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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 84574 queued and waiting for resources
srun: job 84574 has been allocated resources
Running benchmark on hydro05
Epoch [1/64], Step [100/600], Loss: 0.2531
Epoch [1/64], Step [200/600], Loss: 0.1102
Epoch [1/64], Step [300/600], Loss: 0.0667
Epoch [1/64], Step [400/600], Loss: 0.0657
Epoch [1/64], Step [500/600], Loss: 0.0431
Epoch [1/64], Step [600/600], Loss: 0.0627
Epoch [2/64], Step [100/600], Loss: 0.0465
Epoch [2/64], Step [200/600], Loss: 0.1000
Epoch [2/64], Step [300/600], Loss: 0.0258
Epoch [2/64], Step [400/600], Loss: 0.1055
Epoch [2/64], Step [500/600], Loss: 0.0729
Epoch [2/64], Step [600/600], Loss: 0.0596
Epoch [3/64], Step [100/600], Loss: 0.0438
Epoch [3/64], Step [200/600], Loss: 0.0124
Epoch [3/64], Step [300/600], Loss: 0.0195
Epoch [3/64], Step [400/600], Loss: 0.0168
Epoch [3/64], Step [500/600], Loss: 0.0587
Epoch [3/64], Step [600/600], Loss: 0.0647
Epoch [4/64], Step [100/600], Loss: 0.0077
Epoch [4/64], Step [200/600], Loss: 0.0328
Epoch [4/64], Step [300/600], Loss: 0.0694
Epoch [4/64], Step [400/600], Loss: 0.0096
Epoch [4/64], Step [500/600], Loss: 0.0131
Epoch [4/64], Step [600/600], Loss: 0.0116
Epoch [5/64], Step [100/600], Loss: 0.1069
Epoch [5/64], Step [200/600], Loss: 0.0263
Epoch [5/64], Step [300/600], Loss: 0.0308
Epoch [5/64], Step [400/600], Loss: 0.0756
Epoch [5/64], Step [500/600], Loss: 0.0073
Epoch [5/64], Step [600/600], Loss: 0.0199
Epoch [6/64], Step [100/600], Loss: 0.0186
Epoch [6/64], Step [200/600], Loss: 0.0186
Epoch [6/64], Step [300/600], Loss: 0.0271
Epoch [6/64], Step [400/600], Loss: 0.0179
Epoch [6/64], Step [500/600], Loss: 0.0284
Epoch [6/64], Step [600/600], Loss: 0.0088
Epoch [7/64], Step [100/600], Loss: 0.0121
Epoch [7/64], Step [200/600], Loss: 0.0039
Epoch [7/64], Step [300/600], Loss: 0.0118
Epoch [7/64], Step [400/600], Loss: 0.0615
Epoch [7/64], Step [500/600], Loss: 0.0107
Epoch [7/64], Step [600/600], Loss: 0.0211
Epoch [8/64], Step [100/600], Loss: 0.0184
Epoch [8/64], Step [200/600], Loss: 0.0033
Epoch [8/64], Step [300/600], Loss: 0.0092
Epoch [8/64], Step [400/600], Loss: 0.0029
Epoch [8/64], Step [500/600], Loss: 0.0120
Epoch [8/64], Step [600/600], Loss: 0.0034
Epoch [9/64], Step [100/600], Loss: 0.0035
Epoch [9/64], Step [200/600], Loss: 0.0109
Epoch [9/64], Step [300/600], Loss: 0.0028
Epoch [9/64], Step [400/600], Loss: 0.0012
Epoch [9/64], Step [500/600], Loss: 0.0262
Epoch [9/64], Step [600/600], Loss: 0.0251
Epoch [10/64], Step [100/600], Loss: 0.0248
Epoch [10/64], Step [200/600], Loss: 0.0311
Epoch [10/64], Step [300/600], Loss: 0.0007
Epoch [10/64], Step [400/600], Loss: 0.0073
Epoch [10/64], Step [500/600], Loss: 0.0355
Epoch [10/64], Step [600/600], Loss: 0.0125
Epoch [11/64], Step [100/600], Loss: 0.0048
Epoch [11/64], Step [200/600], Loss: 0.0079
Epoch [11/64], Step [300/600], Loss: 0.0165
Epoch [11/64], Step [400/600], Loss: 0.0190
