[tune] Auto-init Ray + default SearchAlg (#4815)

python/ray/tune/ray_trial_executor.py

@@ -39,6 +39,7 @@ class RayTrialExecutor(TrialExecutor):
     def __init__(self,
                  queue_trials=False,
                  reuse_actors=False,
+                 ray_auto_init=False,
                  refresh_period=RESOURCE_REFRESH_PERIOD):
         super(RayTrialExecutor, self).__init__(queue_trials)
         self._running = {}
@@ -55,6 +56,12 @@ class RayTrialExecutor(TrialExecutor):
         self._refresh_period = refresh_period
         self._last_resource_refresh = float("-inf")
         self._last_nontrivial_wait = time.time()
+        if not ray.is_initialized() and ray_auto_init:
+            logger.info("Initializing Ray automatically."
+                        "For cluster usage or custom Ray initialization, "
+                        "call `ray.init(...)` before `tune.run`.")
+            ray.init()
+
         if ray.is_initialized():
             self._update_avail_resources()
 

python/ray/tune/tests/test_trial_runner.py

@@ -1218,7 +1218,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testExtraResources(self):
         ray.init(num_cpus=4, num_gpus=2)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 1
@@ -1239,7 +1239,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testCustomResources(self):
         ray.init(num_cpus=4, num_gpus=2, resources={"a": 2})
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 1
@@ -1260,7 +1260,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testExtraCustomResources(self):
         ray.init(num_cpus=4, num_gpus=2, resources={"a": 2})
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 1
@@ -1283,7 +1283,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testCustomResources2(self):
         ray.init(num_cpus=4, num_gpus=2, resources={"a": 2})
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         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})
@@ -1295,7 +1295,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testFractionalGpus(self):
         ray.init(num_cpus=4, num_gpus=1)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "resources": Resources(cpu=1, gpu=0.5),
         }
@@ -1318,7 +1318,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testResourceScheduler(self):
         ray.init(num_cpus=4, num_gpus=1)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 1
@@ -1347,7 +1347,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testMultiStepRun(self):
         ray.init(num_cpus=4, num_gpus=2)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 5
@@ -1377,7 +1377,7 @@ class TrialRunnerTest(unittest.TestCase):
     def testMultiStepRun2(self):
         """Checks that runner.step throws when overstepping."""
         ray.init(num_cpus=1)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 2
@@ -1411,8 +1411,7 @@ class TrialRunnerTest(unittest.TestCase):
                     executor.start_trial(trial)
                 return TrialScheduler.CONTINUE
 
-        runner = TrialRunner(
-            BasicVariantGenerator(), scheduler=ChangingScheduler())
+        runner = TrialRunner(scheduler=ChangingScheduler())
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 2
@@ -1434,7 +1433,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testErrorHandling(self):
         ray.init(num_cpus=4, num_gpus=2)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 1
@@ -1456,7 +1455,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testThrowOnOverstep(self):
         ray.init(num_cpus=1, num_gpus=1)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         runner.step()
         self.assertRaises(TuneError, runner.step)
 
@@ -1550,7 +1549,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testFailureRecoveryMaxFailures(self):
         ray.init(num_cpus=1, num_gpus=1)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "resources": Resources(cpu=1, gpu=1),
             "checkpoint_freq": 1,
@@ -1579,7 +1578,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testCheckpointing(self):
         ray.init(num_cpus=1, num_gpus=1)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 1
@@ -1610,7 +1609,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testRestoreMetricsAfterCheckpointing(self):
         ray.init(num_cpus=1, num_gpus=1)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "resources": Resources(cpu=1, gpu=1),
         }
@@ -1642,7 +1641,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testCheckpointingAtEnd(self):
         ray.init(num_cpus=1, num_gpus=1)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 2
@@ -1663,7 +1662,7 @@ class TrialRunnerTest(unittest.TestCase):
     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())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 2
@@ -1682,7 +1681,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testPauseThenResume(self):
         ray.init(num_cpus=1, num_gpus=1)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 2
@@ -1713,7 +1712,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testStepHook(self):
         ray.init(num_cpus=4, num_gpus=2)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
 
         def on_step_begin(self):
             self._update_avail_resources()
@@ -1743,7 +1742,7 @@ class TrialRunnerTest(unittest.TestCase):
 
     def testStopTrial(self):
         ray.init(num_cpus=4, num_gpus=2)
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         kwargs = {
             "stopping_criterion": {
                 "training_iteration": 5
@@ -1953,8 +1952,7 @@ class TrialRunnerTest(unittest.TestCase):
         ray.init(num_cpus=3)
         tmpdir = tempfile.mkdtemp()
 
