diff --git a/clinicadl/commandline/pipelines/generate/trivial/cli.py b/clinicadl/commandline/pipelines/generate/trivial/cli.py
index b48651811..4798dc904 100644
--- a/clinicadl/commandline/pipelines/generate/trivial/cli.py
+++ b/clinicadl/commandline/pipelines/generate/trivial/cli.py
@@ -118,7 +118,6 @@ def create_trivial_image(subject_id: int) -> pd.DataFrame:
         if caps_config.data.mask_path is None:
             caps_config.data.mask_path = get_mask_path()
         path_to_mask = caps_config.data.mask_path / f"mask-{label + 1}.nii"
-        print(path_to_mask)
         if path_to_mask.is_file():
             atlas_to_mask = nib.loadsave.load(path_to_mask).get_fdata()
         else:
diff --git a/clinicadl/commandline/pipelines/train/from_json/cli.py b/clinicadl/commandline/pipelines/train/from_json/cli.py
index c0130a9b9..517ec8fa5 100644
--- a/clinicadl/commandline/pipelines/train/from_json/cli.py
+++ b/clinicadl/commandline/pipelines/train/from_json/cli.py
@@ -27,6 +27,8 @@ def cli(**kwargs):
     logger.info(f"Reading JSON file at path {kwargs['config_file']}...")
 
     trainer = Trainer.from_json(
-        config_file=kwargs["config_file"], maps_path=kwargs["output_maps_directory"]
+        config_file=kwargs["config_file"],
+        maps_path=kwargs["output_maps_directory"],
+        split=kwargs["split"],
     )
     trainer.train(split_list=kwargs["split"], overwrite=True)
diff --git a/clinicadl/monai_networks/config/vit.py b/clinicadl/monai_networks/config/vit.py
index 206d0d881..028537612 100644
--- a/clinicadl/monai_networks/config/vit.py
+++ b/clinicadl/monai_networks/config/vit.py
@@ -109,7 +109,6 @@ def _divide(
         numerator: Union[int, Tuple[int, ...]],
         denominator: Union[int, Tuple[int, ...]],
     ) -> bool:
-        print(self.dim)
         """Checks if numerator is divisible by denominator."""
         if isinstance(numerator, int):
             numerator = (numerator,) * self.dim
diff --git a/clinicadl/nn/networks/cnn.py b/clinicadl/nn/networks/cnn.py
index eb2104b1e..5fe596bcb 100644
--- a/clinicadl/nn/networks/cnn.py
+++ b/clinicadl/nn/networks/cnn.py
@@ -63,8 +63,6 @@ def __init__(self, convolution_layers: nn.Module, fc_layers: nn.Module) -> None:
 
     def forward(self, x):
         inter = self.convolutions(x)
-        print(self.convolutions)
-        print(inter.shape)
         return self.fc(inter)
 
 
diff --git a/clinicadl/nn/utils.py b/clinicadl/nn/utils.py
index dc3afd71c..263afc407 100644
--- a/clinicadl/nn/utils.py
+++ b/clinicadl/nn/utils.py
@@ -64,7 +64,6 @@ def compute_output_size(
     input_ = torch.randn(input_size).unsqueeze(0)
     if isinstance(layer, nn.MaxUnpool3d) or isinstance(layer, nn.MaxUnpool2d):
         indices = torch.zeros_like(input_, dtype=int)
-        print(indices)
         output = layer(input_, indices)
     else:
         output = layer(input_)
diff --git a/clinicadl/predictor/predictor.py b/clinicadl/predictor/predictor.py
index 2039ea75a..d686c944d 100644
--- a/clinicadl/predictor/predictor.py
+++ b/clinicadl/predictor/predictor.py
@@ -53,19 +53,11 @@ def __init__(self, _config: Union[PredictConfig, InterpretConfig]) -> None:
 
         self.maps_manager = MapsManager(_config.maps_manager.maps_dir)
         self._config.adapt_with_maps_manager_info(self.maps_manager)
-        print(
-            self._config.data.model_dump(
-                exclude=set(["preprocessing_dict", "mode", "caps_dict"])
-            )
-        )
         tmp = self._config.data.model_dump(
             exclude=set(["preprocessing_dict", "mode", "caps_dict"])
         )
-        print(tmp)
         tmp.update(self._config.split.model_dump())
-        print(tmp)
         tmp.update(self._config.validation.model_dump())
-        print(tmp)
         self.splitter = Splitter(SplitterConfig(**tmp))
 
     def predict(
@@ -205,7 +197,6 @@ def _predict_single(
         )
         if self._config.maps_manager.save_tensor:
             logger.debug("Saving tensors")
-            print("save_tensor")
             self._compute_output_tensors(
                 maps_manager=self.maps_manager,
                 dataset=data_test,
@@ -526,9 +517,7 @@ def _check_data_group(
             / self._config.maps_manager.data_group
         )
         logger.debug(f"Group path {group_dir}")
-        print(f"group_dir: {group_dir}")
         if group_dir.is_dir():  # Data group already exists
-            print("is dir")
             if self._config.maps_manager.overwrite:
                 if self._config.maps_manager.data_group in ["train", "validation"]:
                     raise MAPSError("Cannot overwrite train or validation data group.")
@@ -1047,7 +1036,6 @@ def _test_loader(
 
