mirror of
https://github.com/wassname/ray.git
synced 2026-06-27 17:49:47 +08:00
Robert Nishihara
658c14282c
* Remove legacy Ray code. * Fix cmake and simplify monitor. * Fix linting * Updates * Fix * Implement some methods. * Remove more plasma manager references. * Fix * Linting * Fix * Fix * Make sure class IDs are strings. * Some path fixes * Fix * Path fixes and update arrow * Fixes. * linting * Fixes * Java fixes * Some java fixes * TaskLanguage -> Language * Minor * Fix python test and remove unused method signature. * Fix java tests * Fix jenkins tests * Remove commented out code.
1417 lines
46 KiB
Python
1417 lines
46 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import os
|
|
import time
|
|
import unittest
|
|
|
|
import ray
|
|
from ray.rllib import _register_all
|
|
|
|
from ray.tune import Trainable, TuneError
|
|
from ray.tune import register_env, register_trainable, run_experiments
|
|
from ray.tune.ray_trial_executor import RayTrialExecutor
|
|
from ray.tune.schedulers import TrialScheduler, FIFOScheduler
|
|
from ray.tune.registry import _global_registry, TRAINABLE_CLASS
|
|
from ray.tune.result import DEFAULT_RESULTS_DIR, TIMESTEPS_TOTAL, DONE
|
|
from ray.tune.util import pin_in_object_store, get_pinned_object
|
|
from ray.tune.experiment import Experiment
|
|
from ray.tune.trial import Trial, Resources
|
|
from ray.tune.trial_runner import TrialRunner
|
|
from ray.tune.suggest import grid_search, BasicVariantGenerator
|
|
from ray.tune.suggest.suggestion import (_MockSuggestionAlgorithm,
|
|
SuggestionAlgorithm)
|
|
from ray.tune.suggest.variant_generator import RecursiveDependencyError
|
|
|
|
|
|
class TrainableFunctionApiTest(unittest.TestCase):
|
|
def setUp(self):
|
|
ray.init(num_cpus=4, num_gpus=0)
|
|
|
|
def tearDown(self):
|
|
ray.shutdown()
|
|
_register_all() # re-register the evicted objects
|
|
|
|
def testPinObject(self):
|
|
X = pin_in_object_store("hello")
|
|
|
|
@ray.remote
|
|
def f():
|
|
return get_pinned_object(X)
|
|
|
|
self.assertEqual(ray.get(f.remote()), "hello")
|
|
|
|
def testFetchPinned(self):
|
|
X = pin_in_object_store("hello")
|
|
|
|
def train(config, reporter):
|
|
get_pinned_object(X)
|
|
reporter(timesteps_total=100, done=True)
|
|
|
|
register_trainable("f1", train)
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0,
|
|
},
|
|
}
|
|
})
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 100)
|
|
|
|
def testRegisterEnv(self):
|
|
register_env("foo", lambda: None)
|
|
self.assertRaises(TypeError, lambda: register_env("foo", 2))
|
|
|
|
def testRegisterEnvOverwrite(self):
|
|
def train(config, reporter):
|
|
reporter(timesteps_total=100, done=True)
|
|
|
|
def train2(config, reporter):
|
|
reporter(timesteps_total=200, done=True)
|
|
|
|
register_trainable("f1", train)
|
|
register_trainable("f1", train2)
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0,
|
|
},
|
|
}
|
|
})
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 200)
|
|
|
|
def testRegisterTrainable(self):
|
|
def train(config, reporter):
|
|
pass
|
|
|
|
class A(object):
|
|
pass
|
|
|
|
class B(Trainable):
|
|
pass
|
|
|
|
register_trainable("foo", train)
|
|
register_trainable("foo", B)
|
|
self.assertRaises(TypeError, lambda: register_trainable("foo", B()))
|
|
self.assertRaises(TypeError, lambda: register_trainable("foo", A))
|
|
|
|
def testBuiltInTrainableResources(self):
|
|
class B(Trainable):
|
|
@classmethod
|
|
def default_resource_request(cls, config):
|
|
return Resources(cpu=config["cpu"], gpu=config["gpu"])
|
|
|
|
def _train(self):
|
|
return {"timesteps_this_iter": 1, "done": True}
|
|
|
|
register_trainable("B", B)
|
|
|
|
def f(cpus, gpus, queue_trials):
|
|
return run_experiments(
|
|
{
|
|
"foo": {
|
|
"run": "B",
|
|
"config": {
|
|
"cpu": cpus,
|
|
"gpu": gpus,
|
|
},
|
|
}
|
|
},
|
|
queue_trials=queue_trials)[0]
|
|
|
|
# Should all succeed
|
|
self.assertEqual(f(0, 0, False).status, Trial.TERMINATED)
|
|
self.assertEqual(f(1, 0, True).status, Trial.TERMINATED)
|
|
self.assertEqual(f(1, 0, True).status, Trial.TERMINATED)
|
|
|
|
# Infeasible even with queueing enabled (no gpus)
|
|
self.assertRaises(TuneError, lambda: f(1, 1, True))
|
|
|
|
# Too large resource request
|
|
self.assertRaises(TuneError, lambda: f(100, 100, False))
|
|
self.assertRaises(TuneError, lambda: f(0, 100, False))
|
|
self.assertRaises(TuneError, lambda: f(100, 0, False))