-        runner = TrialRunner(
-            BasicVariantGenerator(), metadata_checkpoint_dir=tmpdir)
+        runner = TrialRunner(metadata_checkpoint_dir=tmpdir)
         trials = [
             Trial(
                 "__fake",
@@ -2013,8 +2011,7 @@ class TrialRunnerTest(unittest.TestCase):
         ray.init(num_cpus=3)
         tmpdir = tempfile.mkdtemp()
 
-        runner = TrialRunner(
-            BasicVariantGenerator(), metadata_checkpoint_dir=tmpdir)
+        runner = TrialRunner(metadata_checkpoint_dir=tmpdir)
 
         runner.add_trial(
             Trial(
@@ -2069,8 +2066,7 @@ class TrialRunnerTest(unittest.TestCase):
             },
             checkpoint_freq=1)
         tmpdir = tempfile.mkdtemp()
-        runner = TrialRunner(
-            BasicVariantGenerator(), metadata_checkpoint_dir=tmpdir)
+        runner = TrialRunner(metadata_checkpoint_dir=tmpdir)
         runner.add_trial(trial)
         for i in range(5):
             runner.step()
@@ -2091,8 +2087,7 @@ class TrialRunnerTest(unittest.TestCase):
         ray.init()
         trial = Trial("__fake", checkpoint_freq=1)
         tmpdir = tempfile.mkdtemp()
-        runner = TrialRunner(
-            BasicVariantGenerator(), metadata_checkpoint_dir=tmpdir)
+        runner = TrialRunner(metadata_checkpoint_dir=tmpdir)
         runner.add_trial(trial)
         for i in range(5):
             runner.step()

python/ray/tune/tests/test_tune_restore.py

@@ -53,5 +53,20 @@ class TuneRestoreTest(unittest.TestCase):
         )
 
 
+class AutoInitTest(unittest.TestCase):
+    def testTuneRestore(self):
+        self.assertFalse(ray.is_initialized())
+        tune.run(
+            "__fake",
+            name="TestAutoInit",
+            stop={"training_iteration": 1},
+            ray_auto_init=True)
+        self.assertTrue(ray.is_initialized())
+
+    def tearDown(self):
+        ray.shutdown()
+        _register_all()
+
+
 if __name__ == "__main__":
     unittest.main(verbosity=2)

python/ray/tune/tests/test_tune_server.py

@@ -12,7 +12,6 @@ from ray import tune
 from ray.rllib import _register_all
 from ray.tune.trial import Trial, Resources
 from ray.tune.web_server import TuneClient
-from ray.tune.suggest import BasicVariantGenerator
 from ray.tune.trial_runner import TrialRunner
 
 
@@ -34,8 +33,7 @@ class TuneServerSuite(unittest.TestCase):
     def basicSetup(self):
         ray.init(num_cpus=4, num_gpus=1)
         port = get_valid_port()
-        self.runner = TrialRunner(
-            BasicVariantGenerator(), launch_web_server=True, server_port=port)
+        self.runner = TrialRunner(launch_web_server=True, server_port=port)
         runner = self.runner
         kwargs = {
             "stopping_criterion": {

python/ray/tune/trial_runner.py

@@ -18,6 +18,7 @@ from ray.tune.result import TIME_THIS_ITER_S, RESULT_DUPLICATE
 from ray.tune.trial import Trial, Checkpoint
 from ray.tune.sample import function
 from ray.tune.schedulers import FIFOScheduler, TrialScheduler
+from ray.tune.suggest import BasicVariantGenerator
 from ray.tune.util import warn_if_slow
 from ray.utils import binary_to_hex, hex_to_binary
 from ray.tune.web_server import TuneServer
@@ -77,7 +78,7 @@ class TrialRunner(object):
     """A TrialRunner implements the event loop for scheduling trials on Ray.
 
     Example:
-        runner = TrialRunner(BasicVariantGenerator())
+        runner = TrialRunner()
         runner.add_trial(Trial(...))
         runner.add_trial(Trial(...))
         while not runner.is_finished():
@@ -98,14 +99,12 @@ class TrialRunner(object):
     CKPT_FILE_TMPL = "experiment_state-{}.json"
 
     def __init__(self,
-                 search_alg,
+                 search_alg=None,
                  scheduler=None,
                  launch_web_server=False,
                  metadata_checkpoint_dir=None,
                  server_port=TuneServer.DEFAULT_PORT,
                  verbose=True,
-                 queue_trials=False,
-                 reuse_actors=False,
                  trial_executor=None):
         """Initializes a new TrialRunner.
 