Epoch [11/64], Step [500/600], Loss: 0.0044
Epoch [11/64], Step [600/600], Loss: 0.0037
Epoch [12/64], Step [100/600], Loss: 0.0065
Epoch [12/64], Step [200/600], Loss: 0.0039
Epoch [12/64], Step [300/600], Loss: 0.0454
Epoch [12/64], Step [400/600], Loss: 0.0121
Epoch [12/64], Step [500/600], Loss: 0.0004
Epoch [12/64], Step [600/600], Loss: 0.0019
Epoch [13/64], Step [100/600], Loss: 0.0023
Epoch [13/64], Step [200/600], Loss: 0.0183
Epoch [13/64], Step [300/600], Loss: 0.0034
Epoch [13/64], Step [400/600], Loss: 0.0050
Epoch [13/64], Step [500/600], Loss: 0.0024
Epoch [13/64], Step [600/600], Loss: 0.0034
Epoch [14/64], Step [100/600], Loss: 0.0017
Epoch [14/64], Step [200/600], Loss: 0.0058
Epoch [14/64], Step [300/600], Loss: 0.0074
Epoch [14/64], Step [400/600], Loss: 0.0183
Epoch [14/64], Step [500/600], Loss: 0.0506
Epoch [14/64], Step [600/600], Loss: 0.0025
Epoch [15/64], Step [100/600], Loss: 0.0009
Epoch [15/64], Step [200/600], Loss: 0.0003
Epoch [15/64], Step [300/600], Loss: 0.0064
Epoch [15/64], Step [400/600], Loss: 0.0103
Epoch [15/64], Step [500/600], Loss: 0.0011
Epoch [15/64], Step [600/600], Loss: 0.0002
Epoch [16/64], Step [100/600], Loss: 0.0011
Epoch [16/64], Step [200/600], Loss: 0.0007
Epoch [16/64], Step [300/600], Loss: 0.0063
Epoch [16/64], Step [400/600], Loss: 0.0020
Epoch [16/64], Step [500/600], Loss: 0.0016
Epoch [16/64], Step [600/600], Loss: 0.0076
Epoch [17/64], Step [100/600], Loss: 0.0010
Epoch [17/64], Step [200/600], Loss: 0.0006
Epoch [17/64], Step [300/600], Loss: 0.0002
Epoch [17/64], Step [400/600], Loss: 0.0015
Epoch [17/64], Step [500/600], Loss: 0.0043
Epoch [17/64], Step [600/600], Loss: 0.0006
Epoch [18/64], Step [100/600], Loss: 0.0087
Epoch [18/64], Step [200/600], Loss: 0.0034
Epoch [18/64], Step [300/600], Loss: 0.0005
Epoch [18/64], Step [400/600], Loss: 0.0013
Epoch [18/64], Step [500/600], Loss: 0.0007
Epoch [18/64], Step [600/600], Loss: 0.0036
Epoch [19/64], Step [100/600], Loss: 0.0053
Epoch [19/64], Step [200/600], Loss: 0.0031
Epoch [19/64], Step [300/600], Loss: 0.0015
Epoch [19/64], Step [400/600], Loss: 0.0016
Epoch [19/64], Step [500/600], Loss: 0.0066
Epoch [19/64], Step [600/600], Loss: 0.0247
Epoch [20/64], Step [100/600], Loss: 0.0068
Epoch [20/64], Step [200/600], Loss: 0.0010
Epoch [20/64], Step [300/600], Loss: 0.0041
Epoch [20/64], Step [400/600], Loss: 0.0177
Epoch [20/64], Step [500/600], Loss: 0.0140
Epoch [20/64], Step [600/600], Loss: 0.0008
Epoch [21/64], Step [100/600], Loss: 0.0026
Epoch [21/64], Step [200/600], Loss: 0.0002
Epoch [21/64], Step [300/600], Loss: 0.0002
Epoch [21/64], Step [400/600], Loss: 0.0010
Epoch [21/64], Step [500/600], Loss: 0.0020
Epoch [21/64], Step [600/600], Loss: 0.0110
Epoch [22/64], Step [100/600], Loss: 0.0026
Epoch [22/64], Step [200/600], Loss: 0.0045
Epoch [22/64], Step [300/600], Loss: 0.0007
Epoch [22/64], Step [400/600], Loss: 0.0012
Epoch [22/64], Step [500/600], Loss: 0.0012
Epoch [22/64], Step [600/600], Loss: 0.0385
