mirror of
https://github.com/wassname/ray.git
synced 2026-07-06 05:16:30 +08:00
Richard Liaw
2066 lines
67 KiB
Python
2066 lines
67 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import shutil
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import sys
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import tempfile
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import time
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import unittest
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import ray
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from ray.rllib import _register_all
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from ray import tune
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from ray.tune import Trainable, TuneError
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from ray.tune import register_env, register_trainable, run_experiments
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from ray.tune.ray_trial_executor import RayTrialExecutor
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from ray.tune.schedulers import TrialScheduler, FIFOScheduler
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from ray.tune.registry import _global_registry, TRAINABLE_CLASS
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from ray.tune.result import (DEFAULT_RESULTS_DIR, TIMESTEPS_TOTAL, DONE,
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EPISODES_TOTAL, TRAINING_ITERATION)
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from ray.tune.logger import Logger
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from ray.tune.util import pin_in_object_store, get_pinned_object
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from ray.tune.experiment import Experiment
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from ray.tune.trial import (Trial, ExportFormat, Resources, resources_to_json,
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json_to_resources)
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from ray.tune.trial_runner import TrialRunner
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from ray.tune.suggest import grid_search, BasicVariantGenerator
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from ray.tune.suggest.suggestion import (_MockSuggestionAlgorithm,
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SuggestionAlgorithm)
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from ray.tune.suggest.variant_generator import (RecursiveDependencyError,
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resolve_nested_dict)
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if sys.version_info >= (3, 3):
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from unittest.mock import patch
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else:
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from mock import patch
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class TrainableFunctionApiTest(unittest.TestCase):
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def setUp(self):
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ray.init(num_cpus=4, num_gpus=0)
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def tearDown(self):
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ray.shutdown()
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_register_all() # re-register the evicted objects
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def testPinObject(self):
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X = pin_in_object_store("hello")
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@ray.remote
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def f():
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return get_pinned_object(X)
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self.assertEqual(ray.get(f.remote()), "hello")
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def testFetchPinned(self):
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X = pin_in_object_store("hello")
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def train(config, reporter):
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get_pinned_object(X)
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reporter(timesteps_total=100, done=True)
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register_trainable("f1", train)
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[trial] = run_experiments({
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"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 100)
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def testRegisterEnv(self):
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register_env("foo", lambda: None)
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self.assertRaises(TypeError, lambda: register_env("foo", 2))
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def testRegisterEnvOverwrite(self):
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def train(config, reporter):
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reporter(timesteps_total=100, done=True)
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def train2(config, reporter):
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reporter(timesteps_total=200, done=True)
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register_trainable("f1", train)
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register_trainable("f1", train2)
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[trial] = run_experiments({
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"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 200)
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def testRegisterTrainable(self):
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def train(config, reporter):
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pass
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class A(object):
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pass
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class B(Trainable):
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pass
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register_trainable("foo", train)
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register_trainable("foo", B)
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self.assertRaises(TypeError, lambda: register_trainable("foo", B()))
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self.assertRaises(TypeError, lambda: register_trainable("foo", A))
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def testRegisterTrainableCallable(self):
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def dummy_fn(config, reporter, steps):
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reporter(timesteps_total=steps, done=True)
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from functools import partial
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steps = 500
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register_trainable("test", partial(dummy_fn, steps=steps))
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[trial] = run_experiments({
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"foo": {
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"run": "test",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], steps)
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def testBuiltInTrainableResources(self):
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class B(Trainable):
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@classmethod
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def default_resource_request(cls, config):
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return Resources(cpu=config["cpu"], gpu=config["gpu"])
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def _train(self):
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return {"timesteps_this_iter": 1, "done": True}
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register_trainable("B", B)
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def f(cpus, gpus, queue_trials):
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return run_experiments(
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{
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"foo": {
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"run": "B",
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"config": {
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"cpu": cpus,
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"gpu": gpus,
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},
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}
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},
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queue_trials=queue_trials)[0]
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# Should all succeed
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self.assertEqual(f(0, 0, False).status, Trial.TERMINATED)
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self.assertEqual(f(1, 0, True).status, Trial.TERMINATED)
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self.assertEqual(f(1, 0, True).status, Trial.TERMINATED)
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# Infeasible even with queueing enabled (no gpus)
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self.assertRaises(TuneError, lambda: f(1, 1, True))
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# Too large resource request
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self.assertRaises(TuneError, lambda: f(100, 100, False))
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self.assertRaises(TuneError, lambda: f(0, 100, False))
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self.assertRaises(TuneError, lambda: f(100, 0, False))
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# TODO(ekl) how can we test this is queued (hangs)?
