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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 84643 queued and waiting for resources
srun: job 84643 has been allocated resources
Running benchmark on hydro02
Epoch [1/64], Step [100/600], Loss: 0.1639
Epoch [1/64], Step [200/600], Loss: 0.0973
Epoch [1/64], Step [300/600], Loss: 0.1089
Epoch [1/64], Step [400/600], Loss: 0.0366
Epoch [1/64], Step [500/600], Loss: 0.1034
Epoch [1/64], Step [600/600], Loss: 0.0687
Epoch [2/64], Step [100/600], Loss: 0.0671
Epoch [2/64], Step [200/600], Loss: 0.0111
Epoch [2/64], Step [300/600], Loss: 0.0614
Epoch [2/64], Step [400/600], Loss: 0.0394
Epoch [2/64], Step [500/600], Loss: 0.0865
Epoch [2/64], Step [600/600], Loss: 0.0135
Epoch [3/64], Step [100/600], Loss: 0.0398
Epoch [3/64], Step [200/600], Loss: 0.2014
Epoch [3/64], Step [300/600], Loss: 0.0167
Epoch [3/64], Step [400/600], Loss: 0.0285
Epoch [3/64], Step [500/600], Loss: 0.0314
Epoch [3/64], Step [600/600], Loss: 0.0493
Epoch [4/64], Step [100/600], Loss: 0.0049
Epoch [4/64], Step [200/600], Loss: 0.0368
Epoch [4/64], Step [300/600], Loss: 0.0430
Epoch [4/64], Step [400/600], Loss: 0.0156
Epoch [4/64], Step [500/600], Loss: 0.0251
Epoch [4/64], Step [600/600], Loss: 0.0688
Epoch [5/64], Step [100/600], Loss: 0.0423
Epoch [5/64], Step [200/600], Loss: 0.0106
Epoch [5/64], Step [300/600], Loss: 0.0506
Epoch [5/64], Step [400/600], Loss: 0.0082
Epoch [5/64], Step [500/600], Loss: 0.0466
Epoch [5/64], Step [600/600], Loss: 0.0145
Epoch [6/64], Step [100/600], Loss: 0.0188
Epoch [6/64], Step [200/600], Loss: 0.0617
Epoch [6/64], Step [300/600], Loss: 0.0049
Epoch [6/64], Step [400/600], Loss: 0.0040
Epoch [6/64], Step [500/600], Loss: 0.0232
Epoch [6/64], Step [600/600], Loss: 0.0165
Epoch [7/64], Step [100/600], Loss: 0.0052
Epoch [7/64], Step [200/600], Loss: 0.0151
Epoch [7/64], Step [300/600], Loss: 0.0319
Epoch [7/64], Step [400/600], Loss: 0.0198
Epoch [7/64], Step [500/600], Loss: 0.0022
Epoch [7/64], Step [600/600], Loss: 0.0234
Epoch [8/64], Step [100/600], Loss: 0.0135
Epoch [8/64], Step [200/600], Loss: 0.0046
Epoch [8/64], Step [300/600], Loss: 0.0038
Epoch [8/64], Step [400/600], Loss: 0.0122
Epoch [8/64], Step [500/600], Loss: 0.0170
Epoch [8/64], Step [600/600], Loss: 0.0562
Epoch [9/64], Step [100/600], Loss: 0.0008
Epoch [9/64], Step [200/600], Loss: 0.0196
Epoch [9/64], Step [300/600], Loss: 0.0011
Epoch [9/64], Step [400/600], Loss: 0.0129
Epoch [9/64], Step [500/600], Loss: 0.0029
Epoch [9/64], Step [600/600], Loss: 0.0183
Epoch [10/64], Step [100/600], Loss: 0.0029
Epoch [10/64], Step [200/600], Loss: 0.0003
Epoch [10/64], Step [300/600], Loss: 0.0207
Epoch [10/64], Step [400/600], Loss: 0.0079
Epoch [10/64], Step [500/600], Loss: 0.0318
Epoch [10/64], Step [600/600], Loss: 0.0111
Epoch [11/64], Step [100/600], Loss: 0.0052
Epoch [11/64], Step [200/600], Loss: 0.0019
Epoch [11/64], Step [300/600], Loss: 0.0120
Epoch [11/64], Step [400/600], Loss: 0.0013
Epoch [11/64], Step [500/600], Loss: 0.0014
Epoch [11/64], Step [600/600], Loss: 0.0193
Epoch [12/64], Step [100/600], Loss: 0.0050
Epoch [12/64], Step [200/600], Loss: 0.0419