|
|
|
|
# TODO(ekl) how can we test this is queued (hangs)?
|
|
# f(100, 0, True)
|
|
|
|
def testRewriteEnv(self):
|
|
def train(config, reporter):
|
|
reporter(timesteps_total=1)
|
|
|
|
register_trainable("f1", train)
|
|
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"env": "CartPole-v0",
|
|
}
|
|
})
|
|
self.assertEqual(trial.config["env"], "CartPole-v0")
|
|
|
|
def testConfigPurity(self):
|
|
def train(config, reporter):
|
|
assert config == {"a": "b"}, config
|
|
reporter(timesteps_total=1)
|
|
|
|
register_trainable("f1", train)
|
|
run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"a": "b"
|
|
},
|
|
}
|
|
})
|
|
|
|
def testLogdir(self):
|
|
def train(config, reporter):
|
|
assert "/tmp/logdir/foo" in os.getcwd(), os.getcwd()
|
|
reporter(timesteps_total=1)
|
|
|
|
register_trainable("f1", train)
|
|
run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"local_dir": "/tmp/logdir",
|
|
"config": {
|
|
"a": "b"
|
|
},
|
|
}
|
|
})
|
|
|
|
def testLogdirStartingWithTilde(self):
|
|
local_dir = '~/ray_results/local_dir'
|
|
|
|
def train(config, reporter):
|
|
cwd = os.getcwd()
|
|
assert cwd.startswith(os.path.expanduser(local_dir)), cwd
|
|
assert not cwd.startswith('~'), cwd
|
|
reporter(timesteps_total=1)
|
|
|
|
register_trainable('f1', train)
|
|
run_experiments({
|
|
'foo': {
|
|
'run': 'f1',
|
|
'local_dir': local_dir,
|
|
'config': {
|
|
'a': 'b'
|
|
},
|
|
}
|
|
})
|
|
|
|
def testLongFilename(self):
|
|
def train(config, reporter):
|
|
assert "/tmp/logdir/foo" in os.getcwd(), os.getcwd()
|
|
reporter(timesteps_total=1)
|
|
|
|
register_trainable("f1", train)
|
|
run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"local_dir": "/tmp/logdir",
|
|
"config": {
|
|
"a" * 50: lambda spec: 5.0 / 7,
|
|
"b" * 50: lambda spec: "long" * 40
|
|
},
|
|
}
|
|
})
|
|
|
|
def testBadParams(self):
|
|
def f():
|
|
run_experiments({"foo": {}})
|
|
|
|
self.assertRaises(TuneError, f)
|
|
|
|
def testBadParams2(self):
|
|
def f():
|
|
run_experiments({
|
|
"foo": {
|
|
"run": "asdf",
|
|
"bah": "this param is not allowed",
|
|
}
|
|
})
|
|
|
|
self.assertRaises(TuneError, f)
|
|
|
|
def testBadParams3(self):
|
|
def f():
|
|
run_experiments({
|
|
"foo": {
|
|
"run": grid_search("invalid grid search"),
|
|
}
|
|
})
|
|
|
|
self.assertRaises(TuneError, f)
|
|
|
|
def testBadParams4(self):
|
|
def f():
|
|
run_experiments({
|
|
"foo": {
|
|
"run": "asdf",
|
|
}
|
|
})
|
|
|
|
self.assertRaises(TuneError, f)
|
|
|
|
def testBadParams5(self):
|
|
def f():
|
|
run_experiments({"foo": {"run": "PPO", "stop": {"asdf": 1}}})
|
|
|
|
self.assertRaises(TuneError, f)
|
|
|
|
def testBadParams6(self):
|
|
def f():
|
|
run_experiments({
|
|
"foo": {
|
|
"run": "PPO",
|
|
"trial_resources": {
|
|
"asdf": 1
|
|
}
|
|
}
|
|
})
|
|
|
|
self.assertRaises(TuneError, f)
|
|
|
|
def testBadStoppingReturn(self):
|
|
def train(config, reporter):
|
|
reporter()
|
|
|
|
register_trainable("f1", train)
|
|
|
|
def f():
|
|
run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"stop": {
|
|
"time": 10
|
|
},
|
|
"config": {
|
|
"script_min_iter_time_s": 0,
|
|
},
|
|
}
|
|
})
|
|
|
|
self.assertRaises(TuneError, f)
|
|
|
|
def testEarlyReturn(self):
|
|
def train(config, reporter):
|
|
reporter(timesteps_total=100, done=True)
|
|
time.sleep(99999)
|
|
|
|
register_trainable("f1", train)
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0,
|
|
},
|
|
}
|
|
})
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 100)
|
|
|
|
def testAbruptReturn(self):
|
|
def train(config, reporter):
|
|
reporter(timesteps_total=100)
|
|
|
|
register_trainable("f1", train)
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0,
|
|
},
|
|
}
|
|
})
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 100)
|
|
|
|
def testErrorReturn(self):
|
|
def train(config, reporter):
|
|
raise Exception("uh oh")
|
|
|
|
register_trainable("f1", train)
|
|
|
|
def f():
|
|
run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0,
|
|
},
|
|
}
|
|
})
|
|
|
|
self.assertRaises(TuneError, f)
|
|
|
|
def testSuccess(self):
|
|
def train(config, reporter):
|
|
for i in range(100):
|
|
reporter(timesteps_total=i)
|
|
|
|
register_trainable("f1", train)
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0,
|
|
},
|
|
}
|
|
})
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 99)
