ray/test/trial_runner_test.py
Richard Liaw 0c9817fa76 [tune] Tune Pausing (#1136)
* fix yaml bug

* add ext agent

* gpus

* update

* tuning

* docs

* Sun Oct 15 21:09:25 PDT 2017

* lint

* update

* Sun Oct 15 22:39:55 PDT 2017

* Sun Oct 15 22:40:17 PDT 2017

* Sun Oct 15 22:43:06 PDT 2017

* Sun Oct 15 22:46:06 PDT 2017

* Sun Oct 15 22:46:21 PDT 2017

* Sun Oct 15 22:48:11 PDT 2017

* Sun Oct 15 22:48:44 PDT 2017

* Sun Oct 15 22:49:23 PDT 2017

* Sun Oct 15 22:50:21 PDT 2017

* Sun Oct 15 22:53:00 PDT 2017

* Sun Oct 15 22:53:34 PDT 2017

* Sun Oct 15 22:54:33 PDT 2017

* Sun Oct 15 22:54:50 PDT 2017

* Sun Oct 15 22:55:20 PDT 2017

* Sun Oct 15 22:56:56 PDT 2017

* Sun Oct 15 22:59:03 PDT 2017

* fix

* Update tune_mnist_ray.py

* remove script trial

* fix

* reorder

* fix ex

* py2 support

* upd

* comments

* comments

* cleanup readme

* fix trial

* annotate

* Update rllib.rst

* init pausing

* Docs, Lint

* fix danglings and restore endpoint moved to trialrunner

* renaming

* nit

* start always starts from checkpoint

* smalls

* nits

* lint

* last change
2017-10-22 23:04:15 -07:00

260 lines
8.6 KiB
Python

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import os
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].experiment_tag, "0")
self.assertEqual(trials[0].local_dir, "/tmp/ray/tune-pong")
self.assertEqual(trials[1].experiment_tag, "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].experiment_tag, "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].experiment_tag, "0_bar=True_foo=1")
self.assertEqual(trials[1].config, {"bar": False, "foo": 1})
self.assertEqual(trials[1].experiment_tag, "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].experiment_tag, "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)
def testCheckpointing(self):
ray.init(num_cpus=1, num_gpus=1)
runner = TrialRunner()
kwargs = {
"stopping_criterion": {"training_iteration": 1},
"resources": Resources(cpu=1, gpu=1),
}
runner.add_trial(Trial("CartPole-v0", "__fake", **kwargs))
trials = runner.get_trials()
runner.step()
self.assertEqual(trials[0].status, Trial.RUNNING)
self.assertEqual(ray.get(trials[0].agent.set_info.remote(1)), 1)
path = trials[0].checkpoint()
kwargs["restore_path"] = path
runner.add_trial(Trial("CartPole-v0", "__fake", **kwargs))
trials = runner.get_trials()
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)
self.assertEqual(ray.get(trials[1].agent.get_info.remote()), 1)
self.addCleanup(os.remove, path)
def testPauseThenResume(self):
ray.init(num_cpus=1, num_gpus=1)
runner = TrialRunner()
kwargs = {
"stopping_criterion": {"training_iteration": 2},
"resources": Resources(cpu=1, gpu=1),
}
runner.add_trial(Trial("CartPole-v0", "__fake", **kwargs))
trials = runner.get_trials()
runner.step()
self.assertEqual(trials[0].status, Trial.RUNNING)
self.assertEqual(ray.get(trials[0].agent.get_info.remote()), None)
self.assertEqual(ray.get(trials[0].agent.set_info.remote(1)), 1)
trials[0].pause()
self.assertEqual(trials[0].status, Trial.PAUSED)
trials[0].resume()
self.assertEqual(trials[0].status, Trial.RUNNING)
runner.step()
self.assertEqual(trials[0].status, Trial.RUNNING)
self.assertEqual(ray.get(trials[0].agent.get_info.remote()), 1)
runner.step()
self.assertEqual(trials[0].status, Trial.TERMINATED)
if __name__ == "__main__":
unittest.main(verbosity=2)