Epoch [12/64], Step [300/600], Loss: 0.0036
Epoch [12/64], Step [400/600], Loss: 0.0011
Epoch [12/64], Step [500/600], Loss: 0.0059
Epoch [12/64], Step [600/600], Loss: 0.0154
Epoch [13/64], Step [100/600], Loss: 0.0008
Epoch [13/64], Step [200/600], Loss: 0.0016
Epoch [13/64], Step [300/600], Loss: 0.0170
Epoch [13/64], Step [400/600], Loss: 0.0016
Epoch [13/64], Step [500/600], Loss: 0.0189
Epoch [13/64], Step [600/600], Loss: 0.0012
Epoch [14/64], Step [100/600], Loss: 0.0139
Epoch [14/64], Step [200/600], Loss: 0.0054
Epoch [14/64], Step [300/600], Loss: 0.0089
Epoch [14/64], Step [400/600], Loss: 0.0046
Epoch [14/64], Step [500/600], Loss: 0.0038
Epoch [14/64], Step [600/600], Loss: 0.0038
Epoch [15/64], Step [100/600], Loss: 0.0007
Epoch [15/64], Step [200/600], Loss: 0.0071
Epoch [15/64], Step [300/600], Loss: 0.0048
Epoch [15/64], Step [400/600], Loss: 0.0024
Epoch [15/64], Step [500/600], Loss: 0.0049
Epoch [15/64], Step [600/600], Loss: 0.0005
Epoch [16/64], Step [100/600], Loss: 0.0019
Epoch [16/64], Step [200/600], Loss: 0.0035
Epoch [16/64], Step [300/600], Loss: 0.0022
Epoch [16/64], Step [400/600], Loss: 0.0004
Epoch [16/64], Step [500/600], Loss: 0.0049
Epoch [16/64], Step [600/600], Loss: 0.0032
Epoch [17/64], Step [100/600], Loss: 0.0018
Epoch [17/64], Step [200/600], Loss: 0.0240
Epoch [17/64], Step [300/600], Loss: 0.0172
Epoch [17/64], Step [400/600], Loss: 0.0009
Epoch [17/64], Step [500/600], Loss: 0.0017
Epoch [17/64], Step [600/600], Loss: 0.0038
Epoch [18/64], Step [100/600], Loss: 0.0000
Epoch [18/64], Step [200/600], Loss: 0.0007
Epoch [18/64], Step [300/600], Loss: 0.0079
Epoch [18/64], Step [400/600], Loss: 0.0002
Epoch [18/64], Step [500/600], Loss: 0.0141
Epoch [18/64], Step [600/600], Loss: 0.0037
Epoch [19/64], Step [100/600], Loss: 0.0034
Epoch [19/64], Step [200/600], Loss: 0.0001
Epoch [19/64], Step [300/600], Loss: 0.0006
Epoch [19/64], Step [400/600], Loss: 0.0128
Epoch [19/64], Step [500/600], Loss: 0.0015
Epoch [19/64], Step [600/600], Loss: 0.0001
Epoch [20/64], Step [100/600], Loss: 0.0196
Epoch [20/64], Step [200/600], Loss: 0.0002
Epoch [20/64], Step [300/600], Loss: 0.0006
Epoch [20/64], Step [400/600], Loss: 0.0023
Epoch [20/64], Step [500/600], Loss: 0.0004
Epoch [20/64], Step [600/600], Loss: 0.0218
Epoch [21/64], Step [100/600], Loss: 0.0034
Epoch [21/64], Step [200/600], Loss: 0.0031
Epoch [21/64], Step [300/600], Loss: 0.0091
Epoch [21/64], Step [400/600], Loss: 0.0005
Epoch [21/64], Step [500/600], Loss: 0.0015
Epoch [21/64], Step [600/600], Loss: 0.0005
Epoch [22/64], Step [100/600], Loss: 0.0007
Epoch [22/64], Step [200/600], Loss: 0.0000
Epoch [22/64], Step [300/600], Loss: 0.0002
Epoch [22/64], Step [400/600], Loss: 0.0022
Epoch [22/64], Step [500/600], Loss: 0.0240
Epoch [22/64], Step [600/600], Loss: 0.0006
Epoch [23/64], Step [100/600], Loss: 0.0008
Epoch [23/64], Step [200/600], Loss: 0.0010
Epoch [23/64], Step [300/600], Loss: 0.0017
Epoch [23/64], Step [400/600], Loss: 0.0027
Epoch [23/64], Step [500/600], Loss: 0.0001
Epoch [23/64], Step [600/600], Loss: 0.0009
Epoch [24/64], Step [100/600], Loss: 0.0002
Epoch [24/64], Step [200/600], Loss: 0.0012
Epoch [24/64], Step [300/600], Loss: 0.0018
