Started by timer
Running as SYSTEM
Building in workspace /var/lib/jenkins/jobs/pytorch_train/workspace
[SSH] script:
TARGETNODE=""""

module load anaconda3_gpu/4.13.0
module load cuda/11.7.0

cd pytorch_train
rm -f train_results_jenkins.csv

# Slurm Arguments
sargs="--nodes=1 "
sargs+="--ntasks-per-node=1 "
sargs+="--mem=16g "
sargs+="--time=00:10:00 "
sargs+="--account=bbmb-hydro "
sargs+="--gpus-per-node=1 "
sargs+="--gpu-bind=closest "
# Add Target node if it exists
if [[ ! -z ${TARGETNODE} ]]
then
    PARTITION=`sinfo --format="%R,%N" -n hydro61  | grep hydro61  | cut -d',' -f1 | tail -1`
    sargs+="--partition=${PARTITION} "
    sargs+="--nodelist=${TARGETNODE} "
else
    sargs+="--partition=a100 "
fi
# Executable to run
scmd="python train.py | tee time.txt"

# Run the command
start_time=`date +%s.%N`
echo $"Starting srun with command"
echo "srun $sargs $scmd"
srun $sargs $scmd
end_time=`date +%s.%N`

runtime=$( echo "$end_time - $start_time" | bc -l )
echo "YVALUE=$runtime" > time.txt
printf "Pytorch test completed in %0.3f secs\n" $runtime

[SSH] executing...
Starting srun with command
srun --nodes=1 --ntasks-per-node=1 --mem=16g --time=00:10:00 --account=bbmb-hydro --gpus-per-node=1 --gpu-bind=closest --partition=a100  python train.py | tee time.txt
srun: job 96447 queued and waiting for resources
srun: job 96447 has been allocated resources
Running benchmark on hydro03
Epoch [1/64], Step [100/600], Loss: 0.1870
Epoch [1/64], Step [200/600], Loss: 0.0944
Epoch [1/64], Step [300/600], Loss: 0.0624
Epoch [1/64], Step [400/600], Loss: 0.0660
Epoch [1/64], Step [500/600], Loss: 0.0764
Epoch [1/64], Step [600/600], Loss: 0.1219
Epoch [2/64], Step [100/600], Loss: 0.0521
Epoch [2/64], Step [200/600], Loss: 0.0231
Epoch [2/64], Step [300/600], Loss: 0.0731
Epoch [2/64], Step [400/600], Loss: 0.0715
Epoch [2/64], Step [500/600], Loss: 0.0486
Epoch [2/64], Step [600/600], Loss: 0.0178
Epoch [3/64], Step [100/600], Loss: 0.0238
Epoch [3/64], Step [200/600], Loss: 0.0654
Epoch [3/64], Step [300/600], Loss: 0.0138
Epoch [3/64], Step [400/600], Loss: 0.0872
Epoch [3/64], Step [500/600], Loss: 0.0581
Epoch [3/64], Step [600/600], Loss: 0.0115
Epoch [4/64], Step [100/600], Loss: 0.0270
Epoch [4/64], Step [200/600], Loss: 0.0309
Epoch [4/64], Step [300/600], Loss: 0.0287
Epoch [4/64], Step [400/600], Loss: 0.0544
Epoch [4/64], Step [500/600], Loss: 0.0141
Epoch [4/64], Step [600/600], Loss: 0.0401
Epoch [5/64], Step [100/600], Loss: 0.0061
Epoch [5/64], Step [200/600], Loss: 0.0200
Epoch [5/64], Step [300/600], Loss: 0.0240
Epoch [5/64], Step [400/600], Loss: 0.0378
Epoch [5/64], Step [500/600], Loss: 0.0211
