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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 95693 queued and waiting for resources
srun: job 95693 has been allocated resources
Running benchmark on hydro03
Epoch [1/64], Step [100/600], Loss: 0.2472
Epoch [1/64], Step [200/600], Loss: 0.0748
Epoch [1/64], Step [300/600], Loss: 0.1228
Epoch [1/64], Step [400/600], Loss: 0.0543
Epoch [1/64], Step [500/600], Loss: 0.0575
Epoch [1/64], Step [600/600], Loss: 0.1195
Epoch [2/64], Step [100/600], Loss: 0.0327
Epoch [2/64], Step [200/600], Loss: 0.1201
Epoch [2/64], Step [300/600], Loss: 0.1269
Epoch [2/64], Step [400/600], Loss: 0.0558
Epoch [2/64], Step [500/600], Loss: 0.0121
Epoch [2/64], Step [600/600], Loss: 0.0389
Epoch [3/64], Step [100/600], Loss: 0.0264
Epoch [3/64], Step [200/600], Loss: 0.0384
Epoch [3/64], Step [300/600], Loss: 0.0440
Epoch [3/64], Step [400/600], Loss: 0.0768
Epoch [3/64], Step [500/600], Loss: 0.0263
Epoch [3/64], Step [600/600], Loss: 0.1165
Epoch [4/64], Step [100/600], Loss: 0.0226
Epoch [4/64], Step [200/600], Loss: 0.0348
Epoch [4/64], Step [300/600], Loss: 0.0139
Epoch [4/64], Step [400/600], Loss: 0.0198
Epoch [4/64], Step [500/600], Loss: 0.0045
Epoch [4/64], Step [600/600], Loss: 0.0209
Epoch [5/64], Step [100/600], Loss: 0.0440
Epoch [5/64], Step [200/600], Loss: 0.0896
Epoch [5/64], Step [300/600], Loss: 0.0104
Epoch [5/64], Step [400/600], Loss: 0.0537
Epoch [5/64], Step [500/600], Loss: 0.0133
Epoch [5/64], Step [600/600], Loss: 0.0349
Epoch [6/64], Step [100/600], Loss: 0.0371
Epoch [6/64], Step [200/600], Loss: 0.0505
Epoch [6/64], Step [300/600], Loss: 0.0097
Epoch [6/64], Step [400/600], Loss: 0.0053
Epoch [6/64], Step [500/600], Loss: 0.0720
Epoch [6/64], Step [600/600], Loss: 0.1055
Epoch [7/64], Step [100/600], Loss: 0.0082
Epoch [7/64], Step [200/600], Loss: 0.0061
Epoch [7/64], Step [300/600], Loss: 0.0083
Epoch [7/64], Step [400/600], Loss: 0.0054
Epoch [7/64], Step [500/600], Loss: 0.0220
Epoch [7/64], Step [600/600], Loss: 0.0520
Epoch [8/64], Step [100/600], Loss: 0.0043
Epoch [8/64], Step [200/600], Loss: 0.0289
Epoch [8/64], Step [300/600], Loss: 0.0055
Epoch [8/64], Step [400/600], Loss: 0.0313
Epoch [8/64], Step [500/600], Loss: 0.0101
Epoch [8/64], Step [600/600], Loss: 0.0038
Epoch [9/64], Step [100/600], Loss: 0.0030
Epoch [9/64], Step [200/600], Loss: 0.0357
Epoch [9/64], Step [300/600], Loss: 0.0020
Epoch [9/64], Step [400/600], Loss: 0.0110
Epoch [9/64], Step [500/600], Loss: 0.0054
Epoch [9/64], Step [600/600], Loss: 0.0233
Epoch [10/64], Step [100/600], Loss: 0.0430
Epoch [10/64], Step [200/600], Loss: 0.0013
Epoch [10/64], Step [300/600], Loss: 0.0082
Epoch [10/64], Step [400/600], Loss: 0.0065
Epoch [10/64], Step [500/600], Loss: 0.0025
Epoch [10/64], Step [600/600], Loss: 0.0461
Epoch [11/64], Step [100/600], Loss: 0.0177
Epoch [11/64], Step [200/600], Loss: 0.0131
Epoch [11/64], Step [300/600], Loss: 0.0013
Epoch [11/64], Step [400/600], Loss: 0.0092
Epoch [11/64], Step [500/600], Loss: 0.0088
Epoch [11/64], Step [600/600], Loss: 0.0146