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# f(100, 0, True)
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def testRewriteEnv(self):
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def train(config, reporter):
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reporter(timesteps_total=1)
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register_trainable("f1", train)
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[trial] = run_experiments({
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"foo": {
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"run": "f1",
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"env": "CartPole-v0",
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}
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})
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self.assertEqual(trial.config["env"], "CartPole-v0")
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def testConfigPurity(self):
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def train(config, reporter):
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assert config == {"a": "b"}, config
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reporter(timesteps_total=1)
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register_trainable("f1", train)
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run_experiments({
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"foo": {
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"run": "f1",
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"config": {
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"a": "b"
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},
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}
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})
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def testLogdir(self):
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def train(config, reporter):
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assert "/tmp/logdir/foo" in os.getcwd(), os.getcwd()
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reporter(timesteps_total=1)
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register_trainable("f1", train)
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run_experiments({
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"foo": {
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"run": "f1",
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"local_dir": "/tmp/logdir",
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"config": {
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"a": "b"
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},
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}
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})
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def testUploadDirNone(self):
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def train(config, reporter):
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reporter(timesteps_total=1)
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[trial] = run_experiments({
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"foo": {
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"run": train,
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"upload_dir": None,
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"config": {
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"a": "b"
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},
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}
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})
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self.assertFalse(trial.upload_dir)
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def testLogdirStartingWithTilde(self):
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local_dir = '~/ray_results/local_dir'
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def train(config, reporter):
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cwd = os.getcwd()
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assert cwd.startswith(os.path.expanduser(local_dir)), cwd
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assert not cwd.startswith('~'), cwd
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reporter(timesteps_total=1)
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register_trainable('f1', train)
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run_experiments({
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'foo': {
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'run': 'f1',
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'local_dir': local_dir,
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'config': {
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'a': 'b'
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},
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}
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})
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def testLongFilename(self):
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def train(config, reporter):
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assert "/tmp/logdir/foo" in os.getcwd(), os.getcwd()
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reporter(timesteps_total=1)
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register_trainable("f1", train)
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run_experiments({
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"foo": {
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"run": "f1",
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"local_dir": "/tmp/logdir",
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"config": {
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"a" * 50: tune.sample_from(lambda spec: 5.0 / 7),
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"b" * 50: tune.sample_from(lambda spec: "long" * 40),
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},
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}
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})
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def testBadParams(self):
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def f():
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run_experiments({"foo": {}})
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self.assertRaises(TuneError, f)
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def testBadParams2(self):
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def f():
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run_experiments({
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"foo": {
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"run": "asdf",
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"bah": "this param is not allowed",
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}
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})
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self.assertRaises(TuneError, f)
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def testBadParams3(self):
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def f():
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run_experiments({