Epoch [23/64], Step [100/600], Loss: 0.0012
Epoch [23/64], Step [200/600], Loss: 0.0007
Epoch [23/64], Step [300/600], Loss: 0.0015
Epoch [23/64], Step [400/600], Loss: 0.0035
Epoch [23/64], Step [500/600], Loss: 0.0024
Epoch [23/64], Step [600/600], Loss: 0.0010
Epoch [24/64], Step [100/600], Loss: 0.0466
Epoch [24/64], Step [200/600], Loss: 0.0006
Epoch [24/64], Step [300/600], Loss: 0.0048
Epoch [24/64], Step [400/600], Loss: 0.0011
Epoch [24/64], Step [500/600], Loss: 0.0004
Epoch [24/64], Step [600/600], Loss: 0.0015
Epoch [25/64], Step [100/600], Loss: 0.0025
Epoch [25/64], Step [200/600], Loss: 0.0008
Epoch [25/64], Step [300/600], Loss: 0.0002
Epoch [25/64], Step [400/600], Loss: 0.0001
Epoch [25/64], Step [500/600], Loss: 0.0004
Epoch [25/64], Step [600/600], Loss: 0.0001
Epoch [26/64], Step [100/600], Loss: 0.0008
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: 0.0000
Epoch [26/64], Step [500/600], Loss: 0.0010
Epoch [26/64], Step [600/600], Loss: 0.0001
Epoch [27/64], Step [100/600], Loss: 0.0001
Epoch [27/64], Step [200/600], Loss: 0.0003
Epoch [27/64], Step [300/600], Loss: 0.0006
Epoch [27/64], Step [400/600], Loss: 0.0003
Epoch [27/64], Step [500/600], Loss: 0.0002
Epoch [27/64], Step [600/600], Loss: 0.0008
Epoch [28/64], Step [100/600], Loss: 0.0093
Epoch [28/64], Step [200/600], Loss: 0.0001
Epoch [28/64], Step [300/600], Loss: 0.0019
Epoch [28/64], Step [400/600], Loss: 0.0010
Epoch [28/64], Step [500/600], Loss: 0.0014
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: 0.0036
Epoch [29/64], Step [300/600], Loss: 0.0001
Epoch [29/64], Step [400/600], Loss: 0.0004
Epoch [29/64], Step [500/600], Loss: 0.0000
Epoch [29/64], Step [600/600], Loss: 0.0003
Epoch [30/64], Step [100/600], Loss: 0.0002
Epoch [30/64], Step [200/600], Loss: 0.0001
Epoch [30/64], Step [300/600], Loss: 0.0001
Epoch [30/64], Step [400/600], Loss: 0.0001
Epoch [30/64], Step [500/600], Loss: 0.0003
Epoch [30/64], Step [600/600], Loss: 0.0026
Epoch [31/64], Step [100/600], Loss: 0.0000
Epoch [31/64], Step [200/600], Loss: 0.0000
Epoch [31/64], Step [300/600], Loss: 0.0001
Epoch [31/64], Step [400/600], Loss: 0.0004
Epoch [31/64], Step [500/600], Loss: 0.0007
Epoch [31/64], Step [600/600], Loss: 0.0000
Epoch [32/64], Step [100/600], Loss: 0.0005
Epoch [32/64], Step [200/600], Loss: 0.0005
Epoch [32/64], Step [300/600], Loss: 0.0015
Epoch [32/64], Step [400/600], Loss: 0.0031
Epoch [32/64], Step [500/600], Loss: 0.0011
Epoch [32/64], Step [600/600], Loss: 0.0019
Epoch [33/64], Step [100/600], Loss: 0.0005
Epoch [33/64], Step [200/600], Loss: 0.0002
Epoch [33/64], Step [300/600], Loss: 0.0011
Epoch [33/64], Step [400/600], Loss: 0.0003
Epoch [33/64], Step [500/600], Loss: 0.0012
Epoch [33/64], Step [600/600], Loss: 0.0002
Epoch [34/64], Step [100/600], Loss: 0.0142
Epoch [34/64], Step [200/600], Loss: 0.0009
Epoch [34/64], Step [300/600], Loss: 0.0002
Epoch [34/64], Step [400/600], Loss: 0.0001
Epoch [34/64], Step [500/600], Loss: 0.0005