Epoch [24/64], Step [400/600], Loss: 0.0002
Epoch [24/64], Step [500/600], Loss: 0.0125
Epoch [24/64], Step [600/600], Loss: 0.0031
Epoch [25/64], Step [100/600], Loss: 0.0010
Epoch [25/64], Step [200/600], Loss: 0.0000
Epoch [25/64], Step [300/600], Loss: 0.0004
Epoch [25/64], Step [400/600], Loss: 0.0008
Epoch [25/64], Step [500/600], Loss: 0.0040
Epoch [25/64], Step [600/600], Loss: 0.0022
Epoch [26/64], Step [100/600], Loss: 0.0000
Epoch [26/64], Step [200/600], Loss: 0.0104
Epoch [26/64], Step [300/600], Loss: 0.0001
Epoch [26/64], Step [400/600], Loss: 0.0029
Epoch [26/64], Step [500/600], Loss: 0.0001
Epoch [26/64], Step [600/600], Loss: 0.0011
Epoch [27/64], Step [100/600], Loss: 0.0001
Epoch [27/64], Step [200/600], Loss: 0.0011
Epoch [27/64], Step [300/600], Loss: 0.0004
Epoch [27/64], Step [400/600], Loss: 0.0000
Epoch [27/64], Step [500/600], Loss: 0.0001
Epoch [27/64], Step [600/600], Loss: 0.0013
Epoch [28/64], Step [100/600], Loss: 0.0000
Epoch [28/64], Step [200/600], Loss: 0.0004
Epoch [28/64], Step [300/600], Loss: 0.0001
Epoch [28/64], Step [400/600], Loss: 0.0001
Epoch [28/64], Step [500/600], Loss: 0.0000
Epoch [28/64], Step [600/600], Loss: 0.0001
Epoch [29/64], Step [100/600], Loss: 0.0001
Epoch [29/64], Step [200/600], Loss: 0.0001
Epoch [29/64], Step [300/600], Loss: 0.0001
Epoch [29/64], Step [400/600], Loss: 0.0001
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: 0.0001
Epoch [30/64], Step [300/600], Loss: 0.0013
Epoch [30/64], Step [400/600], Loss: 0.0345
Epoch [30/64], Step [500/600], Loss: 0.0049
Epoch [30/64], Step [600/600], Loss: 0.0006
Epoch [31/64], Step [100/600], Loss: 0.0011
Epoch [31/64], Step [200/600], Loss: 0.0007
Epoch [31/64], Step [300/600], Loss: 0.0034
Epoch [31/64], Step [400/600], Loss: 0.0014
Epoch [31/64], Step [500/600], Loss: 0.0023
Epoch [31/64], Step [600/600], Loss: 0.0371
Epoch [32/64], Step [100/600], Loss: 0.0002
Epoch [32/64], Step [200/600], Loss: 0.0121
Epoch [32/64], Step [300/600], Loss: 0.0015
Epoch [32/64], Step [400/600], Loss: 0.0002
Epoch [32/64], Step [500/600], Loss: 0.0016
Epoch [32/64], Step [600/600], Loss: 0.0023
Epoch [33/64], Step [100/600], Loss: 0.0004
Epoch [33/64], Step [200/600], Loss: 0.0001
Epoch [33/64], Step [300/600], Loss: 0.0000
Epoch [33/64], Step [400/600], Loss: 0.0001
Epoch [33/64], Step [500/600], Loss: 0.0001
Epoch [33/64], Step [600/600], Loss: 0.0008
Epoch [34/64], Step [100/600], Loss: 0.0001
Epoch [34/64], Step [200/600], Loss: 0.0003
Epoch [34/64], Step [300/600], Loss: 0.0005
Epoch [34/64], Step [400/600], Loss: 0.0001
Epoch [34/64], Step [500/600], Loss: 0.0001
Epoch [34/64], Step [600/600], Loss: 0.0001
Epoch [35/64], Step [100/600], Loss: 0.0000
Epoch [35/64], Step [200/600], Loss: 0.0003
Epoch [35/64], Step [300/600], Loss: 0.0001
Epoch [35/64], Step [400/600], Loss: 0.0001
Epoch [35/64], Step [500/600], Loss: 0.0002
Epoch [35/64], Step [600/600], Loss: 0.0002
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: 0.0001
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: 0.0000
Epoch [37/64], Step [100/600], Loss: 0.0002
Epoch [37/64], Step [200/600], Loss: 0.0001
Epoch [37/64], Step [300/600], Loss: 0.0003
