Update ngc_containers.md

doc.zih.tu-dresden.de/docs/software/ngc_containers.md

@@ -50,14 +50,14 @@ If you are not familiar with Singularity's syntax, please find the information o
 However, some main commands will be explained.
 
 Create a container from the image from the NGC catalog.
-(For this example, the alpha is used):
+(For this example, the cluster alpha is used):
 
 ```console
-marie@login$ srun --partition=alpha --nodes=1 --ntasks-per-node=1 --ntasks=1 --gres=gpu:1 --time=08:00:00 --pty --mem=50000 bash
+marie@login.alpha$ srun --nodes=1 --ntasks-per-node=1 --ntasks=1 --gres=gpu:1 --time=08:00:00 --pty --mem=50000 bash
 
-marie@compute$ cd /scratch/ws/<name_of_your_workspace>/containers   #please create a Workspace
+marie@alpha$ cd /data/horse/ws/<name_of_your_workspace>/containers   #please create a Workspace
 
-marie@compute$ singularity pull pytorch:21.08-py3.sif docker://nvcr.io/nvidia/pytorch:21.08-py3
+marie@alpha$ singularity pull pytorch:21.08-py3.sif docker://nvcr.io/nvidia/pytorch:21.08-py3
 ```
 
 Now, you have a fully functional PyTorch container.
@@ -73,20 +73,20 @@ To download the dataset, please follow the
 Also, you can find the instructions in a README file which you can find inside the container:
 
 ```console
-marie@compute$ singularity exec pytorch:21.06-py3_beegfs vim /workspace/examples/resnet50v1.5/README.md
+marie@alpha$ singularity exec pytorch:21.06-py3_beegfs vim /workspace/examples/resnet50v1.5/README.md
 ```
 
 It is recommended to run the container with a single command.
 However, for the educational purpose, the separate commands will be presented below:
 
 ```console
-marie@login$ srun --partition=alpha --nodes=1 --ntasks-per-node=1 --ntasks=1 --gres=gpu:1 --time=08:00:00 --pty --mem=50000 bash
+marie@login.alpha$ srun --nodes=1 --ntasks-per-node=1 --ntasks=1 --gres=gpu:1 --time=08:00:00 --pty --mem=50000 bash
 ```
 
 Run a shell within a container with the `singularity shell` command:
 
 ```console
-marie@compute$ singularity shell --nv -B /scratch/imagenet:/data/imagenet pytorch:21.06-py3
+marie@alpha$ singularity shell --nv -B /data/horse/imagenet:/data/imagenet pytorch:21.06-py3
 ```
 
 The flag `--nv` in the command above was used to enable Nvidia support for GPU usage
@@ -112,8 +112,8 @@ As an example, please find the full command to run the ResNet50 model
 on the ImageNet dataset inside the PyTorch container:
 
 ```console
-marie@login$ srun --partition=alpha --nodes=1 --ntasks-per-node=1 --ntasks=1 --gres=gpu:1 --time=08:00:00 --pty --mem=50000 \
-                singularity exec --nv -B /scratch/ws/0/anpo879a-ImgNet/imagenet:/data/imagenet pytorch:21.06-py3 \
+marie@login.alpha$ srun --nodes=1 --ntasks-per-node=1 --ntasks=1 --gres=gpu:1 --time=08:00:00 --pty --mem=50000 \
+                singularity exec --nv -B /data/horse/ws/0/anpo879a-ImgNet/imagenet:/data/imagenet pytorch:21.06-py3 \
                 python /workspace/examples/resnet50v1.5/multiproc.py --nnodes=1 --nproc_per_node 1 \
                 --node_rank=0 /workspace/examples/resnet50v1.5/main.py --data-backend dali-cpu --raport-file raport.json \
                 -j16 -p 100 --lr 2.048 --optimizer-batch-size 2048 --warmup 8 --arch resnet50 -c fanin --label-smoothing 0.1 \
@@ -136,11 +136,11 @@ An example of using the PyTorch container for the training of the ResNet50 model
 on the classification task on the ImageNet dataset is presented below:
 
 ```console
-marie@login$ srun --partition=alpha --nodes=1 --ntasks-per-node=8 --ntasks=8 --gres=gpu:8 --time=08:00:00 --pty --mem=700G bash
+marie@login.alpha$ srun --nodes=1 --ntasks-per-node=8 --ntasks=8 --gres=gpu:8 --time=08:00:00 --pty --mem=700G bash
 ```
 
 ```console
-marie@alpha$ singularity exec --nv -B /scratch/ws/0/marie-ImgNet/imagenet:/data/imagenet pytorch:21.06-py3 \
+marie@alpha$ singularity exec --nv -B /data/horse/ws/0/marie-ImgNet/imagenet:/data/imagenet pytorch:21.06-py3 \
                 python /workspace/examples/resnet50v1.5/multiproc.py --nnodes=1 --nproc_per_node 8 \
                 --node_rank=0 /workspace/examples/resnet50v1.5/main.py --data-backend dali-cpu \
                 --raport-file raport.json -j16 -p 100 --lr 2.048 --optimizer-batch-size 2048 --warmup 8 \