diff --git a/nnunetv2/inference/predict_from_raw_data.py b/nnunetv2/inference/predict_from_raw_data.py
index 1985afe52..4a7f97c8a 100644
--- a/nnunetv2/inference/predict_from_raw_data.py
+++ b/nnunetv2/inference/predict_from_raw_data.py
@@ -748,6 +748,12 @@ def predict_from_files_sequential(self,
         empty_cache(self.device)
         return ret
 
+def _getDefaultValue(env: str, dtype: type, default: any,) -> any:
+    try:
+        val = dtype(os.environ.get(env) or default)
+    except:
+        val = default
+    return val
 
 def predict_entry_point_modelfolder():
     import argparse
@@ -883,10 +889,10 @@ def predict_entry_point():
                         help='Continue an aborted previous prediction (will not overwrite existing files)')
     parser.add_argument('-chk', type=str, required=False, default='checkpoint_final.pth',
                         help='Name of the checkpoint you want to use. Default: checkpoint_final.pth')
-    parser.add_argument('-npp', type=int, required=False, default=3,
+    parser.add_argument('-npp', type=int, required=False, default=_getDefaultValue('nnUNet_npp', int, 3),
                         help='Number of processes used for preprocessing. More is not always better. Beware of '
                              'out-of-RAM issues. Default: 3')
-    parser.add_argument('-nps', type=int, required=False, default=3,
+    parser.add_argument('-nps', type=int, required=False, default=_getDefaultValue('nnUNet_nps', int, 3),
                         help='Number of processes used for segmentation export. More is not always better. Beware of '
                              'out-of-RAM issues. Default: 3')
     parser.add_argument('-prev_stage_predictions', type=str, required=False, default=None,
@@ -953,13 +959,26 @@ def predict_entry_point():
         args.f,
         checkpoint_name=args.chk
     )
-    predictor.predict_from_files(args.i, args.o, save_probabilities=args.save_probabilities,
-                                 overwrite=not args.continue_prediction,
-                                 num_processes_preprocessing=args.npp,
-                                 num_processes_segmentation_export=args.nps,
-                                 folder_with_segs_from_prev_stage=args.prev_stage_predictions,
-                                 num_parts=args.num_parts,
-                                 part_id=args.part_id)
+    
+    run_sequential = args.nps == 0 and args.npp == 0
+    
+    if run_sequential:
+        
+        print("Running in non-multiprocessing mode")
+        predictor.predict_from_files_sequential(args.i, args.o, save_probabilities=args.save_probabilities,
+                                                overwrite=not args.continue_prediction,
+                                                folder_with_segs_from_prev_stage=args.prev_stage_predictions)
+    
+    else:
+        
+        predictor.predict_from_files(args.i, args.o, save_probabilities=args.save_probabilities,
+                                    overwrite=not args.continue_prediction,
+                                    num_processes_preprocessing=args.npp,
+                                    num_processes_segmentation_export=args.nps,
+                                    folder_with_segs_from_prev_stage=args.prev_stage_predictions,
+                                    num_parts=args.num_parts,
+                                    part_id=args.part_id)
+    
     # r = predict_from_raw_data(args.i,
     #                           args.o,
     #                           model_folder,