|
|
|
|
def testNoRaiseFlag(self):
|
|
def train(config, reporter):
|
|
# Finish this trial without any metric,
|
|
# which leads to a failed trial
|
|
return
|
|
|
|
register_trainable("f1", train)
|
|
|
|
[trial] = run_experiments(
|
|
{
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0,
|
|
},
|
|
}
|
|
},
|
|
raise_on_failed_trial=False)
|
|
self.assertEqual(trial.status, Trial.ERROR)
|
|
|
|
def testReportInfinity(self):
|
|
def train(config, reporter):
|
|
for i in range(100):
|
|
reporter(mean_accuracy=float('inf'))
|
|
|
|
register_trainable("f1", train)
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0,
|
|
},
|
|
}
|
|
})
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertEqual(trial.last_result['mean_accuracy'], float('inf'))
|
|
|
|
def testReportTimeStep(self):
|
|
def train(config, reporter):
|
|
for i in range(100):
|
|
reporter(mean_accuracy=5)
|
|
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": train,
|
|
"config": {
|
|
"script_min_iter_time_s": 0,
|
|
},
|
|
}
|
|
})
|
|
self.assertIsNone(trial.last_result[TIMESTEPS_TOTAL])
|
|
|
|
def train3(config, reporter):
|
|
for i in range(10):
|
|
reporter(timesteps_total=5)
|
|
|
|
[trial3] = run_experiments({
|
|
"foo": {
|
|
"run": train3,
|
|
"config": {
|
|
"script_min_iter_time_s": 0,
|
|
},
|
|
}
|
|
})
|
|
self.assertEqual(trial3.last_result[TIMESTEPS_TOTAL], 5)
|
|
self.assertEqual(trial3.last_result["timesteps_this_iter"], 0)
|
|
|
|
def testCheckpointDict(self):
|
|
class TestTrain(Trainable):
|
|
def _setup(self, config):
|
|
self.state = {"hi": 1}
|
|
|
|
def _train(self):
|
|
return {"timesteps_this_iter": 1, "done": True}
|
|
|
|
def _save(self, path):
|
|
return self.state
|
|
|
|
def _restore(self, state):
|
|
self.state = state
|
|
|
|
test_trainable = TestTrain()
|
|
result = test_trainable.save()
|
|
test_trainable.state["hi"] = 2
|
|
test_trainable.restore(result)
|
|
self.assertEqual(test_trainable.state["hi"], 1)
|
|
|
|
trials = run_experiments({
|
|
"foo": {
|
|
"run": TestTrain,
|
|
"checkpoint_at_end": True
|
|
}
|
|
})
|
|
for trial in trials:
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertTrue(trial.has_checkpoint())
|
|
|
|
def testMultipleCheckpoints(self):
|
|
class TestTrain(Trainable):
|
|
def _setup(self, config):
|
|
self.state = {"hi": 1, "iter": 0}
|
|
|
|
def _train(self):
|
|
self.state["iter"] += 1
|
|
return {"timesteps_this_iter": 1, "done": True}
|
|
|
|
def _save(self, path):
|
|
return self.state
|
|
|
|
def _restore(self, state):
|
|
self.state = state
|
|
|
|
test_trainable = TestTrain()
|
|
checkpoint_1 = test_trainable.save()
|
|
test_trainable.train()
|
|
checkpoint_2 = test_trainable.save()
|
|
self.assertNotEqual(checkpoint_1, checkpoint_2)
|
|
test_trainable.restore(checkpoint_2)
|
|
self.assertEqual(test_trainable.state["iter"], 1)
|
|
test_trainable.restore(checkpoint_1)
|
|
self.assertEqual(test_trainable.state["iter"], 0)
|
|
|
|
trials = run_experiments({
|
|
"foo": {
|
|
"run": TestTrain,
|
|
"checkpoint_at_end": True
|
|
}
|
|
})
|
|
for trial in trials:
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertTrue(trial.has_checkpoint())
|
|
|
|
|
|
class RunExperimentTest(unittest.TestCase):
|
|
def setUp(self):
|
|
ray.init()
|
|
|
|
def tearDown(self):
|
|
ray.shutdown()
|
|
_register_all() # re-register the evicted objects
|
|
|
|
def testDict(self):
|
|
def train(config, reporter):
|
|
for i in range(100):
|
|
reporter(timesteps_total=i)
|
|
|
|
register_trainable("f1", train)
|
|
trials = run_experiments({
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0
|
|
}
|
|
},
|
|
"bar": {
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0
|
|
}
|
|
}
|
|
})
|
|
for trial in trials:
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 99)
|
|
|
|
def testExperiment(self):
|
|
def train(config, reporter):
|
|
for i in range(100):
|
|
reporter(timesteps_total=i)
|
|
|
|
register_trainable("f1", train)
|
|
exp1 = Experiment(**{
|
|
"name": "foo",
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0
|
|
}
|
|
})
|
|
[trial] = run_experiments(exp1)
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 99)
|
|
|
|
def testExperimentList(self):
|
|
def train(config, reporter):
|
|
for i in range(100):
|
|
reporter(timesteps_total=i)
|
|
|
|
register_trainable("f1", train)