Epoch [12/64], Step [100/600], Loss: 0.0081
Epoch [12/64], Step [200/600], Loss: 0.0040
Epoch [12/64], Step [300/600], Loss: 0.0033
Epoch [12/64], Step [400/600], Loss: 0.0363
Epoch [12/64], Step [500/600], Loss: 0.0098
Epoch [12/64], Step [600/600], Loss: 0.0020
Epoch [13/64], Step [100/600], Loss: 0.0031
Epoch [13/64], Step [200/600], Loss: 0.0004
Epoch [13/64], Step [300/600], Loss: 0.0077
Epoch [13/64], Step [400/600], Loss: 0.0011
Epoch [13/64], Step [500/600], Loss: 0.0046
Epoch [13/64], Step [600/600], Loss: 0.0047
Epoch [14/64], Step [100/600], Loss: 0.0156
Epoch [14/64], Step [200/600], Loss: 0.0009
Epoch [14/64], Step [300/600], Loss: 0.0018
Epoch [14/64], Step [400/600], Loss: 0.0229
Epoch [14/64], Step [500/600], Loss: 0.0007
Epoch [14/64], Step [600/600], Loss: 0.0116
Epoch [15/64], Step [100/600], Loss: 0.0057
Epoch [15/64], Step [200/600], Loss: 0.0058
Epoch [15/64], Step [300/600], Loss: 0.0122
Epoch [15/64], Step [400/600], Loss: 0.0083
Epoch [15/64], Step [500/600], Loss: 0.0114
Epoch [15/64], Step [600/600], Loss: 0.0020
Epoch [16/64], Step [100/600], Loss: 0.0033
Epoch [16/64], Step [200/600], Loss: 0.0011
Epoch [16/64], Step [300/600], Loss: 0.0013
Epoch [16/64], Step [400/600], Loss: 0.0020
Epoch [16/64], Step [500/600], Loss: 0.0094
Epoch [16/64], Step [600/600], Loss: 0.0029
Epoch [17/64], Step [100/600], Loss: 0.0046
Epoch [17/64], Step [200/600], Loss: 0.0031
Epoch [17/64], Step [300/600], Loss: 0.0142
Epoch [17/64], Step [400/600], Loss: 0.0027
Epoch [17/64], Step [500/600], Loss: 0.0007
Epoch [17/64], Step [600/600], Loss: 0.0014
Epoch [18/64], Step [100/600], Loss: 0.0003
Epoch [18/64], Step [200/600], Loss: 0.0035
Epoch [18/64], Step [300/600], Loss: 0.0047
Epoch [18/64], Step [400/600], Loss: 0.0013
Epoch [18/64], Step [500/600], Loss: 0.0006
Epoch [18/64], Step [600/600], Loss: 0.0008
Epoch [19/64], Step [100/600], Loss: 0.0015
Epoch [19/64], Step [200/600], Loss: 0.0131
Epoch [19/64], Step [300/600], Loss: 0.0019
Epoch [19/64], Step [400/600], Loss: 0.0034
Epoch [19/64], Step [500/600], Loss: 0.0155
Epoch [19/64], Step [600/600], Loss: 0.0014
Epoch [20/64], Step [100/600], Loss: 0.0008
Epoch [20/64], Step [200/600], Loss: 0.0018
Epoch [20/64], Step [300/600], Loss: 0.0002
Epoch [20/64], Step [400/600], Loss: 0.0017
Epoch [20/64], Step [500/600], Loss: 0.0007
Epoch [20/64], Step [600/600], Loss: 0.0010
Epoch [21/64], Step [100/600], Loss: 0.0009
Epoch [21/64], Step [200/600], Loss: 0.0063
Epoch [21/64], Step [300/600], Loss: 0.0054
Epoch [21/64], Step [400/600], Loss: 0.0018
Epoch [21/64], Step [500/600], Loss: 0.0014
Epoch [21/64], Step [600/600], Loss: 0.0007
Epoch [22/64], Step [100/600], Loss: 0.0007
Epoch [22/64], Step [200/600], Loss: 0.0001
Epoch [22/64], Step [300/600], Loss: 0.0009
Epoch [22/64], Step [400/600], Loss: 0.0002
Epoch [22/64], Step [500/600], Loss: 0.0010
Epoch [22/64], Step [600/600], Loss: 0.0004
Epoch [23/64], Step [100/600], Loss: 0.0006
Epoch [23/64], Step [200/600], Loss: 0.0011
Epoch [23/64], Step [300/600], Loss: 0.0003
Epoch [23/64], Step [400/600], Loss: 0.0035
Epoch [23/64], Step [500/600], Loss: 0.0051
Epoch [23/64], Step [600/600], Loss: 0.0011
Epoch [24/64], Step [100/600], Loss: 0.0219
Epoch [24/64], Step [200/600], Loss: 0.0270
Epoch [24/64], Step [300/600], Loss: 0.0181
Epoch [24/64], Step [400/600], Loss: 0.0085
Epoch [24/64], Step [500/600], Loss: 0.0023
Epoch [24/64], Step [600/600], Loss: 0.0008
Epoch [25/64], Step [100/600], Loss: 0.0001
Epoch [25/64], Step [200/600], Loss: 0.0016
Epoch [25/64], Step [300/600], Loss: 0.0007
Epoch [25/64], Step [400/600], Loss: 0.0023
Epoch [25/64], Step [500/600], Loss: 0.0009
Epoch [25/64], Step [600/600], Loss: 0.0016
Epoch [26/64], Step [100/600], Loss: 0.0001
Epoch [26/64], Step [200/600], Loss: 0.0002
Epoch [26/64], Step [300/600], Loss: 0.0003
Epoch [26/64], Step [400/600], Loss: 0.0003
Epoch [26/64], Step [500/600], Loss: 0.0003
Epoch [26/64], Step [600/600], Loss: 0.0003
Epoch [27/64], Step [100/600], Loss: 0.0001
Epoch [27/64], Step [200/600], Loss: 0.0001
Epoch [27/64], Step [300/600], Loss: 0.0001
Epoch [27/64], Step [400/600], Loss: 0.0005
Epoch [27/64], Step [500/600], Loss: 0.0002
Epoch [27/64], Step [600/600], Loss: 0.0000
Epoch [28/64], Step [100/600], Loss: 0.0000
Epoch [28/64], Step [200/600], Loss: 0.0001
Epoch [28/64], Step [300/600], Loss: 0.0001
Epoch [28/64], Step [400/600], Loss: 0.0000
Epoch [28/64], Step [500/600], Loss: 0.0001
Epoch [28/64], Step [600/600], Loss: 0.0003
Epoch [29/64], Step [100/600], Loss: 0.0001
Epoch [29/64], Step [200/600], Loss: 0.0000
Epoch [29/64], Step [300/600], Loss: 0.0000
Epoch [29/64], Step [400/600], Loss: 0.0004
Epoch [29/64], Step [500/600], Loss: 0.0085
Epoch [29/64], Step [600/600], Loss: 0.0007
Epoch [30/64], Step [100/600], Loss: 0.0012
Epoch [30/64], Step [200/600], Loss: 0.0107
Epoch [30/64], Step [300/600], Loss: 0.0016
Epoch [30/64], Step [400/600], Loss: 0.0012
Epoch [30/64], Step [500/600], Loss: 0.0045
Epoch [30/64], Step [600/600], Loss: 0.0022
Epoch [31/64], Step [100/600], Loss: 0.0002
Epoch [31/64], Step [200/600], Loss: 0.0002
Epoch [31/64], Step [300/600], Loss: 0.0001
Epoch [31/64], Step [400/600], Loss: 0.0002
Epoch [31/64], Step [500/600], Loss: 0.0101
Epoch [31/64], Step [600/600], Loss: 0.0595
Epoch [32/64], Step [100/600], Loss: 0.0002
Epoch [32/64], Step [200/600], Loss: 0.0000
Epoch [32/64], Step [300/600], Loss: 0.0002
Epoch [32/64], Step [400/600], Loss: 0.0000
Epoch [32/64], Step [500/600], Loss: 0.0011
Epoch [32/64], Step [600/600], Loss: 0.0001
Epoch [33/64], Step [100/600], Loss: 0.0001
Epoch [33/64], Step [200/600], Loss: 0.0002
Epoch [33/64], Step [300/600], Loss: 0.0003
Epoch [33/64], Step [400/600], Loss: 0.0004
Epoch [33/64], Step [500/600], Loss: 0.0020
Epoch [33/64], Step [600/600], Loss: 0.0001
Epoch [34/64], Step [100/600], Loss: 0.0002
Epoch [34/64], Step [200/600], Loss: 0.0004
Epoch [34/64], Step [300/600], Loss: 0.0001
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: 0.0001
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: 0.0000
Epoch [35/64], Step [500/600], Loss: 0.0001