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"foo": {
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"run": grid_search("invalid grid search"),
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}
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})
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self.assertRaises(TuneError, f)
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def testBadParams4(self):
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def f():
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run_experiments({
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"foo": {
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"run": "asdf",
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}
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})
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self.assertRaises(TuneError, f)
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def testBadParams5(self):
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def f():
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run_experiments({"foo": {"run": "PPO", "stop": {"asdf": 1}}})
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self.assertRaises(TuneError, f)
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def testBadParams6(self):
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def f():
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run_experiments({
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"foo": {
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"run": "PPO",
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"resources_per_trial": {
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"asdf": 1
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}
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}
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})
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self.assertRaises(TuneError, f)
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def testBadStoppingReturn(self):
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def train(config, reporter):
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reporter()
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register_trainable("f1", train)
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def f():
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run_experiments({
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"foo": {
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"run": "f1",
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"stop": {
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"time": 10
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},
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertRaises(TuneError, f)
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def testEarlyReturn(self):
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def train(config, reporter):
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reporter(timesteps_total=100, done=True)
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time.sleep(99999)
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register_trainable("f1", train)
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[trial] = run_experiments({
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"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 100)
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def testAbruptReturn(self):
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def train(config, reporter):
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reporter(timesteps_total=100)
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register_trainable("f1", train)
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[trial] = run_experiments({
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"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 100)
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def testErrorReturn(self):
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def train(config, reporter):
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raise Exception("uh oh")
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register_trainable("f1", train)
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def f():
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run_experiments({
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"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertRaises(TuneError, f)
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def testSuccess(self):
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def train(config, reporter):
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for i in range(100):
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reporter(timesteps_total=i)
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register_trainable("f1", train)
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[trial] = run_experiments({
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"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 99)
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def testNoRaiseFlag(self):
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def train(config, reporter):
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# Finish this trial without any metric,
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# which leads to a failed trial
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return
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register_trainable("f1", train)
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[trial] = run_experiments(
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{
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"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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},
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raise_on_failed_trial=False)
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self.assertEqual(trial.status, Trial.ERROR)
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def testReportInfinity(self):
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def train(config, reporter):
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for i in range(100):
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reporter(mean_accuracy=float('inf'))
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register_trainable("f1", train)
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[trial] = run_experiments({
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"foo": {
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"run": "f1",
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertEqual(trial.last_result['mean_accuracy'], float('inf'))
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def testReportTimeStep(self):
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def train(config, reporter):
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for i in range(100):
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reporter(mean_accuracy=5)
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[trial] = run_experiments({
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"foo": {