@@ -119,20 +118,15 @@ class TrialRunner(object):
             server_port (int): Port number for launching TuneServer
             verbose (bool): Flag for verbosity. If False, trial results
                 will not be output.
-            queue_trials (bool): Whether to queue trials when the cluster does
-                not currently have enough resources to launch one. This should
-                be set to True when running on an autoscaling cluster to enable
-                automatic scale-up.
             reuse_actors (bool): Whether to reuse actors between different
                 trials when possible. This can drastically speed up experiments
                 that start and stop actors often (e.g., PBT in
                 time-multiplexing mode).
             trial_executor (TrialExecutor): Defaults to RayTrialExecutor.
         """
-        self._search_alg = search_alg
+        self._search_alg = search_alg or BasicVariantGenerator()
         self._scheduler_alg = scheduler or FIFOScheduler()
-        self.trial_executor = (trial_executor or RayTrialExecutor(
-            queue_trials=queue_trials, reuse_actors=reuse_actors))
+        self.trial_executor = trial_executor or RayTrialExecutor()
 
         # For debugging, it may be useful to halt trials after some time has
         # elapsed. TODO(ekl) consider exposing this in the API.
@@ -141,7 +135,6 @@ class TrialRunner(object):
         self._total_time = 0
         self._iteration = 0
         self._verbose = verbose
-        self._queue_trials = queue_trials
 
         self._server = None
         self._server_port = server_port
@@ -229,11 +222,8 @@ class TrialRunner(object):
             "This will ignore any new changes to the specification."
         ]))
 
-        from ray.tune.suggest import BasicVariantGenerator
         runner = TrialRunner(
-            search_alg or BasicVariantGenerator(),
-            scheduler=scheduler,
-            trial_executor=trial_executor)
+            search_alg, scheduler=scheduler, trial_executor=trial_executor)
 
         runner.__setstate__(runner_state["runner_data"])
 

python/ray/tune/tune.py

@@ -11,6 +11,7 @@ from ray.tune.error import TuneError
 from ray.tune.experiment import convert_to_experiment_list, Experiment
 from ray.tune.suggest import BasicVariantGenerator
 from ray.tune.trial import Trial, DEBUG_PRINT_INTERVAL
+from ray.tune.ray_trial_executor import RayTrialExecutor
 from ray.tune.log_sync import wait_for_log_sync
 from ray.tune.trial_runner import TrialRunner
 from ray.tune.schedulers import (HyperBandScheduler, AsyncHyperBandScheduler,
@@ -90,7 +91,8 @@ def run(run_or_experiment,
         queue_trials=False,
         reuse_actors=False,
         trial_executor=None,
-        raise_on_failed_trial=True):
+        raise_on_failed_trial=True,
+        ray_auto_init=True):
     """Executes training.
 
     Args:
@@ -166,6 +168,9 @@ def run(run_or_experiment,
         trial_executor (TrialExecutor): Manage the execution of trials.
         raise_on_failed_trial (bool): Raise TuneError if there exists failed
             trial (of ERROR state) when the experiments complete.
+        ray_auto_init (bool): Automatically starts a local Ray cluster
+            if using a RayTrialExecutor (which is the default) and
+            if Ray is not initialized. Defaults to True.
 
     Returns:
         List of Trial objects.
@@ -187,6 +192,10 @@ def run(run_or_experiment,
                 }
             )
     """
+    trial_executor = trial_executor or RayTrialExecutor(
+        queue_trials=queue_trials,
+        reuse_actors=reuse_actors,
+        ray_auto_init=ray_auto_init)
     experiment = run_or_experiment
     if not isinstance(run_or_experiment, Experiment):
         experiment = Experiment(
@@ -229,14 +238,12 @@ def run(run_or_experiment,
         search_alg.add_configurations([experiment])
 
         runner = TrialRunner(
-            search_alg,
+            search_alg=search_alg,
             scheduler=scheduler,
             metadata_checkpoint_dir=checkpoint_dir,
             launch_web_server=with_server,
             server_port=server_port,
             verbose=bool(verbose > 1),
-            queue_trials=queue_trials,
-            reuse_actors=reuse_actors,
             trial_executor=trial_executor)
 
     if verbose: