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 84088 queued and waiting for resources
srun: job 84088 has been allocated resources
Running benchmark on hydro05
Epoch [1/64], Step [100/600], Loss: 0.1556
Epoch [1/64], Step [200/600], Loss: 0.1192
Epoch [1/64], Step [300/600], Loss: 0.1176
Epoch [1/64], Step [400/600], Loss: 0.0658
Epoch [1/64], Step [500/600], Loss: 0.1263
Epoch [1/64], Step [600/600], Loss: 0.0408
Epoch [2/64], Step [100/600], Loss: 0.0581
Epoch [2/64], Step [200/600], Loss: 0.0168
Epoch [2/64], Step [300/600], Loss: 0.0245
Epoch [2/64], Step [400/600], Loss: 0.0264
Epoch [2/64], Step [500/600], Loss: 0.0920
Epoch [2/64], Step [600/600], Loss: 0.0329
Epoch [3/64], Step [100/600], Loss: 0.0594
Epoch [3/64], Step [200/600], Loss: 0.0875
Epoch [3/64], Step [300/600], Loss: 0.0180
Epoch [3/64], Step [400/600], Loss: 0.1636
Epoch [3/64], Step [500/600], Loss: 0.0117
Epoch [3/64], Step [600/600], Loss: 0.0762
Epoch [4/64], Step [100/600], Loss: 0.0069
Epoch [4/64], Step [200/600], Loss: 0.0350
Epoch [4/64], Step [300/600], Loss: 0.0138
Epoch [4/64], Step [400/600], Loss: 0.0147
Epoch [4/64], Step [500/600], Loss: 0.0176
Epoch [4/64], Step [600/600], Loss: 0.0978
Epoch [5/64], Step [100/600], Loss: 0.0105
Epoch [5/64], Step [200/600], Loss: 0.0199
Epoch [5/64], Step [300/600], Loss: 0.0127
Epoch [5/64], Step [400/600], Loss: 0.0549
Epoch [5/64], Step [500/600], Loss: 0.0282
Epoch [5/64], Step [600/600], Loss: 0.0667
Epoch [6/64], Step [100/600], Loss: 0.0193
Epoch [6/64], Step [200/600], Loss: 0.0139
Epoch [6/64], Step [300/600], Loss: 0.0084
Epoch [6/64], Step [400/600], Loss: 0.1442
Epoch [6/64], Step [500/600], Loss: 0.0256
Epoch [6/64], Step [600/600], Loss: 0.0119
Epoch [7/64], Step [100/600], Loss: 0.0235
Epoch [7/64], Step [200/600], Loss: 0.0074
Epoch [7/64], Step [300/600], Loss: 0.0155
Epoch [7/64], Step [400/600], Loss: 0.0171
Epoch [7/64], Step [500/600], Loss: 0.0019
Epoch [7/64], Step [600/600], Loss: 0.0310
Epoch [8/64], Step [100/600], Loss: 0.0116
Epoch [8/64], Step [200/600], Loss: 0.0079
Epoch [8/64], Step [300/600], Loss: 0.0056
Epoch [8/64], Step [400/600], Loss: 0.0508
Epoch [8/64], Step [500/600], Loss: 0.0009
Epoch [8/64], Step [600/600], Loss: 0.0206
Epoch [9/64], Step [100/600], Loss: 0.0077
Epoch [9/64], Step [200/600], Loss: 0.0021
Epoch [9/64], Step [300/600], Loss: 0.0044
Epoch [9/64], Step [400/600], Loss: 0.0088
Epoch [9/64], Step [500/600], Loss: 0.0046
Epoch [9/64], Step [600/600], Loss: 0.0091
Epoch [10/64], Step [100/600], Loss: 0.0614
Epoch [10/64], Step [200/600], Loss: 0.0074
Epoch [10/64], Step [300/600], Loss: 0.0038
Epoch [10/64], Step [400/600], Loss: 0.0014
Epoch [10/64], Step [500/600], Loss: 0.0142
Epoch [10/64], Step [600/600], Loss: 0.0030
Epoch [11/64], Step [100/600], Loss: 0.0018
Epoch [11/64], Step [200/600], Loss: 0.0035
Epoch [11/64], Step [300/600], Loss: 0.0186