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"run": train,
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertIsNone(trial.last_result[TIMESTEPS_TOTAL])
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def train2(config, reporter):
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for i in range(10):
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reporter(timesteps_total=5)
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[trial2] = run_experiments({
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"foo": {
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"run": train2,
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertEqual(trial2.last_result[TIMESTEPS_TOTAL], 5)
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self.assertEqual(trial2.last_result["timesteps_this_iter"], 0)
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def train3(config, reporter):
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for i in range(10):
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reporter(timesteps_this_iter=0, episodes_this_iter=0)
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[trial3] = run_experiments({
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"foo": {
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"run": train3,
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"config": {
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"script_min_iter_time_s": 0,
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},
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}
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})
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self.assertEqual(trial3.last_result[TIMESTEPS_TOTAL], 0)
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self.assertEqual(trial3.last_result[EPISODES_TOTAL], 0)
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def testCheckpointDict(self):
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class TestTrain(Trainable):
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def _setup(self, config):
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self.state = {"hi": 1}
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def _train(self):
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return {"timesteps_this_iter": 1, "done": True}
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def _save(self, path):
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return self.state
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def _restore(self, state):
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self.state = state
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test_trainable = TestTrain()
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result = test_trainable.save()
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test_trainable.state["hi"] = 2
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test_trainable.restore(result)
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self.assertEqual(test_trainable.state["hi"], 1)
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trials = run_experiments({
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"foo": {
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"run": TestTrain,
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"checkpoint_at_end": True
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}
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})
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for trial in trials:
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertTrue(trial.has_checkpoint())
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def testMultipleCheckpoints(self):
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class TestTrain(Trainable):
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def _setup(self, config):
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self.state = {"hi": 1, "iter": 0}
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def _train(self):
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self.state["iter"] += 1
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return {"timesteps_this_iter": 1, "done": True}
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def _save(self, path):
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return self.state
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def _restore(self, state):
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self.state = state
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test_trainable = TestTrain()
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checkpoint_1 = test_trainable.save()
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test_trainable.train()
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checkpoint_2 = test_trainable.save()
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self.assertNotEqual(checkpoint_1, checkpoint_2)
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test_trainable.restore(checkpoint_2)
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self.assertEqual(test_trainable.state["iter"], 1)
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test_trainable.restore(checkpoint_1)
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self.assertEqual(test_trainable.state["iter"], 0)
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trials = run_experiments({
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"foo": {
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"run": TestTrain,
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"checkpoint_at_end": True
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}
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})
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for trial in trials:
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self.assertEqual(trial.status, Trial.TERMINATED)
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self.assertTrue(trial.has_checkpoint())
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def testIterationCounter(self):
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def train(config, reporter):
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for i in range(100):
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reporter(itr=i, done=i == 99)
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register_trainable("exp", train)
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config = {
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"my_exp": {
|
|
"run": "exp",
|
|
"config": {
|
|
"iterations": 100,
|
|
},
|
|
"stop": {
|
|
"timesteps_total": 100
|
|
},
|
|
}
|
|
}
|
|
[trial] = run_experiments(config)
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertEqual(trial.last_result[TRAINING_ITERATION], 100)
|
|
self.assertEqual(trial.last_result["itr"], 99)
|
|
|
|
|
|
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 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):
|
|
checkpoint = path + "/checkpoint"
|
|
with open(checkpoint, "w") as f:
|
|
f.write("OK")
|
|
return checkpoint
|
|
|
|
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())
|
|
|
|
def testExportFormats(self):
|
|
class train(Trainable):
|
|
def _train(self):
|
|
return {"timesteps_this_iter": 1, "done": True}