|
|
exp1 = Experiment(**{
|
|
"name": "foo",
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0
|
|
}
|
|
})
|
|
exp2 = Experiment(**{
|
|
"name": "bar",
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0
|
|
}
|
|
})
|
|
trials = run_experiments([exp1, exp2])
|
|
for trial in trials:
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 99)
|
|
|
|
def testSpecifyAlgorithm(self):
|
|
"""Tests run_experiments works without specifying experiment."""
|
|
|
|
def train(config, reporter):
|
|
for i in range(100):
|
|
reporter(timesteps_total=i)
|
|
|
|
register_trainable("f1", train)
|
|
|
|
alg = BasicVariantGenerator()
|
|
alg.add_configurations({
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"script_min_iter_time_s": 0
|
|
}
|
|
}
|
|
})
|
|
trials = run_experiments(search_alg=alg)
|
|
for trial in trials:
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 99)
|
|
|
|
def testAutoregisterTrainable(self):
|
|
def train(config, reporter):
|
|
for i in range(100):
|
|
reporter(timesteps_total=i)
|
|
|
|
class B(Trainable):
|
|
def _train(self):
|
|
return {"timesteps_this_iter": 1, "done": True}
|
|
|
|
register_trainable("f1", train)
|
|
trials = run_experiments({
|
|
"foo": {
|
|
"run": train,
|
|
"config": {
|
|
"script_min_iter_time_s": 0
|
|
}
|
|
},
|
|
"bar": {
|
|
"run": B
|
|
}
|
|
})
|
|
for trial in trials:
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
|
|
def testCheckpointAtEnd(self):
|
|
class train(Trainable):
|
|
def _train(self):
|
|
return {"timesteps_this_iter": 1, "done": True}
|
|
|
|
def _save(self, path):
|
|
return path
|
|
|
|
trials = run_experiments({
|
|
"foo": {
|
|
"run": train,
|
|
"checkpoint_at_end": True
|
|
}
|
|
})
|
|
for trial in trials:
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertTrue(trial.has_checkpoint())
|
|
|
|
|
|
class VariantGeneratorTest(unittest.TestCase):
|
|
def setUp(self):
|
|
ray.init()
|
|
|
|
def tearDown(self):
|
|
ray.shutdown()
|
|
_register_all() # re-register the evicted objects
|
|
|
|
def generate_trials(self, spec, name):
|
|
suggester = BasicVariantGenerator()
|
|
suggester.add_configurations({name: spec})
|
|
return suggester.next_trials()
|
|
|
|
def testParseToTrials(self):
|
|
trials = self.generate_trials({
|
|
"run": "PPO",
|
|
"num_samples": 2,
|
|
"max_failures": 5,
|
|
"config": {
|
|
"env": "Pong-v0",
|
|
"foo": "bar"
|
|
},
|
|
}, "tune-pong")
|
|
trials = list(trials)
|
|
self.assertEqual(len(trials), 2)
|
|
self.assertEqual(str(trials[0]), "PPO_Pong-v0_0")
|
|
self.assertEqual(trials[0].config, {"foo": "bar", "env": "Pong-v0"})
|
|
self.assertEqual(trials[0].trainable_name, "PPO")
|
|
self.assertEqual(trials[0].experiment_tag, "0")
|
|
self.assertEqual(trials[0].max_failures, 5)
|
|
self.assertEqual(trials[0].local_dir,
|
|
os.path.join(DEFAULT_RESULTS_DIR, "tune-pong"))
|
|
self.assertEqual(trials[1].experiment_tag, "1")
|
|
|
|
def testEval(self):
|
|
trials = self.generate_trials({
|
|
"run": "PPO",
|
|
"config": {
|
|
"foo": {
|
|
"eval": "2 + 2"
|
|
},
|
|
},
|
|
}, "eval")
|
|
trials = list(trials)
|
|
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 = self.generate_trials({
|
|
"run": "PPO",
|
|
"config": {
|
|
"bar": {
|
|
"grid_search": [True, False]
|
|
},
|
|
"foo": {
|
|
"grid_search": [1, 2, 3]
|
|
},
|
|
},
|
|
}, "grid_search")
|
|
trials = list(trials)
|
|
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 = self.generate_trials({
|
|
"run": "PPO",
|
|
"config": {
|
|
"qux": lambda spec: 2 + 2,
|
|
"bar": grid_search([True, False]),
|
|
"foo": grid_search([1, 2, 3]),
|
|
},
|
|
}, "grid_eval")
|
|
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 testConditionResolution(self):
|
|
trials = self.generate_trials({
|
|
"run": "PPO",
|
|
"config": {
|
|
"x": 1,
|
|
"y": lambda spec: spec.config.x + 1,
|
|
"z": lambda spec: spec.config.y + 1,
|
|
},
|
|
}, "condition_resolution")
|
|
trials = list(trials)
|
|
self.assertEqual(len(trials), 1)
|
|
self.assertEqual(trials[0].config, {"x": 1, "y": 2, "z": 3})
|
|
|
|
def testDependentLambda(self):
|
|
trials = self.generate_trials({
|
|
"run": "PPO",
|
|
"config": {
|
|
"x": grid_search([1, 2]),
|
|
"y": lambda spec: spec.config.x * 100,
|
|
},
|
|
}, "dependent_lambda")
|
|
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 = self.generate_trials({