Epoch [5/64], Step [600/600], Loss: 0.0399
Epoch [6/64], Step [100/600], Loss: 0.0226
Epoch [6/64], Step [200/600], Loss: 0.0634
Epoch [6/64], Step [300/600], Loss: 0.0357
Epoch [6/64], Step [400/600], Loss: 0.0043
Epoch [6/64], Step [500/600], Loss: 0.0151
Epoch [6/64], Step [600/600], Loss: 0.0343
Epoch [7/64], Step [100/600], Loss: 0.0045
Epoch [7/64], Step [200/600], Loss: 0.0087
Epoch [7/64], Step [300/600], Loss: 0.0036
Epoch [7/64], Step [400/600], Loss: 0.0087
Epoch [7/64], Step [500/600], Loss: 0.0859
Epoch [7/64], Step [600/600], Loss: 0.0219
Epoch [8/64], Step [100/600], Loss: 0.0149
Epoch [8/64], Step [200/600], Loss: 0.0023
Epoch [8/64], Step [300/600], Loss: 0.0680
Epoch [8/64], Step [400/600], Loss: 0.0219
Epoch [8/64], Step [500/600], Loss: 0.0022
Epoch [8/64], Step [600/600], Loss: 0.0297
Epoch [9/64], Step [100/600], Loss: 0.0398
Epoch [9/64], Step [200/600], Loss: 0.0006
Epoch [9/64], Step [300/600], Loss: 0.0029
Epoch [9/64], Step [400/600], Loss: 0.0235
Epoch [9/64], Step [500/600], Loss: 0.0188
Epoch [9/64], Step [600/600], Loss: 0.0167
Epoch [10/64], Step [100/600], Loss: 0.0073
Epoch [10/64], Step [200/600], Loss: 0.0019
Epoch [10/64], Step [300/600], Loss: 0.0007
Epoch [10/64], Step [400/600], Loss: 0.0048
Epoch [10/64], Step [500/600], Loss: 0.0113
Epoch [10/64], Step [600/600], Loss: 0.0158
Epoch [11/64], Step [100/600], Loss: 0.0307
Epoch [11/64], Step [200/600], Loss: 0.0188
Epoch [11/64], Step [300/600], Loss: 0.0005
Epoch [11/64], Step [400/600], Loss: 0.0089
Epoch [11/64], Step [500/600], Loss: 0.0061
Epoch [11/64], Step [600/600], Loss: 0.0020
Epoch [12/64], Step [100/600], Loss: 0.0037
Epoch [12/64], Step [200/600], Loss: 0.0023
Epoch [12/64], Step [300/600], Loss: 0.0030
Epoch [12/64], Step [400/600], Loss: 0.0015
Epoch [12/64], Step [500/600], Loss: 0.0328
Epoch [12/64], Step [600/600], Loss: 0.0093
Epoch [13/64], Step [100/600], Loss: 0.0027
Epoch [13/64], Step [200/600], Loss: 0.0068
Epoch [13/64], Step [300/600], Loss: 0.0017
Epoch [13/64], Step [400/600], Loss: 0.0010
Epoch [13/64], Step [500/600], Loss: 0.0074
Epoch [13/64], Step [600/600], Loss: 0.0070
Epoch [14/64], Step [100/600], Loss: 0.0361
Epoch [14/64], Step [200/600], Loss: 0.0006
Epoch [14/64], Step [300/600], Loss: 0.0019
Epoch [14/64], Step [400/600], Loss: 0.0047
Epoch [14/64], Step [500/600], Loss: 0.0025
Epoch [14/64], Step [600/600], Loss: 0.0007
Epoch [15/64], Step [100/600], Loss: 0.0014
Epoch [15/64], Step [200/600], Loss: 0.0034
Epoch [15/64], Step [300/600], Loss: 0.0063
Epoch [15/64], Step [400/600], Loss: 0.0091
Epoch [15/64], Step [500/600], Loss: 0.0053
Epoch [15/64], Step [600/600], Loss: 0.0466