|
|
|
|
def _export_model(self, export_formats, export_dir):
|
|
path = export_dir + "/exported"
|
|
with open(path, "w") as f:
|
|
f.write("OK")
|
|
return {export_formats[0]: path}
|
|
|
|
trials = run_experiments({
|
|
"foo": {
|
|
"run": train,
|
|
"export_formats": ["format"]
|
|
}
|
|
})
|
|
for trial in trials:
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
self.assertTrue(
|
|
os.path.exists(os.path.join(trial.logdir, "exported")))
|
|
|
|
def testInvalidExportFormats(self):
|
|
class train(Trainable):
|
|
def _train(self):
|
|
return {"timesteps_this_iter": 1, "done": True}
|
|
|
|
def _export_model(self, export_formats, export_dir):
|
|
ExportFormat.validate(export_formats)
|
|
return {}
|
|
|
|
def fail_trial():
|
|
run_experiments({
|
|
"foo": {
|
|
"run": train,
|
|
"export_formats": ["format"]
|
|
}
|
|
})
|
|
|
|
self.assertRaises(TuneError, fail_trial)
|
|
|
|
def testDeprecatedResources(self):
|
|
class train(Trainable):
|
|
def _train(self):
|
|
return {"timesteps_this_iter": 1, "done": True}
|
|
|
|
trials = run_experiments({
|
|
"foo": {
|
|
"run": train,
|
|
"trial_resources": {
|
|
"cpu": 1
|
|
}
|
|
}
|
|
})
|
|
for trial in trials:
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
|
|
def testCustomResources(self):
|
|
ray.shutdown()
|
|
ray.init(resources={"hi": 3})
|
|
|
|
class train(Trainable):
|
|
def _train(self):
|
|
return {"timesteps_this_iter": 1, "done": True}
|
|
|
|
trials = run_experiments({
|
|
"foo": {
|
|
"run": train,
|
|
"resources_per_trial": {
|
|
"cpu": 1,
|
|
"custom_resources": {
|
|
"hi": 2
|
|
}
|
|
}
|
|
}
|
|
})
|
|
for trial in trials:
|
|
self.assertEqual(trial.status, Trial.TERMINATED)
|
|
|
|
def testCustomLogger(self):
|
|
class CustomLogger(Logger):
|
|
def on_result(self, result):
|
|
with open(os.path.join(self.logdir, "test.log"), "w") as f:
|
|
f.write("hi")
|
|
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "__fake",
|
|
"stop": {
|
|
"training_iteration": 1
|
|
},
|
|
"loggers": [CustomLogger]
|
|
}
|
|
})
|
|
self.assertTrue(os.path.exists(os.path.join(trial.logdir, "test.log")))
|
|
self.assertFalse(
|
|
os.path.exists(os.path.join(trial.logdir, "params.json")))
|
|
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "__fake",
|
|
"stop": {
|
|
"training_iteration": 1
|
|
}
|
|
}
|
|
})
|
|
self.assertTrue(
|
|
os.path.exists(os.path.join(trial.logdir, "params.json")))
|
|
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "__fake",
|
|
"stop": {
|
|
"training_iteration": 1
|
|
},
|
|
"loggers": []
|
|
}
|
|
})
|
|
self.assertFalse(
|
|
os.path.exists(os.path.join(trial.logdir, "params.json")))
|
|
|
|
def testCustomTrialString(self):
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "__fake",
|
|
"stop": {
|
|
"training_iteration": 1
|
|
},
|
|
"trial_name_creator": tune.function(
|
|
lambda t: "{}_{}_321".format(t.trainable_name, t.trial_id))
|
|
}
|
|
})
|
|
self.assertEquals(
|
|
str(trial), "{}_{}_321".format(trial.trainable_name,
|
|
trial.trial_id))
|
|
|
|
def testSyncFunction(self):
|
|
def fail_sync_local():
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "__fake",
|
|
"stop": {
|
|
"training_iteration": 1
|
|
},
|
|
"upload_dir": "test",
|
|
"sync_function": "ls {remote_dir}"
|
|
}
|
|
})
|
|
|
|
self.assertRaises(AssertionError, fail_sync_local)
|
|
|
|
def fail_sync_remote():
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "__fake",
|
|
"stop": {
|
|
"training_iteration": 1
|
|
},
|
|
"upload_dir": "test",
|
|
"sync_function": "ls {local_dir}"
|
|
}
|
|
})
|
|
|
|
self.assertRaises(AssertionError, fail_sync_remote)
|
|
|
|
def sync_func(local, remote):
|
|
with open(os.path.join(local, "test.log"), "w") as f:
|
|
f.write(remote)
|
|
|
|
[trial] = run_experiments({
|
|
"foo": {
|
|
"run": "__fake",
|
|
"stop": {
|
|
"training_iteration": 1
|
|
},
|
|
"upload_dir": "test",
|
|
"sync_function": tune.function(sync_func)
|
|
}
|
|
})
|
|
self.assertTrue(os.path.exists(os.path.join(trial.logdir, "test.log")))
|
|
|
|
|
|
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": tune.sample_from(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": tune.sample_from(lambda spec: spec.config.x + 1),
|
|
"z": tune.sample_from(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": tune.sample_from(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([
|
|
tune.sample_from(lambda spec: spec.config.y * 100),
|
|
tune.sample_from(lambda spec: spec.config.y * 200)
|
|
]),
|
|
"y": tune.sample_from(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 test_resolve_dict(self):
|
|
config = {
|
|
"a": {
|
|
"b": 1,
|
|
"c": 2,
|
|
},
|
|
"b": {
|
|
"a": 3
|
|
}
|
|
}
|
|
resolved = resolve_nested_dict(config)
|
|
for k, v in [(("a", "b"), 1), (("a", "c"), 2), (("b", "a"), 3)]:
|
|
self.assertEqual(resolved.get(k), v)
|
|
|
|
def testRecursiveDep(self):
|
|
try:
|
|
list(
|
|
self.generate_trials({
|
|
"run": "PPO",
|
|
"config": {
|
|
"foo": tune.sample_from(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)
|
|
|
|
|
|
def create_mock_components():
|
|
class _MockScheduler(FIFOScheduler):
|
|
errored_trials = []
|
|
|
|
def on_trial_error(self, trial_runner, trial):
|
|
self.errored_trials += [trial]
|
|
|
|
class _MockSearchAlg(BasicVariantGenerator):
|
|
errored_trials = []
|
|
|
|
def on_trial_complete(self, trial_id, error=False, **kwargs):
|
|
if error:
|
|
self.errored_trials += [trial_id]
|
|
|
|
searchalg = _MockSearchAlg()
|
|
scheduler = _MockScheduler()
|
|
return searchalg, scheduler
|
|
|
|
|
|
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: tune.sample_from(lambda spec: 5.0 / 7),
|
|
"b" * 50: tune.sample_from(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 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 testCustomResources(self):
|
|
ray.init(num_cpus=4, num_gpus=2, resources={"a": 2})
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 1
|
|
},
|
|
"resources": Resources(cpu=1, gpu=0, custom_resources={"a": 2}),
|
|
}
|
|
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 testExtraCustomResources(self):
|
|
ray.init(num_cpus=4, num_gpus=2, resources={"a": 2})
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 1
|
|
},
|
|
"resources": Resources(
|
|
cpu=1, gpu=0, extra_custom_resources={"a": 2}),
|
|
}
|
|
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.assertTrue(sum(t.status == Trial.RUNNING for t in trials) < 2)
|
|
self.assertEqual(trials[0].status, Trial.TERMINATED)
|
|
self.assertEqual(trials[1].status, Trial.PENDING)
|
|
|
|
def testCustomResources2(self):
|
|
ray.init(num_cpus=4, num_gpus=2, resources={"a": 2})
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
resource1 = Resources(cpu=1, gpu=0, extra_custom_resources={"a": 2})
|
|
self.assertTrue(runner.has_resources(resource1))
|
|
resource2 = Resources(cpu=1, gpu=0, custom_resources={"a": 2})
|
|
self.assertTrue(runner.has_resources(resource2))
|
|
resource3 = Resources(cpu=1, gpu=0, custom_resources={"a": 3})
|
|
self.assertFalse(runner.has_resources(resource3))
|
|
resource4 = Resources(cpu=1, gpu=0, extra_custom_resources={"a": 3})
|
|
self.assertFalse(runner.has_resources(resource4))
|
|
|
|
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 testMultiStepRun2(self):
|
|
"""Checks that runner.step throws when overstepping."""