|
|
"run": "PPO",
|
|
"config": {
|
|
"x": grid_search([
|
|
lambda spec: spec.config.y * 100,
|
|
lambda spec: spec.config.y * 200
|
|
]),
|
|
"y": lambda spec: 1,
|
|
},
|
|
}, "dependent_grid_search")
|
|
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 testRecursiveDep(self):
|
|
try:
|
|
list(
|
|
self.generate_trials({
|
|
"run": "PPO",
|
|
"config": {
|
|
"foo": lambda spec: spec.config.foo,
|
|
},
|
|
}, "recursive_dep"))
|
|
except RecursiveDependencyError as e:
|
|
assert "`foo` recursively depends on" in str(e), e
|
|
else:
|
|
assert False
|
|
|
|
def testMaxConcurrentSuggestions(self):
|
|
"""Checks that next_trials() supports throttling."""
|
|
experiment_spec = {
|
|
"run": "PPO",
|
|
"num_samples": 6,
|
|
}
|
|
experiments = [Experiment.from_json("test", experiment_spec)]
|
|
|
|
searcher = _MockSuggestionAlgorithm(max_concurrent=4)
|
|
searcher.add_configurations(experiments)
|
|
trials = searcher.next_trials()
|
|
self.assertEqual(len(trials), 4)
|
|
self.assertEqual(searcher.next_trials(), [])
|
|
|
|
finished_trial = trials.pop()
|
|
searcher.on_trial_complete(finished_trial.trial_id)
|
|
self.assertEqual(len(searcher.next_trials()), 1)
|
|
|
|
finished_trial = trials.pop()
|
|
searcher.on_trial_complete(finished_trial.trial_id)
|
|
|
|
finished_trial = trials.pop()
|
|
searcher.on_trial_complete(finished_trial.trial_id)
|
|
|
|
finished_trial = trials.pop()
|
|
searcher.on_trial_complete(finished_trial.trial_id)
|
|
self.assertEqual(len(searcher.next_trials()), 1)
|
|
self.assertEqual(len(searcher.next_trials()), 0)
|
|
|
|
|
|
class TrialRunnerTest(unittest.TestCase):
|
|
def tearDown(self):
|
|
ray.shutdown()
|
|
_register_all() # re-register the evicted objects
|
|
|
|
def testTrialStatus(self):
|
|
ray.init()
|
|
trial = Trial("__fake")
|
|
trial_executor = RayTrialExecutor()
|
|
self.assertEqual(trial.status, Trial.PENDING)
|
|
trial_executor.start_trial(trial)
|
|
self.assertEqual(trial.status, Trial.RUNNING)
|
|
trial_executor.stop_trial(trial)
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
trial_executor.stop_trial(trial, error=True)
|
|
self.assertEqual(trial.status, Trial.ERROR)
|
|
|
|
def testExperimentTagTruncation(self):
|
|
ray.init()
|
|
|
|
def train(config, reporter):
|
|
reporter(timesteps_total=1)
|
|
|
|
trial_executor = RayTrialExecutor()
|
|
register_trainable("f1", train)
|
|
|
|
experiments = {
|
|
"foo": {
|
|
"run": "f1",
|
|
"config": {
|
|
"a" * 50: lambda spec: 5.0 / 7,
|
|
"b" * 50: lambda spec: "long" * 40
|
|
},
|
|
}
|
|
}
|
|
|
|
for name, spec in experiments.items():
|
|
trial_generator = BasicVariantGenerator()
|
|
trial_generator.add_configurations({name: spec})
|
|
for trial in trial_generator.next_trials():
|
|
trial_executor.start_trial(trial)
|
|
self.assertLessEqual(len(trial.logdir), 200)
|
|
trial_executor.stop_trial(trial)
|
|
|
|
def testTrialErrorOnStart(self):
|
|
ray.init()
|
|
trial_executor = RayTrialExecutor()
|
|
_global_registry.register(TRAINABLE_CLASS, "asdf", None)
|
|
trial = Trial("asdf", resources=Resources(1, 0))
|
|
try:
|
|
trial_executor.start_trial(trial)
|
|
except Exception as e:
|
|
self.assertIn("a class", str(e))
|
|
|
|
def testExtraResources(self):
|
|
ray.init(num_cpus=4, num_gpus=2)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 1
|
|
},
|
|
"resources": Resources(cpu=1, gpu=0, extra_cpu=3, extra_gpu=1),
|
|
}
|
|
trials = [Trial("__fake", **kwargs), Trial("__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)
|
|
|
|
def testFractionalGpus(self):
|
|
ray.init(num_cpus=4, num_gpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"resources": Resources(cpu=1, gpu=0.5),
|
|
}
|
|
trials = [
|
|
Trial("__fake", **kwargs),
|
|
Trial("__fake", **kwargs),
|
|
Trial("__fake", **kwargs),
|
|
Trial("__fake", **kwargs)
|
|
]
|
|
for t in trials:
|
|
runner.add_trial(t)
|
|
|
|
for _ in range(10):
|
|
runner.step()
|
|
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
self.assertEqual(trials[1].status, Trial.RUNNING)
|
|
self.assertEqual(trials[2].status, Trial.PENDING)
|
|
self.assertEqual(trials[3].status, Trial.PENDING)
|
|
|
|
def testResourceScheduler(self):
|
|
ray.init(num_cpus=4, num_gpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 1
|
|
},
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
}
|
