Files
ray/python/ray/tune/tests/test_actor_reuse.py
T

212 lines
7.6 KiB
Python

import os
import pickle
import unittest
import sys
from collections import defaultdict
import ray
from ray import tune, logger
from ray.tune import Trainable, run_experiments, register_trainable
from ray.tune.error import TuneError
from ray.tune.function_runner import wrap_function
from ray.tune.schedulers.trial_scheduler import FIFOScheduler, TrialScheduler
class FrequentPausesScheduler(FIFOScheduler):
def on_trial_result(self, trial_runner, trial, result):
return TrialScheduler.PAUSE
def create_resettable_class():
class MyResettableClass(Trainable):
def setup(self, config):
self.config = config
self.num_resets = 0
self.iter = 0
self.msg = config.get("message", "No message")
def step(self):
self.iter += 1
print("PRINT_STDOUT: {}".format(self.msg))
print("PRINT_STDERR: {}".format(self.msg), file=sys.stderr)
logger.info("LOG_STDERR: {}".format(self.msg))
return {
"id": self.config["id"],
"num_resets": self.num_resets,
"done": self.iter > 1,
"iter": self.iter
}
def save_checkpoint(self, chkpt_dir):
return {"iter": self.iter}
def load_checkpoint(self, item):
self.iter = item["iter"]
def reset_config(self, new_config):
if "fake_reset_not_supported" in self.config:
return False
self.num_resets += 1
self.msg = new_config.get("message", "No message")
return True
return MyResettableClass
def create_resettable_function(num_resets: defaultdict):
def trainable(config, checkpoint_dir=None):
if checkpoint_dir:
with open(os.path.join(checkpoint_dir, "chkpt"), "rb") as fp:
step = pickle.load(fp)
else:
step = 0
while step < 2:
step += 1
with tune.checkpoint_dir(step) as checkpoint_dir:
with open(os.path.join(checkpoint_dir, "chkpt"), "wb") as fp:
pickle.dump(step, fp)
tune.report(**{
"done": step >= 2,
"iter": step,
"id": config["id"]
})
trainable = wrap_function(trainable)
class ResetCountTrainable(trainable):
def reset_config(self, new_config):
num_resets[self.trial_id] += 1
return super().reset_config(new_config)
return ResetCountTrainable
class ActorReuseTest(unittest.TestCase):
def setUp(self):
ray.init(num_cpus=1, num_gpus=0)
os.environ["TUNE_STATE_REFRESH_PERIOD"] = "0.1"
def tearDown(self):
ray.shutdown()
def _run_trials_with_frequent_pauses(self, trainable, reuse=False):
trials = run_experiments(
{
"foo": {
"run": trainable,
"num_samples": 1,
"config": {
"id": tune.grid_search([0, 1, 2, 3])
},
}
},
reuse_actors=reuse,
scheduler=FrequentPausesScheduler(),
verbose=0)
return trials
def testTrialReuseDisabled(self):
trials = self._run_trials_with_frequent_pauses(
create_resettable_class(), reuse=False)
self.assertEqual([t.last_result["id"] for t in trials], [0, 1, 2, 3])
self.assertEqual([t.last_result["iter"] for t in trials], [2, 2, 2, 2])
self.assertEqual([t.last_result["num_resets"] for t in trials],
[0, 0, 0, 0])
def testTrialReuseDisabledFunction(self):
num_resets = defaultdict(lambda: 0)
trials = self._run_trials_with_frequent_pauses(
create_resettable_function(num_resets), reuse=False)
self.assertEqual([t.last_result["id"] for t in trials], [0, 1, 2, 3])
self.assertEqual([t.last_result["iter"] for t in trials], [2, 2, 2, 2])
self.assertEqual([num_resets[t.trial_id] for t in trials],
[0, 0, 0, 0])
def testTrialReuseEnabled(self):
trials = self._run_trials_with_frequent_pauses(
create_resettable_class(), reuse=True)
self.assertEqual([t.last_result["id"] for t in trials], [0, 1, 2, 3])
self.assertEqual([t.last_result["iter"] for t in trials], [2, 2, 2, 2])
self.assertEqual([t.last_result["num_resets"] for t in trials],
[1, 2, 3, 4])
def testTrialReuseEnabledFunction(self):
num_resets = defaultdict(lambda: 0)
trials = self._run_trials_with_frequent_pauses(
create_resettable_function(num_resets), reuse=True)
self.assertEqual([t.last_result["id"] for t in trials], [0, 1, 2, 3])
self.assertEqual([t.last_result["iter"] for t in trials], [2, 2, 2, 2])
self.assertEqual([num_resets[t.trial_id] for t in trials],
[0, 0, 0, 0])
def testReuseEnabledError(self):
def run():
run_experiments(
{
"foo": {
"run": create_resettable_class(),
"max_failures": 1,
"num_samples": 1,
"config": {
"id": tune.grid_search([0, 1, 2, 3]),
"fake_reset_not_supported": True
},
}
},
reuse_actors=True,
scheduler=FrequentPausesScheduler())
self.assertRaises(TuneError, lambda: run())
def testTrialReuseLogToFile(self):
register_trainable("foo2", create_resettable_class())
# Log to default files
[trial1, trial2] = tune.run(
"foo2",
config={
"message": tune.grid_search(["First", "Second"]),
"id": -1
},
log_to_file=True,
scheduler=FrequentPausesScheduler(),
reuse_actors=True).trials
# Check trial 1
self.assertEqual(trial1.last_result["num_resets"], 1)
self.assertTrue(os.path.exists(os.path.join(trial1.logdir, "stdout")))
self.assertTrue(os.path.exists(os.path.join(trial1.logdir, "stderr")))
with open(os.path.join(trial1.logdir, "stdout"), "rt") as fp:
content = fp.read()
self.assertIn("PRINT_STDOUT: First", content)
self.assertNotIn("PRINT_STDOUT: Second", content)
with open(os.path.join(trial1.logdir, "stderr"), "rt") as fp:
content = fp.read()
self.assertIn("PRINT_STDERR: First", content)
self.assertIn("LOG_STDERR: First", content)
self.assertNotIn("PRINT_STDERR: Second", content)
self.assertNotIn("LOG_STDERR: Second", content)
# Check trial 2
self.assertEqual(trial2.last_result["num_resets"], 2)
self.assertTrue(os.path.exists(os.path.join(trial2.logdir, "stdout")))
self.assertTrue(os.path.exists(os.path.join(trial2.logdir, "stderr")))
with open(os.path.join(trial2.logdir, "stdout"), "rt") as fp:
content = fp.read()
self.assertIn("PRINT_STDOUT: Second", content)
self.assertNotIn("PRINT_STDOUT: First", content)
with open(os.path.join(trial2.logdir, "stderr"), "rt") as fp:
content = fp.read()
self.assertIn("PRINT_STDERR: Second", content)
self.assertIn("LOG_STDERR: Second", content)
self.assertNotIn("PRINT_STDERR: First", content)
self.assertNotIn("LOG_STDERR: First", content)
if __name__ == "__main__":
import pytest
sys.exit(pytest.main(["-v", __file__]))