Epoch [11/64], Step [400/600], Loss: 0.0208
Epoch [11/64], Step [500/600], Loss: 0.0182
Epoch [11/64], Step [600/600], Loss: 0.0047
Epoch [12/64], Step [100/600], Loss: 0.0006
Epoch [12/64], Step [200/600], Loss: 0.0009
Epoch [12/64], Step [300/600], Loss: 0.0212
Epoch [12/64], Step [400/600], Loss: 0.0099
Epoch [12/64], Step [500/600], Loss: 0.0027
Epoch [12/64], Step [600/600], Loss: 0.0137
Epoch [13/64], Step [100/600], Loss: 0.0003
Epoch [13/64], Step [200/600], Loss: 0.0320
Epoch [13/64], Step [300/600], Loss: 0.0010
Epoch [13/64], Step [400/600], Loss: 0.0020
Epoch [13/64], Step [500/600], Loss: 0.0029
Epoch [13/64], Step [600/600], Loss: 0.0127
Epoch [14/64], Step [100/600], Loss: 0.0123
Epoch [14/64], Step [200/600], Loss: 0.0095
Epoch [14/64], Step [300/600], Loss: 0.0003
Epoch [14/64], Step [400/600], Loss: 0.0008
Epoch [14/64], Step [500/600], Loss: 0.0047
Epoch [14/64], Step [600/600], Loss: 0.0241
Epoch [15/64], Step [100/600], Loss: 0.0044
Epoch [15/64], Step [200/600], Loss: 0.0020
Epoch [15/64], Step [300/600], Loss: 0.0020
Epoch [15/64], Step [400/600], Loss: 0.0016
Epoch [15/64], Step [500/600], Loss: 0.0142
Epoch [15/64], Step [600/600], Loss: 0.0059
Epoch [16/64], Step [100/600], Loss: 0.0017
Epoch [16/64], Step [200/600], Loss: 0.0039
Epoch [16/64], Step [300/600], Loss: 0.0058
Epoch [16/64], Step [400/600], Loss: 0.0123
Epoch [16/64], Step [500/600], Loss: 0.0176
Epoch [16/64], Step [600/600], Loss: 0.0035
Epoch [17/64], Step [100/600], Loss: 0.0005
Epoch [17/64], Step [200/600], Loss: 0.0016
Epoch [17/64], Step [300/600], Loss: 0.0006
Epoch [17/64], Step [400/600], Loss: 0.0016
Epoch [17/64], Step [500/600], Loss: 0.0016
Epoch [17/64], Step [600/600], Loss: 0.0009
Epoch [18/64], Step [100/600], Loss: 0.0022
Epoch [18/64], Step [200/600], Loss: 0.0007
Epoch [18/64], Step [300/600], Loss: 0.0008
Epoch [18/64], Step [400/600], Loss: 0.0008
Epoch [18/64], Step [500/600], Loss: 0.0106
Epoch [18/64], Step [600/600], Loss: 0.0303
Epoch [19/64], Step [100/600], Loss: 0.0008
Epoch [19/64], Step [200/600], Loss: 0.0018
Epoch [19/64], Step [300/600], Loss: 0.0044
Epoch [19/64], Step [400/600], Loss: 0.0016
Epoch [19/64], Step [500/600], Loss: 0.0018
Epoch [19/64], Step [600/600], Loss: 0.0004
Epoch [20/64], Step [100/600], Loss: 0.0004
Epoch [20/64], Step [200/600], Loss: 0.0003
Epoch [20/64], Step [300/600], Loss: 0.0001
Epoch [20/64], Step [400/600], Loss: 0.0001
Epoch [20/64], Step [500/600], Loss: 0.0006
Epoch [20/64], Step [600/600], Loss: 0.0007
Epoch [21/64], Step [100/600], Loss: 0.0006
Epoch [21/64], Step [200/600], Loss: 0.0004
Epoch [21/64], Step [300/600], Loss: 0.0013
Epoch [21/64], Step [400/600], Loss: 0.0015
Epoch [21/64], Step [500/600], Loss: 0.0002
Epoch [21/64], Step [600/600], Loss: 0.0201
Epoch [22/64], Step [100/600], Loss: 0.0006
Epoch [22/64], Step [200/600], Loss: 0.0005
Epoch [22/64], Step [300/600], Loss: 0.0005