Epoch [35/64], Step [600/600], Loss: 0.0002
Epoch [36/64], Step [100/600], Loss: 0.0001
Epoch [36/64], Step [200/600], Loss: 0.0001
Epoch [36/64], Step [300/600], Loss: 0.0003
Epoch [36/64], Step [400/600], Loss: 0.0003
Epoch [36/64], Step [500/600], Loss: 0.0000
Epoch [36/64], Step [600/600], Loss: 0.0000
Epoch [37/64], Step [100/600], Loss: 0.0000
Epoch [37/64], Step [200/600], Loss: 0.0001
Epoch [37/64], Step [300/600], Loss: 0.0006
Epoch [37/64], Step [400/600], Loss: 0.0000
Epoch [37/64], Step [500/600], Loss: 0.0582
Epoch [37/64], Step [600/600], Loss: 0.0031
Epoch [38/64], Step [100/600], Loss: 0.0189
Epoch [38/64], Step [200/600], Loss: 0.0020
Epoch [38/64], Step [300/600], Loss: 0.0001
Epoch [38/64], Step [400/600], Loss: 0.0002
Epoch [38/64], Step [500/600], Loss: 0.0030
Epoch [38/64], Step [600/600], Loss: 0.0003
Epoch [39/64], Step [100/600], Loss: 0.0001
Epoch [39/64], Step [200/600], Loss: 0.0015
Epoch [39/64], Step [300/600], Loss: 0.0004
Epoch [39/64], Step [400/600], Loss: 0.0002
Epoch [39/64], Step [500/600], Loss: 0.0001
Epoch [39/64], Step [600/600], Loss: 0.0015
Epoch [40/64], Step [100/600], Loss: 0.0001
Epoch [40/64], Step [200/600], Loss: 0.0002
Epoch [40/64], Step [300/600], Loss: 0.0001
Epoch [40/64], Step [400/600], Loss: 0.0004
Epoch [40/64], Step [500/600], Loss: 0.0005
Epoch [40/64], Step [600/600], Loss: 0.0005
Epoch [41/64], Step [100/600], Loss: 0.0003
Epoch [41/64], Step [200/600], Loss: 0.0010
Epoch [41/64], Step [300/600], Loss: 0.0000
Epoch [41/64], Step [400/600], Loss: 0.0010
Epoch [41/64], Step [500/600], Loss: 0.0001
Epoch [41/64], Step [600/600], Loss: 0.0002
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: 0.0001
Epoch [42/64], Step [400/600], Loss: 0.0001
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: 0.0001
Epoch [43/64], Step [400/600], Loss: 0.0002
Epoch [43/64], Step [500/600], Loss: 0.0001
Epoch [43/64], Step [600/600], Loss: 0.0000
Epoch [44/64], Step [100/600], Loss: 0.0001
Epoch [44/64], Step [200/600], Loss: 0.0003
Epoch [44/64], Step [300/600], Loss: 0.0001
Epoch [44/64], Step [400/600], Loss: 0.0000
Epoch [44/64], Step [500/600], Loss: 0.0001
Epoch [44/64], Step [600/600], Loss: 0.0475
Epoch [45/64], Step [100/600], Loss: 0.0086
Epoch [45/64], Step [200/600], Loss: 0.0016
Epoch [45/64], Step [300/600], Loss: 0.0038
Epoch [45/64], Step [400/600], Loss: 0.0025
Epoch [45/64], Step [500/600], Loss: 0.0008
Epoch [45/64], Step [600/600], Loss: 0.0016
Epoch [46/64], Step [100/600], Loss: 0.0028
Epoch [46/64], Step [200/600], Loss: 0.0015
Epoch [46/64], Step [300/600], Loss: 0.0002
Epoch [46/64], Step [400/600], Loss: 0.0142
Epoch [46/64], Step [500/600], Loss: 0.0014
Epoch [46/64], Step [600/600], Loss: 0.0001
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: 0.0001
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: 0.0010
Epoch [48/64], Step [100/600], Loss: 0.0002
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: 0.0015
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: 0.0000
Epoch [49/64], Step [200/600], Loss: 0.0002