Epoch [16/64], Step [100/600], Loss: 0.0005
Epoch [16/64], Step [200/600], Loss: 0.0063
Epoch [16/64], Step [300/600], Loss: 0.0109
Epoch [16/64], Step [400/600], Loss: 0.0013
Epoch [16/64], Step [500/600], Loss: 0.0217
Epoch [16/64], Step [600/600], Loss: 0.0002
Epoch [17/64], Step [100/600], Loss: 0.0048
Epoch [17/64], Step [200/600], Loss: 0.0010
Epoch [17/64], Step [300/600], Loss: 0.0040
Epoch [17/64], Step [400/600], Loss: 0.0055
Epoch [17/64], Step [500/600], Loss: 0.0008
Epoch [17/64], Step [600/600], Loss: 0.0020
Epoch [18/64], Step [100/600], Loss: 0.0002
Epoch [18/64], Step [200/600], Loss: 0.0042
Epoch [18/64], Step [300/600], Loss: 0.0030
Epoch [18/64], Step [400/600], Loss: 0.0005
Epoch [18/64], Step [500/600], Loss: 0.0010
Epoch [18/64], Step [600/600], Loss: 0.0037
Epoch [19/64], Step [100/600], Loss: 0.0001
Epoch [19/64], Step [200/600], Loss: 0.0033
Epoch [19/64], Step [300/600], Loss: 0.0038
Epoch [19/64], Step [400/600], Loss: 0.0028
Epoch [19/64], Step [500/600], Loss: 0.0049
Epoch [19/64], Step [600/600], Loss: 0.0031
Epoch [20/64], Step [100/600], Loss: 0.0007
Epoch [20/64], Step [200/600], Loss: 0.0011
Epoch [20/64], Step [300/600], Loss: 0.0003
Epoch [20/64], Step [400/600], Loss: 0.0004
Epoch [20/64], Step [500/600], Loss: 0.0005
Epoch [20/64], Step [600/600], Loss: 0.0008
Epoch [21/64], Step [100/600], Loss: 0.0002
Epoch [21/64], Step [200/600], Loss: 0.0010
Epoch [21/64], Step [300/600], Loss: 0.0002
Epoch [21/64], Step [400/600], Loss: 0.0007
Epoch [21/64], Step [500/600], Loss: 0.0048
Epoch [21/64], Step [600/600], Loss: 0.0016
Epoch [22/64], Step [100/600], Loss: 0.0024
Epoch [22/64], Step [200/600], Loss: 0.0006
Epoch [22/64], Step [300/600], Loss: 0.0002
Epoch [22/64], Step [400/600], Loss: 0.0012
Epoch [22/64], Step [500/600], Loss: 0.0190
Epoch [22/64], Step [600/600], Loss: 0.0014
Epoch [23/64], Step [100/600], Loss: 0.0002
Epoch [23/64], Step [200/600], Loss: 0.0203
Epoch [23/64], Step [300/600], Loss: 0.0009
Epoch [23/64], Step [400/600], Loss: 0.0142
Epoch [23/64], Step [500/600], Loss: 0.0008
Epoch [23/64], Step [600/600], Loss: 0.0005
Epoch [24/64], Step [100/600], Loss: 0.0008
Epoch [24/64], Step [200/600], Loss: 0.0004
Epoch [24/64], Step [300/600], Loss: 0.0007
Epoch [24/64], Step [400/600], Loss: 0.0184
Epoch [24/64], Step [500/600], Loss: 0.0014
Epoch [24/64], Step [600/600], Loss: 0.0013
Epoch [25/64], Step [100/600], Loss: 0.0002
Epoch [25/64], Step [200/600], Loss: 0.0000
Epoch [25/64], Step [300/600], Loss: 0.0002
Epoch [25/64], Step [400/600], Loss: 0.0009
Epoch [25/64], Step [500/600], Loss: 0.0002
Epoch [25/64], Step [600/600], Loss: 0.0003
Epoch [26/64], Step [100/600], Loss: 0.0005