|
trials = [Trial("__fake", **kwargs), Trial("__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(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 5
|
|
},
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
}
|
|
trials = [Trial("__fake", **kwargs), Trial("__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(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 1
|
|
},
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
}
|
|
_global_registry.register(TRAINABLE_CLASS, "asdf", None)
|
|
trials = [Trial("asdf", **kwargs), Trial("__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 testFailureRecoveryDisabled(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
"checkpoint_freq": 1,
|
|
"max_failures": 0,
|
|
"config": {
|
|
"mock_error": True,
|
|
},
|
|
}
|
|
runner.add_trial(Trial("__fake", **kwargs))
|
|
trials = runner.get_trials()
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.ERROR)
|
|
self.assertEqual(trials[0].num_failures, 1)
|
|
|
|
def testFailureRecoveryEnabled(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
"checkpoint_freq": 1,
|
|
"max_failures": 1,
|
|
"config": {
|
|
"mock_error": True,
|
|
},
|
|
}
|
|
runner.add_trial(Trial("__fake", **kwargs))
|
|
trials = runner.get_trials()
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
self.assertEqual(trials[0].num_failures, 1)
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
|
|
def testFailureRecoveryMaxFailures(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
"checkpoint_freq": 1,
|
|
"max_failures": 2,
|
|
"config": {
|
|
"mock_error": True,
|
|
"persistent_error": True,
|
|
},
|
|
}
|
|
runner.add_trial(Trial("__fake", **kwargs))
|
|
trials = runner.get_trials()
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
self.assertEqual(trials[0].num_failures, 1)
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
self.assertEqual(trials[0].num_failures, 2)
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.ERROR)
|
|
self.assertEqual(trials[0].num_failures, 3)
|
|
|
|
def testCheckpointing(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 1
|
|
},
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
}
|
|
runner.add_trial(Trial("__fake", **kwargs))
|
|
trials = runner.get_trials()
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
self.assertEqual(ray.get(trials[0].runner.set_info.remote(1)), 1)
|
|
path = runner.trial_executor.save(trials[0])
|
|
kwargs["restore_path"] = path
|
|
|
|
runner.add_trial(Trial("__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].runner.get_info.remote()), 1)
|
|
self.addCleanup(os.remove, path)
|
|
|
|
def testRestoreMetricsAfterCheckpointing(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
}
|
|
runner.add_trial(Trial("__fake", **kwargs))
|
|
trials = runner.get_trials()
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
self.assertEqual(ray.get(trials[0].runner.set_info.remote(1)), 1)
|
|
path = runner.trial_executor.save(trials[0])
|
|
runner.trial_executor.stop_trial(trials[0])
|
|
kwargs["restore_path"] = path
|
|
|
|
runner.add_trial(Trial("__fake", **kwargs))
|
|
trials = runner.get_trials()
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
self.assertEqual(trials[1].status, Trial.RUNNING)
|
|
runner.step()
|
|
self.assertEqual(trials[1].last_result["timesteps_since_restore"], 10)
|
|
self.assertEqual(trials[1].last_result["iterations_since_restore"], 1)
|
|
self.assertGreater(trials[1].last_result["time_since_restore"], 0)
|
|
runner.step()
|
|
self.assertEqual(trials[1].last_result["timesteps_since_restore"], 20)
|
|
self.assertEqual(trials[1].last_result["iterations_since_restore"], 2)
|
|
self.assertGreater(trials[1].last_result["time_since_restore"], 0)
|
|
self.addCleanup(os.remove, path)
|
|
|
|
def testCheckpointingAtEnd(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 2
|
|
},
|
|
"checkpoint_at_end": True,
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
}
|
|
runner.add_trial(Trial("__fake", **kwargs))
|
|
trials = runner.get_trials()
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
runner.step()
|
|
runner.step()
|
|
self.assertEqual(trials[0].last_result[DONE], True)
|
|
self.assertEqual(trials[0].has_checkpoint(), True)
|
|
|
|
def testResultDone(self):
|
|
"""Tests that last_result is marked `done` after trial is complete."""