Epoch [22/64], Step [400/600], Loss: 0.0028
Epoch [22/64], Step [500/600], Loss: 0.0092
Epoch [22/64], Step [600/600], Loss: 0.0020
Epoch [23/64], Step [100/600], Loss: 0.0069
Epoch [23/64], Step [200/600], Loss: 0.0041
Epoch [23/64], Step [300/600], Loss: 0.0001
Epoch [23/64], Step [400/600], Loss: 0.0002
Epoch [23/64], Step [500/600], Loss: 0.0012
Epoch [23/64], Step [600/600], Loss: 0.0030
Epoch [24/64], Step [100/600], Loss: 0.0169
Epoch [24/64], Step [200/600], Loss: 0.0312
Epoch [24/64], Step [300/600], Loss: 0.0054
Epoch [24/64], Step [400/600], Loss: 0.0035
Epoch [24/64], Step [500/600], Loss: 0.0004
Epoch [24/64], Step [600/600], Loss: 0.0009
Epoch [25/64], Step [100/600], Loss: 0.0018
Epoch [25/64], Step [200/600], Loss: 0.0002
Epoch [25/64], Step [300/600], Loss: 0.0001
Epoch [25/64], Step [400/600], Loss: 0.0002
Epoch [25/64], Step [500/600], Loss: 0.0001
Epoch [25/64], Step [600/600], Loss: 0.0001
Epoch [26/64], Step [100/600], Loss: 0.0000
Epoch [26/64], Step [200/600], Loss: 0.0006
Epoch [26/64], Step [300/600], Loss: 0.0001
Epoch [26/64], Step [400/600], Loss: 0.0001
Epoch [26/64], Step [500/600], Loss: 0.0008
Epoch [26/64], Step [600/600], Loss: 0.0009
Epoch [27/64], Step [100/600], Loss: 0.0002
Epoch [27/64], Step [200/600], Loss: 0.0002
Epoch [27/64], Step [300/600], Loss: 0.0000
Epoch [27/64], Step [400/600], Loss: 0.0002
Epoch [27/64], Step [500/600], Loss: 0.0003
Epoch [27/64], Step [600/600], Loss: 0.0001
Epoch [28/64], Step [100/600], Loss: 0.0001
Epoch [28/64], Step [200/600], Loss: 0.0003
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: 0.0002
Epoch [28/64], Step [600/600], Loss: 0.0000
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: 0.0004
Epoch [29/64], Step [600/600], Loss: 0.0002
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: 0.0000
Epoch [30/64], Step [400/600], Loss: 0.0000
Epoch [30/64], Step [500/600], Loss: 0.0002
Epoch [30/64], Step [600/600], Loss: 0.0284
Epoch [31/64], Step [100/600], Loss: 0.0030
Epoch [31/64], Step [200/600], Loss: 0.0051
Epoch [31/64], Step [300/600], Loss: 0.0001
Epoch [31/64], Step [400/600], Loss: 0.0093
Epoch [31/64], Step [500/600], Loss: 0.0125
Epoch [31/64], Step [600/600], Loss: 0.0010
Epoch [32/64], Step [100/600], Loss: 0.0005
Epoch [32/64], Step [200/600], Loss: 0.0014
Epoch [32/64], Step [300/600], Loss: 0.0004
Epoch [32/64], Step [400/600], Loss: 0.0003
Epoch [32/64], Step [500/600], Loss: 0.0002
Epoch [32/64], Step [600/600], Loss: 0.0002
Epoch [33/64], Step [100/600], Loss: 0.0045
Epoch [33/64], Step [200/600], Loss: 0.0001
Epoch [33/64], Step [300/600], Loss: 0.0001
Epoch [33/64], Step [400/600], Loss: 0.0005
Epoch [33/64], Step [500/600], Loss: 0.0002
Epoch [33/64], Step [600/600], Loss: 0.0001
Epoch [34/64], Step [100/600], Loss: 0.0003
Epoch [34/64], Step [200/600], Loss: 0.0001
Epoch [34/64], Step [300/600], Loss: 0.0001
