chore: import upstream snapshot with attribution

python/ray/tune/tests/test_remote.py

@@ -0,0 +1,109 @@
+import inspect
+import unittest
+from unittest.mock import patch
+
+import ray
+from ray.tune import choice, register_trainable, run, run_experiments
+from ray.tune.experiment import Experiment, Trial
+from ray.tune.result import TIMESTEPS_TOTAL
+from ray.tune.search.hyperopt import HyperOptSearch
+from ray.util.client.ray_client_helpers import ray_start_client_server
+
+
+def train_fn(config):
+    for i in range(100):
+        ray.tune.report(dict(timesteps_total=i))
+
+
+class RemoteTest(unittest.TestCase):
+    def tearDown(self):
+        ray.shutdown()
+
+    def testRemoteRunExperiments(self):
+        register_trainable("f1", train_fn)
+        exp1 = Experiment(
+            **{
+                "name": "foo",
+                "run": "f1",
+            }
+        )
+        [trial] = run_experiments(exp1, _remote=True)
+        self.assertEqual(trial.status, Trial.TERMINATED)
+        self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 99)
+
+    def testRemoteRun(self):
+        analysis = run(train_fn, _remote=True)
+        [trial] = analysis.trials
+        self.assertEqual(trial.status, Trial.TERMINATED)
+        self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 99)
+
+    def testRemoteRunArguments(self):
+        def mocked_run(*args, **kwargs):
+            capture_args_kwargs = (args, kwargs)
+            return run(*args, **kwargs), capture_args_kwargs
+
+        with patch("ray.tune.tune.run", mocked_run):
+            analysis, capture_args_kwargs = run(train_fn, _remote=True)
+        args, kwargs = capture_args_kwargs
+        self.assertFalse(args)
+        kwargs.pop("run_or_experiment")
+        kwargs.pop("_remote")
+        kwargs.pop("progress_reporter")  # gets autodetected and set
+
+        default_kwargs = {
+            k: v.default for k, v in inspect.signature(run).parameters.items()
+        }
+        default_kwargs.pop("run_or_experiment")
+        default_kwargs.pop("_remote")
+        default_kwargs.pop("progress_reporter")
+
+        self.assertDictEqual(kwargs, default_kwargs)
+
+    def testRemoteRunWithSearcher(self):
+        analysis = run(
+            train_fn,
+            search_alg=HyperOptSearch(),
+            config={"a": choice(["a", "b"])},
+            metric="timesteps_total",
+            mode="max",
+            _remote=True,
+        )
+        [trial] = analysis.trials
+        self.assertEqual(trial.status, Trial.TERMINATED)
+        self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 99)
+
+    def testRemoteRunExperimentsInClient(self):
+        ray.init()
+        assert not ray.util.client.ray.is_connected()
+        with ray_start_client_server():
+            assert ray.util.client.ray.is_connected()
+
+            register_trainable("f1", train_fn)
+            exp1 = Experiment(
+                **{
+                    "name": "foo",
+                    "run": "f1",
+                }
+            )
+            [trial] = run_experiments(exp1)
+            self.assertEqual(trial.status, Trial.TERMINATED)
+            self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 99)
+
+    def testRemoteRunInClient(self):
+        ray.init()
+        assert not ray.util.client.ray.is_connected()
+        with ray_start_client_server():
+            assert ray.util.client.ray.is_connected()
+
+            analysis = run(train_fn)
+            [trial] = analysis.trials
+            self.assertEqual(trial.status, Trial.TERMINATED)
+            self.assertEqual(trial.last_result[TIMESTEPS_TOTAL], 99)
+
+
+if __name__ == "__main__":
+    import sys
+
+    import pytest
+
+    sys.exit(pytest.main(["-v", __file__]))