diff --git a/TrainerClass.py b/TrainerClass.py
index c65adbe..cb351bf 100644
--- a/TrainerClass.py
+++ b/TrainerClass.py
@@ -154,6 +154,11 @@ class eNoseTrainer:
 
 
     def train_and_score_conv1D_v1(self, X_train, X_test, y_train, y_test, epochs=30, num_samples=25):
+        ray.init(ignore_reinit_error=True)
+        X_train_ref = ray.put(X_train)
+        Y_train_ref = ray.put(Y_train)
+        X_test_ref = ray.put(X_test)
+        Y_test_ref = ray.put(Y_test)
         def build_model(config, input_shape, output_dim):
             model = keras.Sequential([
                 layers.Conv1D(filters=config['filters'], kernel_size=config['kernel_size'], activation='relu', input_shape=input_shape),
@@ -199,8 +204,11 @@ class eNoseTrainer:
         }
 
         scheduler = ASHAScheduler(metric='mse', mode='min', max_t=epochs, grace_period=5, reduction_factor=2)
-        analysis = tune.run(train_model, config=config_space, num_samples=num_samples, scheduler=scheduler)
-
+        # analysis = tune.run(train_model, config=config_space, num_samples=num_samples, scheduler=scheduler)
+        analysis = tune.run(
+            tune.with_parameters(train_model, X_train=ray.get(X_train_ref), Y_train=ray.get(Y_train_ref), X_test=ray.get(X_test_ref), Y_test=ray.get(Y_test_ref)),
+            config=config_space, num_samples=num_samples, scheduler=scheduler
+        )
         best_config = analysis.get_best_config(metric='mse', mode='min')
         best_model = build_model(best_config, X_train.shape[1:], Y_train.shape[1])
         best_model.fit(X_train, Y_train, epochs=epochs, batch_size=best_config['batch_size'], verbose=0)