Epoch [34/64], Step [400/600], Loss: 0.0000
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: 0.0003
Epoch [35/64], Step [200/600], Loss: 0.0000
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: 0.0005
Epoch [35/64], Step [600/600], Loss: 0.0001
Epoch [36/64], Step [100/600], Loss: 0.0000
Epoch [36/64], Step [200/600], Loss: 0.0000
Epoch [36/64], Step [300/600], Loss: 0.0001
Epoch [36/64], Step [400/600], Loss: 0.0001
Epoch [36/64], Step [500/600], Loss: 0.0003
Epoch [36/64], Step [600/600], Loss: 0.0001
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: 0.0000
Epoch [37/64], Step [400/600], Loss: 0.0001
Epoch [37/64], Step [500/600], Loss: 0.0000
Epoch [37/64], Step [600/600], Loss: 0.0002
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: 0.0001
Epoch [38/64], Step [400/600], Loss: 0.0001
Epoch [38/64], Step [500/600], Loss: 0.0000
Epoch [38/64], Step [600/600], Loss: 0.0006
Epoch [39/64], Step [100/600], Loss: 0.0000
Epoch [39/64], Step [200/600], Loss: 0.0001
Epoch [39/64], Step [300/600], Loss: 0.0000
Epoch [39/64], Step [400/600], Loss: 0.0001
Epoch [39/64], Step [500/600], Loss: 0.0000
Epoch [39/64], Step [600/600], Loss: 0.0146
Epoch [40/64], Step [100/600], Loss: 0.0437
Epoch [40/64], Step [200/600], Loss: 0.0321
Epoch [40/64], Step [300/600], Loss: 0.0001
Epoch [40/64], Step [400/600], Loss: 0.0008
Epoch [40/64], Step [500/600], Loss: 0.0108
Epoch [40/64], Step [600/600], Loss: 0.0088
Epoch [41/64], Step [100/600], Loss: 0.0003
Epoch [41/64], Step [200/600], Loss: 0.0002
Epoch [41/64], Step [300/600], Loss: 0.0003
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: 0.0011
Epoch [42/64], Step [100/600], Loss: 0.0022
Epoch [42/64], Step [200/600], Loss: 0.0003
Epoch [42/64], Step [300/600], Loss: 0.0001
Epoch [42/64], Step [400/600], Loss: 0.0006
Epoch [42/64], Step [500/600], Loss: 0.0000
Epoch [42/64], Step [600/600], Loss: 0.0000
Epoch [43/64], Step [100/600], Loss: 0.0000
Epoch [43/64], Step [200/600], Loss: 0.0001
Epoch [43/64], Step [300/600], Loss: 0.0000
Epoch [43/64], Step [400/600], Loss: 0.0001
Epoch [43/64], Step [500/600], Loss: 0.0000
Epoch [43/64], Step [600/600], Loss: 0.0000
Epoch [44/64], Step [100/600], Loss: 0.0003
Epoch [44/64], Step [200/600], Loss: 0.0020
Epoch [44/64], Step [300/600], Loss: 0.0000
Epoch [44/64], Step [400/600], Loss: 0.0009
Epoch [44/64], Step [500/600], Loss: 0.0001
Epoch [44/64], Step [600/600], Loss: 0.0003
Epoch [45/64], Step [100/600], Loss: 0.0000
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: 0.0001
Epoch [45/64], Step [500/600], Loss: 0.0002
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: 0.0000
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: 0.0001
Epoch [46/64], Step [600/600], Loss: 0.0000
Epoch [47/64], Step [100/600], Loss: 0.0000
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: 0.0000
Epoch [47/64], Step [600/600], Loss: 0.0001