|
|
ray.init(num_cpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
kwargs = {
|
|
"stopping_criterion": {
|
|
"training_iteration": 2
|
|
},
|
|
"resources": Resources(cpu=1, gpu=0),
|
|
}
|
|
trials = [Trial("__fake", **kwargs)]
|
|
for t in trials:
|
|
runner.add_trial(t)
|
|
|
|
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.TERMINATED)
|
|
self.assertRaises(TuneError, runner.step)
|
|
|
|
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 testThrowOnOverstep(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
runner = TrialRunner(BasicVariantGenerator())
|
|
runner.step()
|
|
self.assertRaises(TuneError, runner.step)
|
|
|
|
def testFailureRecoveryDisabled(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
searchalg, scheduler = create_mock_components()
|
|
|
|
runner = TrialRunner(searchalg, scheduler=scheduler)
|
|
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)
|
|
self.assertEqual(len(searchalg.errored_trials), 1)
|
|
self.assertEqual(len(scheduler.errored_trials), 1)
|
|
|
|
def testFailureRecoveryEnabled(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
searchalg, scheduler = create_mock_components()
|
|
|
|
runner = TrialRunner(searchalg, scheduler=scheduler)
|
|
|
|
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)
|
|
self.assertEqual(len(searchalg.errored_trials), 0)
|
|
self.assertEqual(len(scheduler.errored_trials), 0)
|
|
|
|
def testFailureRecoveryNodeRemoval(self):
|
|
ray.init(num_cpus=1, num_gpus=1)
|
|
searchalg, scheduler = create_mock_components()
|
|
|
|
runner = TrialRunner(searchalg, scheduler=scheduler)
|
|
|
|
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()
|
|
|
|
with patch('ray.global_state.cluster_resources') as resource_mock:
|
|
resource_mock.return_value = {"CPU": 1, "GPU": 1}
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.RUNNING)
|
|
|
|
# Mimic a node failure
|
|
resource_mock.return_value = {"CPU": 0, "GPU": 0}
|
|
runner.step()
|
|
self.assertEqual(trials[0].status, Trial.PENDING)
|
|
self.assertEqual(trials[0].num_failures, 1)
|
|
self.assertEqual(len(searchalg.errored_trials), 0)
|
|
self.assertEqual(len(scheduler.errored_trials), 1)
|
|
|
|
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):
|
|
"""Empty SearchAlg changing state in `next_trials` does not crash."""
|
|
|
|
class FinishFastAlg(SuggestionAlgorithm):
|
|
_index = 0
|
|
|
|
def next_trials(self):
|
|
trials = []
|
|
self._index += 1
|
|
|
|
for trial in self._trial_generator:
|
|
trials += [trial]
|
|
break
|
|
|
|
if self._index > 4:
|
|
self._finished = True
|
|
return trials
|
|
|
|
def _suggest(self, trial_id):
|
|
return {}
|
|
|
|
ray.init(num_cpus=2)
|
|
experiment_spec = {
|
|
"run": "__fake",
|
|
"num_samples": 2,
|
|
"stop": {
|
|
"training_iteration": 1
|
|
}
|
|
}
|
|
searcher = FinishFastAlg()
|
|
experiments = [Experiment.from_json("test", experiment_spec)]
|
|
searcher.add_configurations(experiments)
|
|
|
|
runner = TrialRunner(search_alg=searcher)
|
|
self.assertFalse(runner.is_finished())
|
|
runner.step() # This launches a new run
|
|
runner.step() # This launches a 2nd run
|
|
self.assertFalse(searcher.is_finished())
|
|
self.assertFalse(runner.is_finished())
|
|
runner.step() # This kills the first run
|
|
self.assertFalse(searcher.is_finished())
|
|
self.assertFalse(runner.is_finished())
|
|
runner.step() # This kills the 2nd run
|
|
self.assertFalse(searcher.is_finished())
|
|
self.assertFalse(runner.is_finished())
|
|
runner.step() # this converts self._finished to True
|
|
self.assertTrue(searcher.is_finished())
|
|
self.assertRaises(TuneError, runner.step)
|
|
|
|
def testTrialSaveRestore(self):
|
|
"""Creates different trials to test runner.checkpoint/restore."""
