def testTrialStatus(self):
ray.init()
trial = Trial("CartPole-v0", "__fake")
self.assertEqual(trial.status, Trial.PENDING)
trial.start()
self.assertEqual(trial.status, Trial.RUNNING)
trial.stop()
self.assertEqual(trial.status, Trial.TERMINATED)
trial.stop(error=True)
self.assertEqual(trial.status, Trial.ERROR)
def testTrialErrorOnStart(self):
ray.init()
trial = Trial("CartPole-v0", "asdf")
try:
trial.start()
except Exception as e:
self.assertIn("Unknown algorithm", str(e))
def testResourceScheduler(self):
ray.init(num_cpus=4, num_gpus=1)
runner = TrialRunner()
kwargs = {
"stopping_criterion": {"training_iteration": 1},
"resources": Resources(cpu=1, gpu=1),
}
trials = [
Trial("CartPole-v0", "__fake", **kwargs),
Trial("CartPole-v0", "__fake", **kwargs)]
for t in trials:
runner.add_trial(t)
runner.step()
self.assertEqual(trials[0].status, Trial.RUNNING)
self.assertEqual(trials[1].status, Trial.PENDING)
runner.step()
self.assertEqual(trials[0].status, Trial.TERMINATED)
self.assertEqual(trials[1].status, Trial.PENDING)
runner.step()
self.assertEqual(trials[0].status, Trial.TERMINATED)
self.assertEqual(trials[1].status, Trial.RUNNING)
runner.step()
self.assertEqual(trials[0].status, Trial.TERMINATED)
self.assertEqual(trials[1].status, Trial.TERMINATED)
def testMultiStepRun(self):
ray.init(num_cpus=4, num_gpus=2)
runner = TrialRunner()
kwargs = {
"stopping_criterion": {"training_iteration": 5},
"resources": Resources(cpu=1, gpu=1),
}
trials = [
Trial("CartPole-v0", "__fake", **kwargs),
Trial("CartPole-v0", "__fake", **kwargs)]
for t in trials:
runner.add_trial(t)
runner.step()
self.assertEqual(trials[0].status, Trial.RUNNING)
self.assertEqual(trials[1].status, Trial.PENDING)
runner.step()
self.assertEqual(trials[0].status, Trial.RUNNING)
self.assertEqual(trials[1].status, Trial.RUNNING)
runner.step()
self.assertEqual(trials[0].status, Trial.RUNNING)
self.assertEqual(trials[1].status, Trial.RUNNING)
runner.step()
self.assertEqual(trials[0].status, Trial.RUNNING)
self.assertEqual(trials[1].status, Trial.RUNNING)
def testErrorHandling(self):
ray.init(num_cpus=4, num_gpus=2)
runner = TrialRunner()
kwargs = {
"stopping_criterion": {"training_iteration": 1},
"resources": Resources(cpu=1, gpu=1),
}
trials = [
Trial("CartPole-v0", "asdf", **kwargs),
Trial("CartPole-v0", "__fake", **kwargs)]
for t in trials:
runner.add_trial(t)
runner.step()
self.assertEqual(trials[0].status, Trial.ERROR)
self.assertEqual(trials[1].status, Trial.PENDING)
runner.step()
self.assertEqual(trials[0].status, Trial.ERROR)
self.assertEqual(trials[1].status, Trial.RUNNING)
if __name__ == "__main__":
unittest.main(verbosity=2)