Epoch [37/64], Step [400/600], Loss: 0.0000
Epoch [37/64], Step [500/600], Loss: 0.0001
Epoch [37/64], Step [600/600], Loss: 0.0000
Epoch [38/64], Step [100/600], Loss: 0.0001
Epoch [38/64], Step [200/600], Loss: 0.0000
Epoch [38/64], Step [300/600], Loss: 0.0000
Epoch [38/64], Step [400/600], Loss: 0.0335
Epoch [38/64], Step [500/600], Loss: 0.0443
Epoch [38/64], Step [600/600], Loss: 0.0003
Epoch [39/64], Step [100/600], Loss: 0.0023
Epoch [39/64], Step [200/600], Loss: 0.0014
Epoch [39/64], Step [300/600], Loss: 0.0018
Epoch [39/64], Step [400/600], Loss: 0.0004
Epoch [39/64], Step [500/600], Loss: 0.0012
Epoch [39/64], Step [600/600], Loss: 0.0003
Epoch [40/64], Step [100/600], Loss: 0.0004
Epoch [40/64], Step [200/600], Loss: 0.0001
Epoch [40/64], Step [300/600], Loss: 0.0003
Epoch [40/64], Step [400/600], Loss: 0.0006
Epoch [40/64], Step [500/600], Loss: 0.0004
Epoch [40/64], Step [600/600], Loss: 0.0003
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: 0.0001
Epoch [41/64], Step [500/600], Loss: 0.0000
Epoch [41/64], Step [600/600], Loss: 0.0003
Epoch [42/64], Step [100/600], Loss: 0.0001
Epoch [42/64], Step [200/600], Loss: 0.0000
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: 0.0000
Epoch [42/64], Step [600/600], Loss: 0.0001
Epoch [43/64], Step [100/600], Loss: 0.0000
Epoch [43/64], Step [200/600], Loss: 0.0000
Epoch [43/64], Step [300/600], Loss: 0.0000
Epoch [43/64], Step [400/600], Loss: 0.0000
Epoch [43/64], Step [500/600], Loss: 0.0001
Epoch [43/64], Step [600/600], Loss: 0.0001
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: 0.0000
Epoch [44/64], Step [400/600], Loss: 0.0003
Epoch [44/64], Step [500/600], Loss: 0.0002
Epoch [44/64], Step [600/600], Loss: 0.0000
Epoch [45/64], Step [100/600], Loss: 0.0002
Epoch [45/64], Step [200/600], Loss: 0.0000
Epoch [45/64], Step [300/600], Loss: 0.0000
Epoch [45/64], Step [400/600], Loss: 0.0000
Epoch [45/64], Step [500/600], Loss: 0.0000
Epoch [45/64], Step [600/600], Loss: 0.0000
Epoch [46/64], Step [100/600], Loss: 0.0001
Epoch [46/64], Step [200/600], Loss: 0.0000
Epoch [46/64], Step [300/600], Loss: 0.0001
Epoch [46/64], Step [400/600], Loss: 0.0001
Epoch [46/64], Step [500/600], Loss: 0.0000
Epoch [46/64], Step [600/600], Loss: 0.0000
Epoch [47/64], Step [100/600], Loss: 0.0001
Epoch [47/64], Step [200/600], Loss: 0.0001
Epoch [47/64], Step [300/600], Loss: 0.0000
Epoch [47/64], Step [400/600], Loss: 0.0000
Epoch [47/64], Step [500/600], Loss: 0.0002
Epoch [47/64], Step [600/600], Loss: 0.0000
Epoch [48/64], Step [100/600], Loss: 0.0001
Epoch [48/64], Step [200/600], Loss: 0.0000
Epoch [48/64], Step [300/600], Loss: 0.0001
Epoch [48/64], Step [400/600], Loss: 0.0000
Epoch [48/64], Step [500/600], Loss: 0.0000
Epoch [48/64], Step [600/600], Loss: 0.0000
Epoch [49/64], Step [100/600], Loss: 0.0003
Epoch [49/64], Step [200/600], Loss: 0.0000
Epoch [49/64], Step [300/600], Loss: 0.0005
Epoch [49/64], Step [400/600], Loss: 0.0012
Epoch [49/64], Step [500/600], Loss: 0.0003
Epoch [49/64], Step [600/600], Loss: 0.0004
Epoch [50/64], Step [100/600], Loss: 0.0006
Epoch [50/64], Step [200/600], Loss: 0.0001
Epoch [50/64], Step [300/600], Loss: 0.0001