|
|
ray.init(num_cpus=3)
|
|
tmpdir = tempfile.mkdtemp()
|
|
|
|
runner = TrialRunner(
|
|
BasicVariantGenerator(), metadata_checkpoint_dir=tmpdir)
|
|
trials = [
|
|
Trial(
|
|
"__fake",
|
|
trial_id="trial_terminate",
|
|
stopping_criterion={"training_iteration": 1},
|
|
checkpoint_freq=1)
|
|
]
|
|
runner.add_trial(trials[0])
|
|
runner.step() # start
|
|
runner.step()
|
|
self.assertEquals(trials[0].status, Trial.TERMINATED)
|
|
|
|
trials += [
|
|
Trial(
|
|
"__fake",
|
|
trial_id="trial_fail",
|
|
stopping_criterion={"training_iteration": 3},
|
|
checkpoint_freq=1,
|
|
config={"mock_error": True})
|
|
]
|
|
runner.add_trial(trials[1])
|
|
runner.step()
|
|
runner.step()
|
|
runner.step()
|
|
self.assertEquals(trials[1].status, Trial.ERROR)
|
|
|
|
trials += [
|
|
Trial(
|
|
"__fake",
|
|
trial_id="trial_succ",
|
|
stopping_criterion={"training_iteration": 2},
|
|
checkpoint_freq=1)
|
|
]
|
|
runner.add_trial(trials[2])
|
|
runner.step()
|
|
self.assertEquals(len(runner.trial_executor.get_checkpoints()), 3)
|
|
self.assertEquals(trials[2].status, Trial.RUNNING)
|
|
|
|
runner2 = TrialRunner.restore(tmpdir)
|
|
for tid in ["trial_terminate", "trial_fail"]:
|
|
original_trial = runner.get_trial(tid)
|
|
restored_trial = runner2.get_trial(tid)
|
|
self.assertEqual(original_trial.status, restored_trial.status)
|
|
|
|
restored_trial = runner2.get_trial("trial_succ")
|
|
self.assertEqual(Trial.PENDING, restored_trial.status)
|
|
|
|
runner2.step()
|
|
runner2.step()
|
|
runner2.step()
|
|
self.assertRaises(TuneError, runner2.step)
|
|
shutil.rmtree(tmpdir)
|
|
|
|
def testTrialNoSave(self):
|
|
"""Check that non-checkpointing trials are not saved."""
|
|
ray.init(num_cpus=3)
|
|
tmpdir = tempfile.mkdtemp()
|
|
|
|
runner = TrialRunner(
|
|
BasicVariantGenerator(), metadata_checkpoint_dir=tmpdir)
|
|
|
|
runner.add_trial(
|
|
Trial(
|
|
"__fake",
|
|
trial_id="non_checkpoint",
|
|
stopping_criterion={"training_iteration": 2}))
|
|
|
|
while not all(t.status == Trial.TERMINATED
|
|
for t in runner.get_trials()):
|
|
runner.step()
|
|
|
|
runner.add_trial(
|
|
Trial(
|
|
"__fake",
|
|
trial_id="checkpoint",
|
|
checkpoint_at_end=True,
|
|
stopping_criterion={"training_iteration": 2}))
|
|
|
|
while not all(t.status == Trial.TERMINATED
|
|
for t in runner.get_trials()):
|
|
runner.step()
|
|
|
|
runner.add_trial(
|
|
Trial(
|
|
"__fake",
|
|
trial_id="pending",
|
|
stopping_criterion={"training_iteration": 2}))
|
|
|
|
runner.step()
|
|
runner.step()
|
|
|
|
runner2 = TrialRunner.restore(tmpdir)
|
|
new_trials = runner2.get_trials()
|
|
self.assertEquals(len(new_trials), 3)
|
|
self.assertTrue(
|
|
runner2.get_trial("non_checkpoint").status == Trial.TERMINATED)
|
|
self.assertTrue(
|
|
runner2.get_trial("checkpoint").status == Trial.TERMINATED)
|
|
self.assertTrue(runner2.get_trial("pending").status == Trial.PENDING)
|
|
self.assertTrue(runner2.get_trial("pending").last_result is None)
|
|
runner2.step()
|
|
shutil.rmtree(tmpdir)
|
|
|
|
def testCheckpointWithFunction(self):
|
|
ray.init()
|
|
trial = Trial(
|
|
"__fake",
|
|
config={
|
|
"callbacks": {
|
|
"on_episode_start": tune.function(lambda i: i),
|
|
}
|
|
},
|
|
checkpoint_freq=1)
|
|
tmpdir = tempfile.mkdtemp()
|
|
runner = TrialRunner(
|
|
