#Links
#All the course material originally developed by Khatuna Kakhiani for HLRS-DL courses:
#https://fs.hlrs.de/projects/par/events/2023/sst/


#https://pillow.readthedocs.io/en/stable/
#https://scikit-learn.org
#https://keras.io/api/models/
#https://www.tensorflow.org/resources/models-datasets

#https://www.deeplearningbook.org/ 
#@book{Goodfellow-et-al-2016,
#    title={Deep Learning},
#    author={Ian Goodfellow and Yoshua Bengio and Aaron 
#    Courville},
#    publisher={MIT Press},
#    note={\url{http://www.deeplearningbook.org}},
#    year={2016}
 
# Commands 1 (Vulcan - vulcan.hww.hlrs.de)

ssh YOURUSERNAME@vulcan.hww.hlrs.de

# Commands 2
# For a new ws 
ws_allocate wspart2 15

ws_list
MYSCR=$(ws_find wspart2)
cd $MYSCR

# Commands 3
cp /lustre/nec/ws3/ws/hpckkakh-exchangews/wsday2/data.tar.gz .
cp /lustre/nec/ws3/ws/hpckkakh-exchangews/wsday2/work_data_50.tar.gz .
cp /lustre/nec/ws3/ws/hpckkakh-exchangews/wsday2/notebooks.tar.gz .
cp /lustre/nec/ws3/ws/hpckkakh-exchangews/wsday2/solutions.tar.gz .

tar -zxvf data.tar.gz
tar -zxvf work_data_50.tar.gz
tar -zxvf notebooks.tar.gz

# Commands 4
# To start Jupyter Notebook (JN) auf Vulcan on compute node:
ssh YOURUSERNAME@vulcan.hww.hlrs.de -q -D 8080

#Reserve compute node option as provided for the course (attenton: course queue!)
qsub -I -q R_sst -l select=1:node_type=hsw:mem=100gb,walltime=05:00:00

#Locate your ws for part2
MYSCR=$(ws_find wspart2)
cd $MYSCR

#For working environment on compute node 

. notebooks/modules.sh

#Copy URL link to your browser-Profil
 http://n081501:8888/?token=.....


# Commands 5
# To find the current path:
pwd

# To find the workspace path in Vulcan:
ws_list

# To copy a folder to a local directory (from a _local_terminal):
scp -r vdl3XXX@vulcan.hww.hlrs.de:/path/to/workspace/and/folder /path/to/local/destination

Cat output.txt
head -n 30 output.txt
tail output.txt