Epoch [26/64], Step [200/600], Loss: 0.0005
Epoch [26/64], Step [300/600], Loss: 0.0000
Epoch [26/64], Step [400/600], Loss: 0.0001
Epoch [26/64], Step [500/600], Loss: 0.0001
Epoch [26/64], Step [600/600], Loss: 0.0001
Epoch [27/64], Step [100/600], Loss: 0.0003
Epoch [27/64], Step [200/600], Loss: 0.0005
Epoch [27/64], Step [300/600], Loss: 0.0004
Epoch [27/64], Step [400/600], Loss: 0.0089
Epoch [27/64], Step [500/600], Loss: 0.0006
Epoch [27/64], Step [600/600], Loss: 0.0382
Epoch [28/64], Step [100/600], Loss: 0.0004
Epoch [28/64], Step [200/600], Loss: 0.0021
Epoch [28/64], Step [300/600], Loss: 0.0001
Epoch [28/64], Step [400/600], Loss: 0.0141
Epoch [28/64], Step [500/600], Loss: 0.0015
Epoch [28/64], Step [600/600], Loss: 0.0007
Epoch [29/64], Step [100/600], Loss: 0.0004
Epoch [29/64], Step [200/600], Loss: 0.0001
Epoch [29/64], Step [300/600], Loss: 0.0006
Epoch [29/64], Step [400/600], Loss: 0.0010
Epoch [29/64], Step [500/600], Loss: 0.0002
Epoch [29/64], Step [600/600], Loss: 0.0001
Epoch [30/64], Step [100/600], Loss: 0.0001
Epoch [30/64], Step [200/600], Loss: 0.0004
Epoch [30/64], Step [300/600], Loss: 0.0004
Epoch [30/64], Step [400/600], Loss: 0.0001
Epoch [30/64], Step [500/600], Loss: 0.0001
Epoch [30/64], Step [600/600], Loss: 0.0001
Epoch [31/64], Step [100/600], Loss: 0.0001
Epoch [31/64], Step [200/600], Loss: 0.0001
Epoch [31/64], Step [300/600], Loss: 0.0001
Epoch [31/64], Step [400/600], Loss: 0.0009
Epoch [31/64], Step [500/600], Loss: 0.0001
Epoch [31/64], Step [600/600], Loss: 0.0002
Epoch [32/64], Step [100/600], Loss: 0.0006
Epoch [32/64], Step [200/600], Loss: 0.0002
Epoch [32/64], Step [300/600], Loss: 0.0000
Epoch [32/64], Step [400/600], Loss: 0.0002
Epoch [32/64], Step [500/600], Loss: 0.0001
Epoch [32/64], Step [600/600], Loss: 0.0023
Epoch [33/64], Step [100/600], Loss: 0.0291
Epoch [33/64], Step [200/600], Loss: 0.0007
Epoch [33/64], Step [300/600], Loss: 0.0046
Epoch [33/64], Step [400/600], Loss: 0.0116
Epoch [33/64], Step [500/600], Loss: 0.0099
Epoch [33/64], Step [600/600], Loss: 0.0009
Epoch [34/64], Step [100/600], Loss: 0.0016
Epoch [34/64], Step [200/600], Loss: 0.0001
Epoch [34/64], Step [300/600], Loss: 0.0003
Epoch [34/64], Step [400/600], Loss: 0.0008
Epoch [34/64], Step [500/600], Loss: 0.0000
Epoch [34/64], Step [600/600], Loss: 0.0000
Epoch [35/64], Step [100/600], Loss: 0.0002
Epoch [35/64], Step [200/600], Loss: 0.0001
Epoch [35/64], Step [300/600], Loss: 0.0023
Epoch [35/64], Step [400/600], Loss: 0.0001
Epoch [35/64], Step [500/600], Loss: 0.0000
Epoch [35/64], Step [600/600], Loss: 0.0001
Epoch [36/64], Step [100/600], Loss: 0.0002
