diff --git a/release/ml_user_tests/ray-lightning/simple_example.py b/release/ml_user_tests/ray-lightning/simple_example.py
index 8672729d0776..bbb2f691ae86 100644
--- a/release/ml_user_tests/ray-lightning/simple_example.py
+++ b/release/ml_user_tests/ray-lightning/simple_example.py
@@ -9,7 +9,17 @@
 from torchvision import transforms
 import pytorch_lightning as pl
 
-from ray_lightning import RayPlugin
+from importlib_metadata import version
+from packaging.version import parse as v_parse
+
+rlt_use_master = v_parse(version("ray_lightning")) >= v_parse("0.3.0")
+
+if rlt_use_master:
+    # ray_lightning >= 0.3.0
+    from ray_lightning import RayStrategy
+else:
+    # ray_lightning < 0.3.0
+    from ray_lightning import RayPlugin
 
 
 class LitAutoEncoder(pl.LightningModule):
@@ -47,10 +57,16 @@ def main(num_workers: int = 2, use_gpu: bool = False, max_steps: int = 10):
     train, val = random_split(dataset, [55000, 5000])
 
     autoencoder = LitAutoEncoder()
-    trainer = pl.Trainer(
-        plugins=[RayPlugin(num_workers=num_workers, use_gpu=use_gpu)],
-        max_steps=max_steps,
-    )
+    if rlt_use_master:
+        trainer = pl.Trainer(
+            strategy=RayStrategy(num_workers=num_workers, use_gpu=use_gpu),
+            max_steps=max_steps,
+        )
+    else:
+        trainer = pl.Trainer(
+            plugins=[RayPlugin(num_workers=num_workers, use_gpu=use_gpu)],
+            max_steps=max_steps,
+        )
     trainer.fit(autoencoder, DataLoader(train), DataLoader(val))