             if cluster.master:
                 # Replace here
-                print("before saving")
                 maps_manager._mode_level_to_tsv(
                     prediction_df,
                     metrics,
diff --git a/clinicadl/predictor/utils.py b/clinicadl/predictor/utils.py
index 0ee467956..c66372764 100644
--- a/clinicadl/predictor/utils.py
+++ b/clinicadl/predictor/utils.py
@@ -36,7 +36,6 @@ def get_prediction(
     prediction_dir = (
         maps_path / f"split-{split}" / f"best-{selection_metric}" / data_group
     )
-    print(prediction_dir)
     if not prediction_dir.is_dir():
         raise MAPSError(
             f"No prediction corresponding to data group {data_group} was found."
diff --git a/clinicadl/trainer/tasks_utils.py b/clinicadl/trainer/tasks_utils.py
index d02e47660..e3946790c 100644
--- a/clinicadl/trainer/tasks_utils.py
+++ b/clinicadl/trainer/tasks_utils.py
@@ -181,7 +181,6 @@ def generate_label_code(
     network_task = Task(network_task)
     if network_task == Task.CLASSIFICATION:
         unique_labels = sorted(set(df[label]))
-        print("unique labels", unique_labels)
         return {str(key): value for value, key in enumerate(unique_labels)}
 
     return None
@@ -624,7 +623,6 @@ def generate_sampler(
 
     def calculate_weights_classification(df):
         labels = df[dataset.config.data.label].unique()
-        print(dataset.config.data.label_code)
         codes = {label_code[label] for label in labels}
         count = np.zeros(len(codes))
 
diff --git a/clinicadl/trainer/trainer.py b/clinicadl/trainer/trainer.py
index 271d78fa6..eee7fb998 100644
--- a/clinicadl/trainer/trainer.py
+++ b/clinicadl/trainer/trainer.py
@@ -71,7 +71,7 @@ def __init__(
         predict_config = PredictConfig(**config.get_dict())
         self.validator = Predictor(predict_config)
 
-        ### test
+        # test
         splitter_config = SplitterConfig(**self.config.get_dict())
         self.splitter = Splitter(splitter_config)
         self._check_args()
@@ -92,7 +92,12 @@ def _init_maps_manager(self, config) -> MapsManager:
             )  # TODO : precise which parameters in config are useful
 
     @classmethod
-    def from_json(cls, config_file: str | Path, maps_path: str | Path) -> Trainer:
+    def from_json(
+        cls,
+        config_file: str | Path,
+        maps_path: str | Path,
+        split: Optional[list[int]] = None,
+    ) -> Trainer:
         """
         Creates a Trainer from a json configuration file.
 
@@ -119,10 +124,10 @@ def from_json(cls, config_file: str | Path, maps_path: str | Path) -> Trainer:
             raise FileNotFoundError(f"No file found at {str(config_file)}.")
         config_dict = patch_to_read_json(read_json(config_file))  # TODO : remove patch
         config_dict["maps_dir"] = maps_path
+        config_dict["split"] = split if split else ()
         config_object = create_training_config(config_dict["network_task"])(
             **config_dict
         )
-        print(config_object.model_dump())
         return cls(config_object)
 
     @classmethod
@@ -173,9 +178,6 @@ def resume(self) -> None:
         split_iterator = self.splitter.split_iterator()
         ###
         absent_splits = set(split_iterator) - stopped_splits - finished_splits
-        print("split:", set(split_iterator))
-        print("stopped split:", stopped_splits)
-        print("finished split:", finished_splits)
 
         logger.info(
             f"Finished splits {finished_splits}\n"
@@ -194,8 +196,8 @@ def resume(self) -> None:
 
     def _check_args(self):
         self.config.reproducibility.seed = get_seed(self.config.reproducibility.seed)
-        # if (len(self.config.data.label_code) == 0):
-        #     self.config.data.label_code = self.maps_manager.label_code
+        if len(self.config.data.label_code) == 0:
+            self.config.data.label_code = self.maps_manager.label_code
         # TODO: deal with label_code and replace self.maps_manager.label_code
         from clinicadl.trainer.tasks_utils import generate_label_code
 
@@ -208,9 +210,6 @@ def _check_args(self):
             self.config.data.label_code = generate_label_code(
                 self.config.network_task, train_df, self.config.data.label
             )
-            print(train_df)
-            print(self.config.network_task)
-        print("in check args : ", self.config.data.label_code)
 
     def train(
         self,
diff --git a/tests/test_train_from_json.py b/tests/test_train_from_json.py
index 06b307b0f..f1bdaff01 100644
--- a/tests/test_train_from_json.py
+++ b/tests/test_train_from_json.py
@@ -74,6 +74,7 @@ def test_determinism(cmdopt, tmp_path):
 
     # Reproduce experiment (train from json)
     config_json = tmp_out_dir / "maps_roi_cnn/maps.json"
+
     flag_error = not system(
         f"clinicadl train from_json {str(config_json)} {str(reproduced_maps_dir)} -s 0"
     )
diff --git a/tests/testing_tools.py b/tests/testing_tools.py
index 4044d1022..609393b9e 100644
--- a/tests/testing_tools.py
+++ b/tests/testing_tools.py
@@ -174,6 +174,7 @@ def modify_maps(
     base_dir: Path,
     no_gpu: bool = False,
     adapt_base_dir: bool = False,
+    modify_split: bool = False,
 ) -> Dict[str, Any]:
     """
     Modifies a MAPS dictionary if the user passed --no-gpu or --adapt-base-dir flags.
@@ -208,6 +209,9 @@ def modify_maps(
             )
         except KeyError:  # maps with only caps directory
             pass
+
+    if modify_split:
+        maps["split"] = (0,)
     return maps