BasicVariantGenerator(), metadata_checkpoint_dir=tmpdir)
|
|
runner.add_trial(trial)
|
|
for i in range(5):
|
|
runner.step()
|
|
# force checkpoint
|
|
runner.checkpoint()
|
|
runner2 = TrialRunner.restore(tmpdir)
|
|
new_trial = runner2.get_trials()[0]
|
|
self.assertTrue("callbacks" in new_trial.config)
|
|
self.assertTrue("on_episode_start" in new_trial.config["callbacks"])
|
|
shutil.rmtree(tmpdir)
|
|
|
|
def testCheckpointOverwrite(self):
|
|
def count_checkpoints(cdir):
|
|
return sum((fname.startswith("experiment_state")
|
|
and fname.endswith(".json"))
|
|
for fname in os.listdir(cdir))
|
|
|
|
ray.init()
|
|
trial = Trial("__fake", checkpoint_freq=1)
|
|
tmpdir = tempfile.mkdtemp()
|
|
runner = TrialRunner(
|
|
BasicVariantGenerator(), metadata_checkpoint_dir=tmpdir)
|
|
runner.add_trial(trial)
|
|
for i in range(5):
|
|
runner.step()
|
|
# force checkpoint
|
|
runner.checkpoint()
|
|
self.assertEquals(count_checkpoints(tmpdir), 1)
|
|
|
|
runner2 = TrialRunner.restore(tmpdir)
|
|
for i in range(5):
|
|
runner2.step()
|
|
self.assertEquals(count_checkpoints(tmpdir), 2)
|
|
|
|
runner2.checkpoint()
|
|
self.assertEquals(count_checkpoints(tmpdir), 2)
|
|
shutil.rmtree(tmpdir)
|
|
|
|
|
|
class SearchAlgorithmTest(unittest.TestCase):
|
|
def testNestedSuggestion(self):
|
|
class TestSuggestion(SuggestionAlgorithm):
|
|
def _suggest(self, trial_id):
|
|
return {"a": {"b": {"c": {"d": 4, "e": 5}}}}
|
|
|
|
alg = TestSuggestion()
|
|
alg.add_configurations({"test": {"run": "__fake"}})
|
|
trial = alg.next_trials()[0]
|
|
self.assertTrue("e=5" in trial.experiment_tag)
|
|
self.assertTrue("d=4" in trial.experiment_tag)
|
|
|
|
|
|
class ResourcesTest(unittest.TestCase):
|
|
def testSubtraction(self):
|
|
resource_1 = Resources(
|
|
1,
|
|
0,
|
|
0,
|
|
1,
|
|
custom_resources={
|
|
"a": 1,
|
|
"b": 2
|
|
},
|
|
extra_custom_resources={
|
|
"a": 1,
|
|
"b": 1
|
|
})
|
|
resource_2 = Resources(
|
|
1,
|
|
0,
|
|
0,
|
|
1,
|
|
custom_resources={
|
|
"a": 1,
|
|
"b": 2
|
|
},
|
|
extra_custom_resources={
|
|
"a": 1,
|
|
"b": 1
|
|
})
|
|
new_res = Resources.subtract(resource_1, resource_2)
|
|
self.assertTrue(new_res.cpu == 0)
|
|
self.assertTrue(new_res.gpu == 0)
|
|
self.assertTrue(new_res.extra_cpu == 0)
|
|
self.assertTrue(new_res.extra_gpu == 0)
|
|
self.assertTrue(all(k == 0 for k in new_res.custom_resources.values()))
|
|
self.assertTrue(
|
|
all(k == 0 for k in new_res.extra_custom_resources.values()))
|
|
|
|
def testDifferentResources(self):
|
|
resource_1 = Resources(1, 0, 0, 1, custom_resources={"a": 1, "b": 2})
|
|
resource_2 = Resources(1, 0, 0, 1, custom_resources={"a": 1, "c": 2})
|
|
new_res = Resources.subtract(resource_1, resource_2)
|
|
assert "c" in new_res.custom_resources
|
|
assert "b" in new_res.custom_resources
|
|
self.assertTrue(new_res.cpu == 0)
|
|
self.assertTrue(new_res.gpu == 0)
|
|
self.assertTrue(new_res.extra_cpu == 0)
|
|
self.assertTrue(new_res.extra_gpu == 0)
|
|
self.assertTrue(new_res.get("a") == 0)
|
|
|
|
def testSerialization(self):
|
|
original = Resources(1, 0, 0, 1, custom_resources={"a": 1, "b": 2})
|
|
jsoned = resources_to_json(original)
|
|
new_resource = json_to_resources(jsoned)
|
|
self.assertEquals(original, new_resource)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main(verbosity=2)
|