diff --git a/nnunetv2/dataset_conversion/Dataset224_AbdomenAtlas1.0.py b/nnunetv2/dataset_conversion/Dataset224_AbdomenAtlas1.0.py
index 6a0a9ad59..e8f878088 100644
--- a/nnunetv2/dataset_conversion/Dataset224_AbdomenAtlas1.0.py
+++ b/nnunetv2/dataset_conversion/Dataset224_AbdomenAtlas1.0.py
@@ -4,6 +4,20 @@
 
 
 if __name__ == '__main__':
+    """
+    How to train our submission to the JHU benchmark
+    
+    1. Execute this script here to convert the dataset into nnU-Net format. Adapt the paths to your system!
+    2. Run planning and preprocessing: `nnUNetv2_plan_and_preprocess -d 224 -npfp 64 -np 64 -c 3d_fullres -pl 
+    nnUNetPlannerResEncL_torchres`. Adapt the number of processes to your System (-np; -npfp)! Note that each process 
+    will again spawn 4 threads for resampling. This custom planner replaces the nnU-Net default resampling scheme with 
+    a torch-based implementation which is faster but less accurate. This is needed to satisfy the inference speed 
+    constraints.
+    3. Run training with `nnUNetv2_train 224 3d_fullres all -p nnUNetResEncUNetLPlans_torchres`. 24GB VRAM required, 
+    training will take ~28-30h.
+    """
+
+
     base = '/home/isensee/Downloads/AbdomenAtlas1.0Mini'
     cases = subdirs(base, join=False, prefix='BDMAP')
 
diff --git a/nnunetv2/inference/JHU_inference.py b/nnunetv2/inference/JHU_inference.py
index d57c2a606..0933600a9 100644
--- a/nnunetv2/inference/JHU_inference.py
+++ b/nnunetv2/inference/JHU_inference.py
@@ -176,7 +176,7 @@ def predict_from_data_iterator(self,
     predictor.initialize_from_trained_model_folder(
         args.model,
         ('all', ),
-        'checkpoint_latest.pth'
+        'checkpoint_final.pth'
     )
 
     # we need to create list of list of input files
diff --git a/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py b/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py
index d3803f391..b23847cb2 100644
--- a/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py
+++ b/nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py
@@ -238,13 +238,24 @@ def initialize(self):
     def _do_i_compile(self):
         # new default: compile is enabled!
 
+        # compile does not work on mps
+        if self.device == torch.device('mps'):
+            if 'nnUNet_compile' in os.environ.keys() and os.environ['nnUNet_compile'].lower() in ('true', '1', 't'):
+                self.print_to_log_file("INFO: torch.compile disabled because of unsupported mps device")
+            return False
+
         # CPU compile crashes for 2D models. Not sure if we even want to support CPU compile!? Better disable
         if self.device == torch.device('cpu'):
+            if 'nnUNet_compile' in os.environ.keys() and os.environ['nnUNet_compile'].lower() in ('true', '1', 't'):
+                self.print_to_log_file("INFO: torch.compile disabled because device is CPU")
             return False
 
         # default torch.compile doesn't work on windows because there are apparently no triton wheels for it
         # https://discuss.pytorch.org/t/windows-support-timeline-for-torch-compile/182268/2
         if os.name == 'nt':
+            if 'nnUNet_compile' in os.environ.keys() and os.environ['nnUNet_compile'].lower() in ('true', '1', 't'):
+                self.print_to_log_file("INFO: torch.compile disabled because Windows is not natively supported. If "
+                                       "you know what you are doing, check https://discuss.pytorch.org/t/windows-support-timeline-for-torch-compile/182268/2")
             return False
 
         if 'nnUNet_compile' not in os.environ.keys():