Update 2 files

- /scripts/copy_data_for_benchmark.sh
- /README.md

README.md

-# Benchmark_nnU-Net_for_PyTorch
-Benchmark of nnU-Net for PyTorch on Berzelius  
+# Benchmark of nnU-Net for PyTorch on Berzelius  
 
-It is based on the Nvidia NGC recipe of nnU-net for Pytorch.  
-Latest Version 21.11.0  
-Modified February 3, 2022  
-See [NVIDIA Deep Learning Examples](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/nnunet_for_pytorch).
 
-### On local computer
-- Clone the repository  
+The benchmarking is based on [Nvidia NGC nnU-net for Pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/nnunet_for_pytorch) v21.11.0.  
+
+### On local computer (optional)
+- Download the code  
 ```
 wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/nnunet_for_pytorch/versions/21.11.0/zip -O /tmp/nnunet_for_pytorch_21.11.0.zip
 mkdir /samsung1t/ngc/nnunet_for_pytorch_21.11.0
@@ -15,7 +12,7 @@ unzip /tmp/nnunet_for_pytorch_21.11.0.zip -d /samsung1t/ngc/nnunet_for_pytorch_2
 cd /samsung1t/ngc/nnunet_for_pytorch_21.11.0/ 
 ```
 
-- Build the nnU-Net PyTorch NGC container
+- Build the nnU-Net PyTorch Docker container
 ```
 docker build -t nnunet .
 ```
@@ -28,19 +25,41 @@ docker push xuagu37/nvidia_nnu-net_for_pytorch:21.11.0
 
 ### On Berzelius
 
-- Clone the repository  
+- Create directories
+```
+mkdir -p /proj/nsc_testing/xuan/nnUnet_benchmark
+cd /proj/nsc_testing/xuan/nnUnet_benchmark
+mkdir data results
+```
+<!-- - Clone the repository  
 ```
-cd /proj/nsc/xuan/ngc
+cd /proj/nsc_testing/xuan/ngc
 git clone https://github.com/NVIDIA/DeepLearningExamples
-cd /proj/nsc/xuan/ngc/DeepLearningExamples/PyTorch/Segmentation/nnUNet
+cd DeepLearningExamples/PyTorch/Segmentation/nnUNet
 mkdir data results
+``` -->
+
+Docker is not available on Berzelius. We us Apptainer or Enroot.
+
+- Prepare the dataset
+
+With Apptainer
 ```
-- Pull from xuagu37 and run the image  
+apptainer pull nvidia_nnu-net_for_pytorch.sif docker://xuagu37/nvidia_nnu-net_for_pytorch:21.11.0
+apptainer exec --nv -B ${PWD}/data:/data -B ${PWD}/results:/results --nv nvidia_nnu-net_for_pytorch.sif bash -c "cd /workspace/nnunet_pyt && python download.py --task 01 && python preprocess.py --task 01 --dim 2"
+```  
 
-Using singularity  
+With Enroot
+```
+enroot import 'docker://xuagu37/nvidia_nnu-net_for_pytorch:21.11.0'
+enroot create --name nnunet xuagu37+nvidia_nnu-net_for_pytorch+21.11.0.sqsh
+enroot start --rw --mount ${PWD}/data:/data --mount ${PWD}/results:/results nnunet bash -c "cd /workspace/nnunet_pyt && python download.py --task 01 && python preprocess.py --task 01 --dim 2"
+```
+
+<!-- Using singularity  
 ```
 singularity pull nvidia_nnu-net_for_pytorch.sif docker://xuagu37/nvidia_nnu-net_for_pytorch:21.11.0
-singularity shell -B ${PWD}/data:/data -B ${PWD}/results:/results --nv nvidia_nnu-net_for_pytorch.sif 
+singularity shell -B ${PWD}/data:/data -B ${PWD}/results:/results --nv nvidia_nnu-net_for_pytorch.sif  
 ```  
 Or using enroot  
 ```
@@ -53,11 +72,11 @@ enroot start --rw --mount ${PWD}/data:/data --mount ${PWD}/results:/results nnun
 python download.py --task 01
 python preprocess.py --task 01 --dim 2
 ```
-Exit the image.
+Exit the image. 
+-->
+
 - For benchmarking purpose, we use 1000 copied of a single image 
 ```
-mkdir data_orig
-cp -a data/* data_orig/
 bash copy_data_for_benchmark.sh
 ```
 - Run the script.   

scripts/copy_data_for_benchmark.sh

+mkdir data_orig
+cp -a data/* data_orig/
 cd data/01_2d
 
 for i in {0..9}; do