diff --git a/README.md b/README.md
index afc9592a581c1f80c74aa35d0d16241e42d0472d..6c8d2bdb2131c9b2167eaff0da173bf74d392842 100644
--- a/README.md
+++ b/README.md
@@ -68,7 +68,7 @@ The input arguments are:
 We will average the benchmark performance over the iterations. The maximum usable (without a OOM error) batch size is 256 and 128 for single and multi-node, respectively.
 ```
 cd cd Berzelius-nnU-Net-Benchmark && mkdir -p sbatch_out
-bash scripts/benchmark_sbatch_submit.sh 2 1 8 10 128
+bash scripts/benchmark_sbatch_submit.sh 2 1 8 1 128
 ```
 
 ### Results  
diff --git a/scripts/benchmark_single_node.sbatch b/scripts/benchmark_single_node.sbatch
index 30b90b6fa0e7f79ab09d068229042b3f55ba0189..a2d31397a206aa238c23bc4080908668e5c1284b 100644
--- a/scripts/benchmark_single_node.sbatch
+++ b/scripts/benchmark_single_node.sbatch
@@ -10,10 +10,10 @@
 # This version does not run on multi-node
 # For apptainer
 rm -f results/benchmark_dim${1}_nodes${2}_gpus${3}_batchsize${4}_tf32_iteration${5}.json
-apptainer exec --nv -B ${PWD}/data:/data -B ${PWD}/results:/results nvidia_nnu-net_for_pytorch.sif bash -c "cd /workspace/nnunet_pyt && python scripts/benchmark.py --mode train --gpus ${3} --dim ${1} --batch_size ${4} --logname='benchmark_dim${1}_nodes${2}_gpus${3}_batchsize${4}_tf32_iteration${5}.json'"  
+apptainer exec --nv --no-home -B ${PWD}/data:/data -B ${PWD}/results:/results nvidia_nnu-net_for_pytorch.sif bash -c "cd /workspace/nnunet_pyt && python scripts/benchmark.py --mode train --gpus ${3} --dim ${1} --batch_size ${4} --logname='benchmark_dim${1}_nodes${2}_gpus${3}_batchsize${4}_tf32_iteration${5}.json'"  
 
 rm -f results/benchmark_dim${1}_nodes${2}_gpus${3}_batchsize${4}_amp_iteration${5}.json
-apptainer exec --nv -B ${PWD}/data:/data -B ${PWD}/results:/results nvidia_nnu-net_for_pytorch.sif bash -c "cd /workspace/nnunet_pyt && python scripts/benchmark.py --mode train --gpus ${3} --dim ${1} --batch_size ${4} --amp --logname='benchmark_dim${1}_nodes${2}_gpus${3}_batchsize${4}_amp_iteration${5}.json'"  
+apptainer exec --nv --no-home -B ${PWD}/data:/data -B ${PWD}/results:/results nvidia_nnu-net_for_pytorch.sif bash -c "cd /workspace/nnunet_pyt && python scripts/benchmark.py --mode train --gpus ${3} --dim ${1} --batch_size ${4} --amp --logname='benchmark_dim${1}_nodes${2}_gpus${3}_batchsize${4}_amp_iteration${5}.json'"  
 
 
 ###################22.11.0