diff --git a/benchmark/supervised/hyper_parameter_search.py b/benchmark/supervised/hyper_parameter_search.py
index a2778a01..eadcd14b 100644
--- a/benchmark/supervised/hyper_parameter_search.py
+++ b/benchmark/supervised/hyper_parameter_search.py
@@ -86,27 +86,28 @@ def run(cfg: Mapping[str, Any], filter_pattern: str) -> None:
 
             fold_ds = ds.get_fold_ds(fid)
 
-            cprint("\n|-building train dataset\n", "blue")
-            train_x = tf.constant(np.array(fold_ds["train"][0]))
-            train_y = tf.constant(np.array(fold_ds["train"][1]))
-            train_ds = datasets.utils.make_sampler(
-                train_x,
-                train_y,
-                exp.training.params["train"],
-                train_aug_fns,
-            )
-            exp.training.params["train"]["num_examples"] = train_x.shape[0]
-
-            cprint("\n|-building val dataset\n", "blue")
-            val_x = tf.constant(np.array(fold_ds["val"][0]))
-            val_y = tf.constant(np.array(fold_ds["val"][1]))
-            val_ds = datasets.utils.make_sampler(
-                val_x,
-                val_y,
-                exp.training.params["val"],
-                train_aug_fns,
-            )
-            exp.training.params["val"]["num_examples"] = val_x.shape[0]
+            with tf.device("CPU"):
+                cprint("\n|-building train dataset\n", "blue")
+                train_x = tf.constant(np.array(fold_ds["train"][0]))
+                train_y = tf.constant(np.array(fold_ds["train"][1]))
+                train_ds = datasets.utils.make_sampler(
+                    train_x,
+                    train_y,
+                    exp.training.params["train"],
+                    train_aug_fns,
+                )
+                exp.training.params["train"]["num_examples"] = train_x.shape[0]
+
+                cprint("\n|-building val dataset\n", "blue")
+                val_x = tf.constant(np.array(fold_ds["val"][0]))
+                val_y = tf.constant(np.array(fold_ds["val"][1]))
+                val_ds = datasets.utils.make_sampler(
+                    val_x,
+                    val_y,
+                    exp.training.params["val"],
+                    train_aug_fns,
+                )
+                exp.training.params["val"]["num_examples"] = val_x.shape[0]
 
             # Training params
             callbacks = [