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 2
|
|
},
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
}
|
|
runner.add_trial(Trial("__fake", **kwargs))
|
|
trials = runner.get_trials()
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
runner.step()
|
|
self.assertNotEqual(trials[0].last_result[DONE], True)
|
|
runner.step()
|
|
self.assertEqual(trials[0].last_result[DONE], True)
|
|
|
|
def testPauseThenResume(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 2
|
|
},
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
}
|
|
runner.add_trial(Trial("__fake", **kwargs))
|
|
trials = runner.get_trials()
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
self.assertEqual(ray.get(trials[0].runner.get_info.remote()), None)
|
|
|
|
self.assertEqual(ray.get(trials[0].runner.set_info.remote(1)), 1)
|
|
|
|
runner.trial_executor.pause_trial(trials[0])
|
|
self.assertEqual(trials[0].status, Trial.PAUSED)
|
|
|
|
runner.trial_executor.resume_trial(trials[0])
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
self.assertEqual(ray.get(trials[0].runner.get_info.remote()), 1)
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
|
|
def testStepHook(self):
|
|
ray.init(num_cpus=4, num_gpus=2)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
|
|
def on_step_begin(self):
|
|
self._update_avail_resources()
|
|
cnt = self.pre_step if hasattr(self, 'pre_step') else 0
|
|
setattr(self, 'pre_step', cnt + 1)
|
|
|
|
def on_step_end(self):
|
|
cnt = self.pre_step if hasattr(self, 'post_step') else 0
|
|
setattr(self, 'post_step', 1 + cnt)
|
|
|
|
import types
|
|
runner.trial_executor.on_step_begin = types.MethodType(
|
|
on_step_begin, runner.trial_executor)
|
|
runner.trial_executor.on_step_end = types.MethodType(
|
|
on_step_end, runner.trial_executor)
|
|
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 5
|
|
},
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
}
|
|
runner.add_trial(Trial("__fake", **kwargs))
|
|
runner.step()
|
|
self.assertEqual(runner.trial_executor.pre_step, 1)
|
|
self.assertEqual(runner.trial_executor.post_step, 1)
|
|
|
|
def testStopTrial(self):
|
|
ray.init(num_cpus=4, num_gpus=2)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 5
|
|
},
|
|
"resources": Resources(cpu=1, gpu=1),
|
|
}
|
|
trials = [
|
|
Trial("__fake", **kwargs),
|
|
Trial("__fake", **kwargs),
|
|
Trial("__fake", **kwargs),
|
|
Trial("__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)
|
|
|
|
# Stop trial while running
|
|
runner.stop_trial(trials[0])
|
|
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(trials[-1].status, Trial.PENDING)
|
|
|
|
# Stop trial while pending
|
|
runner.stop_trial(trials[-1])
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
self.assertEqual(trials[1].status, Trial.RUNNING)
|
|
self.assertEqual(trials[-1].status, Trial.TERMINATED)
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
self.assertEqual(trials[1].status, Trial.RUNNING)
|
|
self.assertEqual(trials[2].status, Trial.RUNNING)
|
|
self.assertEqual(trials[-1].status, Trial.TERMINATED)
|
|
|
|
def testSearchAlgNotification(self):
|
|
"""Checks notification of trial to the Search Algorithm."""
|
|
ray.init(num_cpus=4, num_gpus=2)
|
|
experiment_spec = {"run": "__fake", "stop": {"training_iteration": 2}}
|
|
experiments = [Experiment.from_json("test", experiment_spec)]
|
|
searcher = _MockSuggestionAlgorithm(max_concurrent=10)
|
|
searcher.add_configurations(experiments)
|
|
runner = TrialRunner(search_alg=searcher)
|
|
runner.step()
|
|
trials = runner.get_trials()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
|
|
self.assertEqual(searcher.counter["result"], 1)
|
|
self.assertEqual(searcher.counter["complete"], 1)
|
|
|
|
def testSearchAlgFinished(self):
|
|
"""Checks that SearchAlg is Finished before all trials are done."""