Epoch [36/64], Step [200/600], Loss: 0.0001
Epoch [36/64], Step [300/600], Loss: 0.0000
Epoch [36/64], Step [400/600], Loss: 0.0002
Epoch [36/64], Step [500/600], Loss: 0.0002
Epoch [36/64], Step [600/600], Loss: 0.0002
Epoch [37/64], Step [100/600], Loss: 0.0001
Epoch [37/64], Step [200/600], Loss: 0.0001
Epoch [37/64], Step [300/600], Loss: 0.0001
Epoch [37/64], Step [400/600], Loss: 0.0001
Epoch [37/64], Step [500/600], Loss: 0.0001
Epoch [37/64], Step [600/600], Loss: 0.0002
Epoch [38/64], Step [100/600], Loss: 0.0000
Epoch [38/64], Step [200/600], Loss: 0.0000
Epoch [38/64], Step [300/600], Loss: 0.0001
Epoch [38/64], Step [400/600], Loss: 0.0001
Epoch [38/64], Step [500/600], Loss: 0.0003
Epoch [38/64], Step [600/600], Loss: 0.0001
Epoch [39/64], Step [100/600], Loss: 0.0002
Epoch [39/64], Step [200/600], Loss: 0.0000
Epoch [39/64], Step [300/600], Loss: 0.0000
Epoch [39/64], Step [400/600], Loss: 0.0000
Epoch [39/64], Step [500/600], Loss: 0.0001
Epoch [39/64], Step [600/600], Loss: 0.0000
Epoch [40/64], Step [100/600], Loss: 0.0000
Epoch [40/64], Step [200/600], Loss: 0.0000
Epoch [40/64], Step [300/600], Loss: 0.0001
Epoch [40/64], Step [400/600], Loss: 0.0000
Epoch [40/64], Step [500/600], Loss: 0.0000
Epoch [40/64], Step [600/600], Loss: 0.0001
Epoch [41/64], Step [100/600], Loss: 0.0000
Epoch [41/64], Step [200/600], Loss: 0.0000
Epoch [41/64], Step [300/600], Loss: 0.0000
Epoch [41/64], Step [400/600], Loss: 0.0000
Epoch [41/64], Step [500/600], Loss: 0.0000
Epoch [41/64], Step [600/600], Loss: 0.0001
Epoch [42/64], Step [100/600], Loss: 0.0001
Epoch [42/64], Step [200/600], Loss: 0.0001
Epoch [42/64], Step [300/600], Loss: 0.0000
Epoch [42/64], Step [400/600], Loss: 0.0000
Epoch [42/64], Step [500/600], Loss: 0.0001
Epoch [42/64], Step [600/600], Loss: 0.0001
Epoch [43/64], Step [100/600], Loss: 0.0002
Epoch [43/64], Step [200/600], Loss: 0.0223
Epoch [43/64], Step [300/600], Loss: 0.0004
Epoch [43/64], Step [400/600], Loss: 0.0003
Epoch [43/64], Step [500/600], Loss: 0.0004
Epoch [43/64], Step [600/600], Loss: 0.0016
Epoch [44/64], Step [100/600], Loss: 0.0001
Epoch [44/64], Step [200/600], Loss: 0.0000
Epoch [44/64], Step [300/600], Loss: 0.0003
Epoch [44/64], Step [400/600], Loss: 0.0000
Epoch [44/64], Step [500/600], Loss: 0.0000
Epoch [44/64], Step [600/600], Loss: 0.0002
Epoch [45/64], Step [100/600], Loss: 0.0001
Epoch [45/64], Step [200/600], Loss: 0.0004
Epoch [45/64], Step [300/600], Loss: 0.0000
Epoch [45/64], Step [400/600], Loss: 0.0002
Epoch [45/64], Step [500/600], Loss: 0.0010
Epoch [45/64], Step [600/600], Loss: 0.0001
Epoch [46/64], Step [100/600], Loss: 0.0001
Epoch [46/64], Step [200/600], Loss: 0.0001