Epoch [50/64], Step [400/600], Loss: 0.0001
Epoch [50/64], Step [500/600], Loss: 0.0002
Epoch [50/64], Step [600/600], Loss: 0.0001
Epoch [51/64], Step [100/600], Loss: 0.0000
Epoch [51/64], Step [200/600], Loss: 0.0000
Epoch [51/64], Step [300/600], Loss: 0.0001
Epoch [51/64], Step [400/600], Loss: 0.0001
Epoch [51/64], Step [500/600], Loss: 0.0003
Epoch [51/64], Step [600/600], Loss: 0.0001
Epoch [52/64], Step [100/600], Loss: 0.0003
Epoch [52/64], Step [200/600], Loss: 0.0000
Epoch [52/64], Step [300/600], Loss: 0.0001
Epoch [52/64], Step [400/600], Loss: 0.0016
Epoch [52/64], Step [500/600], Loss: 0.0004
Epoch [52/64], Step [600/600], Loss: 0.0001
Epoch [53/64], Step [100/600], Loss: 0.0000
Epoch [53/64], Step [200/600], Loss: 0.0000
Epoch [53/64], Step [300/600], Loss: 0.0001
Epoch [53/64], Step [400/600], Loss: 0.0001
Epoch [53/64], Step [500/600], Loss: 0.0001
Epoch [53/64], Step [600/600], Loss: 0.0000
Epoch [54/64], Step [100/600], Loss: 0.0001
Epoch [54/64], Step [200/600], Loss: 0.0000
Epoch [54/64], Step [300/600], Loss: 0.0002
Epoch [54/64], Step [400/600], Loss: 0.0001
Epoch [54/64], Step [500/600], Loss: 0.0000
Epoch [54/64], Step [600/600], Loss: 0.0000
Epoch [55/64], Step [100/600], Loss: 0.0001
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: 0.0001
Epoch [55/64], Step [500/600], Loss: 0.0000
Epoch [55/64], Step [600/600], Loss: 0.0001
Epoch [56/64], Step [100/600], Loss: 0.0000
Epoch [56/64], Step [200/600], Loss: 0.0001
Epoch [56/64], Step [300/600], Loss: 0.0000
Epoch [56/64], Step [400/600], Loss: 0.0000
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: 0.0001
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: 0.0000
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: 0.0001
Epoch [59/64], Step [300/600], Loss: 0.0000
Epoch [59/64], Step [400/600], Loss: 0.0000
Epoch [59/64], Step [500/600], Loss: 0.0000
Epoch [59/64], Step [600/600], Loss: 0.0004
Epoch [60/64], Step [100/600], Loss: 0.0226
Epoch [60/64], Step [200/600], Loss: 0.0008
Epoch [60/64], Step [300/600], Loss: 0.0595
Epoch [60/64], Step [400/600], Loss: 0.0002
Epoch [60/64], Step [500/600], Loss: 0.0006
Epoch [60/64], Step [600/600], Loss: 0.0000
Epoch [61/64], Step [100/600], Loss: 0.0001
Epoch [61/64], Step [200/600], Loss: 0.0005
Epoch [61/64], Step [300/600], Loss: 0.0000
Epoch [61/64], Step [400/600], Loss: 0.0007
Epoch [61/64], Step [500/600], Loss: 0.0004
Epoch [61/64], Step [600/600], Loss: 0.0004
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: 0.0002
Epoch [62/64], Step [400/600], Loss: 0.0003
Epoch [62/64], Step [500/600], Loss: 0.0000
Epoch [62/64], Step [600/600], Loss: 0.0000
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: 0.0002
Epoch [63/64], Step [400/600], Loss: 0.0001
Epoch [63/64], Step [500/600], Loss: 0.0004
Epoch [63/64], Step [600/600], Loss: 0.0001
Epoch [64/64], Step [100/600], Loss: 0.0001
Epoch [64/64], Step [200/600], Loss: 0.0003
Epoch [64/64], Step [300/600], Loss: 0.0001
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 432.578 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins17322947810811648870.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