|
|
ray.init(num_cpus=4, num_gpus=2)
|
|
experiment_spec = {"run": "__fake", "stop": {"training_iteration": 1}}
|
|
experiments = [Experiment.from_json("test", experiment_spec)]
|
|
searcher = _MockSuggestionAlgorithm(max_concurrent=10)
|
|
searcher.add_configurations(experiments)
|
|
runner = TrialRunner(search_alg=searcher)
|
|
runner.step()
|
|
trials = runner.get_trials()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
self.assertTrue(searcher.is_finished())
|
|
self.assertFalse(runner.is_finished())
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
self.assertEqual(len(searcher.live_trials), 0)
|
|
self.assertTrue(searcher.is_finished())
|
|
self.assertTrue(runner.is_finished())
|
|
|
|
def testSearchAlgSchedulerInteraction(self):
|
|
"""Checks that TrialScheduler killing trial will notify SearchAlg."""
|
|
|
|
class _MockScheduler(FIFOScheduler):
|
|
def on_trial_result(self, *args, **kwargs):
|
|
return TrialScheduler.STOP
|
|
|
|
ray.init(num_cpus=4, num_gpus=2)
|
|
experiment_spec = {"run": "__fake", "stop": {"training_iteration": 2}}
|
|
experiments = [Experiment.from_json("test", experiment_spec)]
|
|
searcher = _MockSuggestionAlgorithm(max_concurrent=10)
|
|
searcher.add_configurations(experiments)
|
|
runner = TrialRunner(search_alg=searcher, scheduler=_MockScheduler())
|
|
runner.step()
|
|
trials = runner.get_trials()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
self.assertTrue(searcher.is_finished())
|
|
self.assertFalse(runner.is_finished())
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
self.assertEqual(len(searcher.live_trials), 0)
|
|
self.assertTrue(searcher.is_finished())
|
|
self.assertTrue(runner.is_finished())
|
|
|
|
def testSearchAlgStalled(self):
|
|
"""Checks that runner and searcher state is maintained when stalled."""
|
|
ray.init(num_cpus=4, num_gpus=2)
|
|
experiment_spec = {
|
|
"run": "__fake",
|
|
"num_samples": 3,
|
|
"stop": {
|
|
"training_iteration": 1
|
|
}
|
|
}
|
|
experiments = [Experiment.from_json("test", experiment_spec)]
|
|
searcher = _MockSuggestionAlgorithm(max_concurrent=1)
|
|
searcher.add_configurations(experiments)
|
|
runner = TrialRunner(search_alg=searcher)
|
|
runner.step()
|
|
trials = runner.get_trials()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
|
|
trials = runner.get_trials()
|
|
runner.step()
|
|
self.assertEqual(trials[1].status, Trial.RUNNING)
|
|
self.assertEqual(len(searcher.live_trials), 1)
|
|
|
|
searcher.stall = True
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[1].status, Trial.TERMINATED)
|
|
self.assertEqual(len(searcher.live_trials), 0)
|
|
|
|
self.assertTrue(all(trial.is_finished() for trial in trials))
|
|
self.assertFalse(searcher.is_finished())
|
|
self.assertFalse(runner.is_finished())
|
|
|
|
searcher.stall = False
|
|
|
|
runner.step()
|
|
trials = runner.get_trials()
|
|
self.assertEqual(trials[2].status, Trial.RUNNING)
|
|
self.assertEqual(len(searcher.live_trials), 1)
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[2].status, Trial.TERMINATED)
|
|
self.assertEqual(len(searcher.live_trials), 0)
|
|
self.assertTrue(searcher.is_finished())
|
|
self.assertTrue(runner.is_finished())
|
|
|
|
def testSearchAlgFinishes(self):
|
|
"""SearchAlg changing state in `next_trials` does not crash."""
|
|
|
|
class FinishFastAlg(SuggestionAlgorithm):
|
|
def next_trials(self):
|
|
self._finished = True
|
|
return []
|
|
|
|
ray.init(num_cpus=4, num_gpus=2)
|
|
experiment_spec = {
|
|
"run": "__fake",
|
|
"num_samples": 3,
|
|
"stop": {
|
|
"training_iteration": 1
|
|
}
|
|
}
|
|
searcher = FinishFastAlg()
|
|
experiments = [Experiment.from_json("test", experiment_spec)]
|
|
searcher.add_configurations(experiments)
|
|
|
|
runner = TrialRunner(search_alg=searcher)
|
|
runner.step() # This should not fail
|
|
self.assertTrue(searcher.is_finished())
|
|
self.assertTrue(runner.is_finished())
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main(verbosity=2)
|