Epoch [46/64], Step [300/600], Loss: 0.0002
Epoch [46/64], Step [400/600], Loss: 0.0003
Epoch [46/64], Step [500/600], Loss: 0.0003
Epoch [46/64], Step [600/600], Loss: 0.0001
Epoch [47/64], Step [100/600], Loss: 0.0000
Epoch [47/64], Step [200/600], Loss: 0.0000
Epoch [47/64], Step [300/600], Loss: 0.0001
Epoch [47/64], Step [400/600], Loss: 0.0001
Epoch [47/64], Step [500/600], Loss: 0.0000
Epoch [47/64], Step [600/600], Loss: 0.0000
Epoch [48/64], Step [100/600], Loss: 0.0000
Epoch [48/64], Step [200/600], Loss: 0.0001
Epoch [48/64], Step [300/600], Loss: 0.0000
Epoch [48/64], Step [400/600], Loss: 0.0002
Epoch [48/64], Step [500/600], Loss: 0.0000
Epoch [48/64], Step [600/600], Loss: 0.0002
Epoch [49/64], Step [100/600], Loss: 0.0001
Epoch [49/64], Step [200/600], Loss: 0.0000
Epoch [49/64], Step [300/600], Loss: 0.0000
Epoch [49/64], Step [400/600], Loss: 0.0000
Epoch [49/64], Step [500/600], Loss: 0.0000
Epoch [49/64], Step [600/600], Loss: 0.0000
Epoch [50/64], Step [100/600], Loss: 0.0000
Epoch [50/64], Step [200/600], Loss: 0.0002
Epoch [50/64], Step [300/600], Loss: 0.0001
Epoch [50/64], Step [400/600], Loss: 0.0000
Epoch [50/64], Step [500/600], Loss: 0.0000
Epoch [50/64], Step [600/600], Loss: 0.0001
Epoch [51/64], Step [100/600], Loss: 0.0001
Epoch [51/64], Step [200/600], Loss: 0.0000
Epoch [51/64], Step [300/600], Loss: 0.0000
Epoch [51/64], Step [400/600], Loss: 0.0002
Epoch [51/64], Step [500/600], Loss: 0.0000
Epoch [51/64], Step [600/600], Loss: 0.0000
Epoch [52/64], Step [100/600], Loss: 0.0000
Epoch [52/64], Step [200/600], Loss: 0.0000
Epoch [52/64], Step [300/600], Loss: 0.0000
Epoch [52/64], Step [400/600], Loss: 0.0000
Epoch [52/64], Step [500/600], Loss: 0.0000
Epoch [52/64], Step [600/600], Loss: 0.0002
Epoch [53/64], Step [100/600], Loss: 0.0377
Epoch [53/64], Step [200/600], Loss: 0.0003
Epoch [53/64], Step [300/600], Loss: 0.0002
Epoch [53/64], Step [400/600], Loss: 0.0001
Epoch [53/64], Step [500/600], Loss: 0.0192
Epoch [53/64], Step [600/600], Loss: 0.0004
Epoch [54/64], Step [100/600], Loss: 0.0025
Epoch [54/64], Step [200/600], Loss: 0.0000
Epoch [54/64], Step [300/600], Loss: 0.0011
Epoch [54/64], Step [400/600], Loss: 0.0000
Epoch [54/64], Step [500/600], Loss: 0.0061
Epoch [54/64], Step [600/600], Loss: 0.0001
Epoch [55/64], Step [100/600], Loss: 0.0002
Epoch [55/64], Step [200/600], Loss: 0.0000
Epoch [55/64], Step [300/600], Loss: 0.0000
Epoch [55/64], Step [400/600], Loss: 0.0000
Epoch [55/64], Step [500/600], Loss: 0.0000
Epoch [55/64], Step [600/600], Loss: 0.0000
Epoch [56/64], Step [100/600], Loss: 0.0002
Epoch [56/64], Step [200/600], Loss: 0.0000