Epoch [49/64], Step [300/600], Loss: 0.0000
Epoch [49/64], Step [400/600], Loss: 0.0001
Epoch [49/64], Step [500/600], Loss: 0.0000
Epoch [49/64], Step [600/600], Loss: 0.0001
Epoch [50/64], Step [100/600], Loss: 0.0002
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: 0.0004
Epoch [50/64], Step [500/600], Loss: 0.0002
Epoch [50/64], Step [600/600], Loss: 0.0000
Epoch [51/64], Step [100/600], Loss: 0.0001
Epoch [51/64], Step [200/600], Loss: 0.0001
Epoch [51/64], Step [300/600], Loss: 0.0001
Epoch [51/64], Step [400/600], Loss: 0.0000
Epoch [51/64], Step [500/600], Loss: 0.0001
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: 0.0015
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: 0.0001
Epoch [53/64], Step [100/600], Loss: 0.0001
Epoch [53/64], Step [200/600], Loss: 0.0000
Epoch [53/64], Step [300/600], Loss: 0.0000
Epoch [53/64], Step [400/600], Loss: 0.0000
Epoch [53/64], Step [500/600], Loss: 0.0000
Epoch [53/64], Step [600/600], Loss: 0.0001
Epoch [54/64], Step [100/600], Loss: 0.0001
Epoch [54/64], Step [200/600], Loss: 0.0001
Epoch [54/64], Step [300/600], Loss: 0.0000
Epoch [54/64], Step [400/600], Loss: 0.0000
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: 0.0000
Epoch [55/64], Step [200/600], Loss: 0.0001
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: 0.0001
Epoch [56/64], Step [100/600], Loss: 0.0519
Epoch [56/64], Step [200/600], Loss: 0.0020
Epoch [56/64], Step [300/600], Loss: 0.0001
Epoch [56/64], Step [400/600], Loss: 0.0000
Epoch [56/64], Step [500/600], Loss: 0.0024
Epoch [56/64], Step [600/600], Loss: 0.0023
Epoch [57/64], Step [100/600], Loss: 0.0000
Epoch [57/64], Step [200/600], Loss: 0.0003
Epoch [57/64], Step [300/600], Loss: 0.0004
Epoch [57/64], Step [400/600], Loss: 0.0002
Epoch [57/64], Step [500/600], Loss: 0.0000
Epoch [57/64], Step [600/600], Loss: 0.0000
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: 0.0002
Epoch [58/64], Step [400/600], Loss: 0.0003
Epoch [58/64], Step [500/600], Loss: 0.0001
Epoch [58/64], Step [600/600], Loss: 0.0000
Epoch [59/64], Step [100/600], Loss: 0.0002
Epoch [59/64], Step [200/600], Loss: 0.0002
Epoch [59/64], Step [300/600], Loss: 0.0001
Epoch [59/64], Step [400/600], Loss: 0.0001
Epoch [59/64], Step [500/600], Loss: 0.0001
Epoch [59/64], Step [600/600], Loss: 0.0000
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: 0.0001
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: 0.0002
Epoch [61/64], Step [500/600], Loss: 0.0003
Epoch [61/64], Step [600/600], Loss: 0.0000
Epoch [62/64], Step [100/600], Loss: 0.0000
Epoch [62/64], Step [200/600], Loss: 0.0000
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: 0.0001
Epoch [62/64], Step [600/600], Loss: 0.0000
Epoch [63/64], Step [100/600], Loss: 0.0001
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: 0.0000
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: 0.0000
Pytorch test completed in 447.064 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins3164521535619822631.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