chore: import upstream snapshot with attribution

python/ray/train/tests/test_windows.py

@@ -0,0 +1,51 @@
+"""This is a very minimal set of windows tests for Train/Tune."""
+
+import os
+
+import pytest
+
+import ray
+from ray import train
+from ray.train.tests.util import create_dict_checkpoint
+from ray.train.v2.api.data_parallel_trainer import DataParallelTrainer
+
+
+@pytest.fixture
+def ray_start_4_cpus():
+    address_info = ray.init(num_cpus=4)
+    yield address_info
+    # The code after the yield will run as teardown code.
+    ray.shutdown()
+
+
+@pytest.fixture
+def chdir_tmpdir(tmp_path):
+    original_path = os.getcwd()
+    os.chdir(tmp_path)
+    yield
+    os.chdir(original_path)
+
+
+def test_storage_path(ray_start_4_cpus, chdir_tmpdir):
+    """Tests that Train with a local storage path works on Windows."""
+
+    def train_fn(config):
+        for i in range(5):
+            if train.get_context().get_world_rank() == 0:
+                with create_dict_checkpoint({"dummy": "data"}) as checkpoint:
+                    train.report({"loss": i}, checkpoint=checkpoint)
+            else:
+                train.report({"loss": i})
+
+    trainer = DataParallelTrainer(
+        train_fn,
+        scaling_config=train.ScalingConfig(num_workers=2),
+        run_config=train.RunConfig(storage_path=os.getcwd()),
+    )
+    trainer.fit()
+
+
+if __name__ == "__main__":
+    import sys
+
+    sys.exit(pytest.main(["-v", "-x", __file__]))