Epoch [56/64], Step [300/600], Loss: 0.0001
Epoch [56/64], Step [400/600], Loss: 0.0001
Epoch [56/64], Step [500/600], Loss: 0.0000
Epoch [56/64], Step [600/600], Loss: 0.0000
Epoch [57/64], Step [100/600], Loss: 0.0000
Epoch [57/64], Step [200/600], Loss: 0.0000
Epoch [57/64], Step [300/600], Loss: 0.0000
Epoch [57/64], Step [400/600], Loss: 0.0000
Epoch [57/64], Step [500/600], Loss: 0.0000
Epoch [57/64], Step [600/600], Loss: 0.0001
Epoch [58/64], Step [100/600], Loss: 0.0000
Epoch [58/64], Step [200/600], Loss: 0.0001
Epoch [58/64], Step [300/600], Loss: 0.0000
Epoch [58/64], Step [400/600], Loss: 0.0000
Epoch [58/64], Step [500/600], Loss: 0.0000
Epoch [58/64], Step [600/600], Loss: 0.0000
Epoch [59/64], Step [100/600], Loss: 0.0000
Epoch [59/64], Step [200/600], Loss: 0.0000
Epoch [59/64], Step [300/600], Loss: 0.0000
Epoch [59/64], Step [400/600], Loss: 0.0001
Epoch [59/64], Step [500/600], Loss: 0.0000
Epoch [59/64], Step [600/600], Loss: 0.0000
Epoch [60/64], Step [100/600], Loss: 0.0001
Epoch [60/64], Step [200/600], Loss: 0.0001
Epoch [60/64], Step [300/600], Loss: 0.0000
Epoch [60/64], Step [400/600], Loss: 0.0000
Epoch [60/64], Step [500/600], Loss: 0.0000
Epoch [60/64], Step [600/600], Loss: 0.0002
Epoch [61/64], Step [100/600], Loss: 0.0000
Epoch [61/64], Step [200/600], Loss: 0.0000
Epoch [61/64], Step [300/600], Loss: 0.0000
Epoch [61/64], Step [400/600], Loss: 0.0000
Epoch [61/64], Step [500/600], Loss: 0.0001
Epoch [61/64], Step [600/600], Loss: 0.0001
Epoch [62/64], Step [100/600], Loss: 0.0000
Epoch [62/64], Step [200/600], Loss: 0.0001
Epoch [62/64], Step [300/600], Loss: 0.0000
Epoch [62/64], Step [400/600], Loss: 0.0001
Epoch [62/64], Step [500/600], Loss: 0.0004
Epoch [62/64], Step [600/600], Loss: 0.0001
Epoch [63/64], Step [100/600], Loss: 0.0000
Epoch [63/64], Step [200/600], Loss: 0.0000
Epoch [63/64], Step [300/600], Loss: 0.0000
Epoch [63/64], Step [400/600], Loss: 0.0000
Epoch [63/64], Step [500/600], Loss: 0.0000
Epoch [63/64], Step [600/600], Loss: 0.0001
Epoch [64/64], Step [100/600], Loss: 0.0000
Epoch [64/64], Step [200/600], Loss: 0.0000
Epoch [64/64], Step [300/600], Loss: 0.0000
Epoch [64/64], Step [400/600], Loss: 0.0000
Epoch [64/64], Step [500/600], Loss: 0.0000
Epoch [64/64], Step [600/600], Loss: 0.0001
Pytorch test completed in 472.296 secs

[SSH] completed
[SSH] exit-status: 0

[workspace] $ /bin/sh -xe /tmp/jenkins16709418709348828708.sh
+ scp 'HYDRO_REMOTE:~svchydrojenkins/pytorch_train/time.txt' /var/lib/jenkins/jobs/pytorch_train/workspace
Recording plot data
Saving plot series data from: /var/lib/jenkins/jobs/pytorch_train/workspace/time.txt
Finished: SUCCESS