Epoch [48/64], Step [100/600], Loss: 0.0000
Epoch [48/64], Step [200/600], Loss: 0.0000
Epoch [48/64], Step [300/600], Loss: 0.0000
Epoch [48/64], Step [400/600], Loss: 0.0000
Epoch [48/64], Step [500/600], Loss: 0.0002
Epoch [48/64], Step [600/600], Loss: 0.0000
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: 0.0001
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: 0.1483
Epoch [51/64], Step [100/600], Loss: 0.0231
Epoch [51/64], Step [200/600], Loss: 0.0012
Epoch [51/64], Step [300/600], Loss: 0.0033
Epoch [51/64], Step [400/600], Loss: 0.0007
Epoch [51/64], Step [500/600], Loss: 0.0037
Epoch [51/64], Step [600/600], Loss: 0.0037
Epoch [52/64], Step [100/600], Loss: 0.0245
Epoch [52/64], Step [200/600], Loss: 0.0001
Epoch [52/64], Step [300/600], Loss: 0.0001
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: 0.0003
Epoch [53/64], Step [100/600], Loss: 0.0000
Epoch [53/64], Step [200/600], Loss: 0.0013
Epoch [53/64], Step [300/600], Loss: 0.0000
Epoch [53/64], Step [400/600], Loss: 0.0001
Epoch [53/64], Step [500/600], Loss: 0.0005
Epoch [53/64], Step [600/600], Loss: 0.0004
Epoch [54/64], Step [100/600], Loss: 0.0001
Epoch [54/64], Step [200/600], Loss: 0.0006
Epoch [54/64], Step [300/600], Loss: 0.0001
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: 0.0001
Epoch [55/64], Step [100/600], Loss: 0.0000
Epoch [55/64], Step [200/600], Loss: 0.0001
Epoch [55/64], Step [300/600], Loss: 0.0002
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: 0.0001
Epoch [56/64], Step [200/600], Loss: 0.0000
Epoch [56/64], Step [300/600], Loss: 0.0002
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: 0.0002
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: 0.0001
Epoch [58/64], Step [600/600], Loss: 0.0001
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: 0.0001
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: 0.0001
Epoch [60/64], Step [100/600], Loss: 0.0000
Epoch [60/64], Step [200/600], Loss: 0.0000
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: 0.0000
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: 0.0000
Epoch [62/64], Step [100/600], Loss: 0.0003
Epoch [62/64], Step [200/600], Loss: 0.0000
Epoch [62/64], Step [300/600], Loss: 0.0001
Epoch [62/64], Step [400/600], Loss: 0.0000
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: 0.0000
Epoch [63/64], Step [400/600], Loss: 0.0000
Epoch [63/64], Step [500/600], Loss: 0.0001
Epoch [63/64], Step [600/600], Loss: 0.0573
Epoch [64/64], Step [100/600], Loss: 0.0023
Epoch [64/64], Step [200/600], Loss: 0.0066
Epoch [64/64], Step [300/600], Loss: 0.0027
Epoch [64/64], Step [400/600], Loss: 0.0074
Epoch [64/64], Step [500/600], Loss: 0.0000
Epoch [64/64], Step [600/600], Loss: 0.0003
Pytorch test completed in 448.008 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins3093868967198751372.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