chore: import upstream snapshot with attribution

python/ray/tune/tests/test_warnings.py

@@ -0,0 +1,143 @@
+import pytest
+
+import ray
+from ray import tune
+from ray.data.context import DataContext
+from ray.tune.error import TuneError
+from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
+
+
+def test_nowarn_zero_cpu():
+    def f(*a):
+        @ray.remote(num_cpus=0)
+        def f():
+            pass
+
+        @ray.remote(num_cpus=0)
+        class Actor:
+            def f(self):
+                pass
+
+        ray.get(f.remote())
+        a = Actor.remote()
+        ray.get(a.f.remote())
+
+    tune.run(f, verbose=0)
+
+
+def test_warn_cpu():
+    def f(*a):
+        @ray.remote(num_cpus=1)
+        def f():
+            pass
+
+        ray.get(f.remote())
+
+    with pytest.raises(TuneError):
+        tune.run(f, verbose=0)
+
+    with pytest.raises(TuneError):
+        tune.run(
+            f, resources_per_trial=tune.PlacementGroupFactory([{"CPU": 1}]), verbose=0
+        )
+
+    def g(*a):
+        @ray.remote(num_cpus=1)
+        class Actor:
+            def f(self):
+                pass
+
+        a = Actor.remote()
+        ray.get(a.f.remote())
+
+    with pytest.raises(TuneError):
+        tune.run(g, verbose=0)
+
+    with pytest.raises(TuneError):
+        tune.run(
+            g, resources_per_trial=tune.PlacementGroupFactory([{"CPU": 1}]), verbose=0
+        )
+
+
+def test_pg_slots_ok():
+    def f(*a):
+        @ray.remote(num_cpus=1)
+        def f():
+            pass
+
+        @ray.remote(num_cpus=1)
+        class Actor:
+            def f(self):
+                pass
+
+        ray.get(f.remote())
+        a = Actor.remote()
+        ray.get(a.f.remote())
+
+    tune.run(
+        f, resources_per_trial=tune.PlacementGroupFactory([{"CPU": 1}] * 2), verbose=0
+    )
+
+
+def test_bad_pg_slots():
+    def f(*a):
+        @ray.remote(num_cpus=2)
+        def f():
+            pass
+
+        ray.get(f.remote())
+
+    with pytest.raises(TuneError):
+        tune.run(
+            f,
+            resources_per_trial=tune.PlacementGroupFactory([{"CPU": 1}] * 2),
+            verbose=0,
+        )
+
+
+def test_dataset_ok():
+    def f(*a):
+        ray.data.range(10).show()
+
+    tune.run(f, verbose=0)
+
+    def g(*a):
+        ctx = DataContext.get_current()
+        ctx.scheduling_strategy = PlacementGroupSchedulingStrategy(
+            ray.util.get_current_placement_group()
+        )
+        ray.data.range(10).show()
+
+    with pytest.raises(TuneError):
+        tune.run(g, verbose=0)
+
+    tune.run(
+        g, resources_per_trial=tune.PlacementGroupFactory([{"CPU": 1}] * 2), verbose=0
+    )
+
+
+def test_scheduling_strategy_override():
+    def f(*a):
+        @ray.remote(num_cpus=1, scheduling_strategy="SPREAD")
+        def f():
+            pass
+
+        @ray.remote(num_cpus=1, scheduling_strategy="SPREAD")
+        class Actor:
+            def f(self):
+                pass
+
+        # SPREAD tasks are not captured by placement groups, so don't warn.
+        ray.get(f.remote())
+
+        # SPREAD actors are not captured by placement groups, so don't warn.
+        a = Actor.remote()
+        ray.get(a.f.remote())
+
+    tune.run(f, verbose=0)
+
+
+if __name__ == "__main__":
+    import sys
+
+    sys.exit(pytest.main(["-v", __file__]))