Epoch [34/64], Step [600/600], Loss: 0.0014
Epoch [35/64], Step [100/600], Loss: 0.0001
Epoch [35/64], Step [200/600], Loss: 0.0265
Epoch [35/64], Step [300/600], Loss: 0.0003
Epoch [35/64], Step [400/600], Loss: 0.0001
Epoch [35/64], Step [500/600], Loss: 0.0020
Epoch [35/64], Step [600/600], Loss: 0.0060
Epoch [36/64], Step [100/600], Loss: 0.0002
Epoch [36/64], Step [200/600], Loss: 0.0002
Epoch [36/64], Step [300/600], Loss: 0.0001
Epoch [36/64], Step [400/600], Loss: 0.0025
Epoch [36/64], Step [500/600], Loss: 0.0007
Epoch [36/64], Step [600/600], Loss: 0.0036
Epoch [37/64], Step [100/600], Loss: 0.0001
Epoch [37/64], Step [200/600], Loss: 0.0009
Epoch [37/64], Step [300/600], Loss: 0.0001
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: 0.0002
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: 0.0003
Epoch [38/64], Step [400/600], Loss: 0.0001
Epoch [38/64], Step [500/600], Loss: 0.0007
Epoch [38/64], Step [600/600], Loss: 0.0002
Epoch [39/64], Step [100/600], Loss: 0.0002
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: 0.0001
Epoch [39/64], Step [600/600], Loss: 0.0001
Epoch [40/64], Step [100/600], Loss: 0.0001
Epoch [40/64], Step [200/600], Loss: 0.0001
Epoch [40/64], Step [300/600], Loss: 0.0001
Epoch [40/64], Step [400/600], Loss: 0.0001
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: 0.0001
Epoch [41/64], Step [300/600], Loss: 0.0001
Epoch [41/64], Step [400/600], Loss: 0.0000
Epoch [41/64], Step [500/600], Loss: 0.0003
Epoch [41/64], Step [600/600], Loss: 0.0000
Epoch [42/64], Step [100/600], Loss: 0.0011
Epoch [42/64], Step [200/600], Loss: 0.0032
Epoch [42/64], Step [300/600], Loss: 0.0006
Epoch [42/64], Step [400/600], Loss: 0.0005
Epoch [42/64], Step [500/600], Loss: 0.0005
Epoch [42/64], Step [600/600], Loss: 0.0043
Epoch [43/64], Step [100/600], Loss: 0.0001
Epoch [43/64], Step [200/600], Loss: 0.0003
Epoch [43/64], Step [300/600], Loss: 0.0013
Epoch [43/64], Step [400/600], Loss: 0.0001
Epoch [43/64], Step [500/600], Loss: 0.0003
Epoch [43/64], Step [600/600], Loss: 0.0000
Epoch [44/64], Step [100/600], Loss: 0.0004
Epoch [44/64], Step [200/600], Loss: 0.0001
Epoch [44/64], Step [300/600], Loss: 0.0010
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: 0.0000
Epoch [45/64], Step [100/600], Loss: 0.0002
Epoch [45/64], Step [200/600], Loss: 0.0002
Epoch [45/64], Step [300/600], Loss: 0.0001
Epoch [45/64], Step [400/600], Loss: 0.0000
Epoch [45/64], Step [500/600], Loss: 0.0002
Epoch [45/64], Step [600/600], Loss: 0.0000
Epoch [46/64], Step [100/600], Loss: 0.0000
Epoch [46/64], Step [200/600], Loss: 0.0001
Epoch [46/64], Step [300/600], Loss: 0.0001
Epoch [46/64], Step [400/600], Loss: 0.0002
Epoch [46/64], Step [500/600], Loss: 0.0000
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: 0.0002
Epoch [47/64], Step [300/600], Loss: 0.0000
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: 0.0002
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: 0.0000
Epoch [48/64], Step [500/600], Loss: 0.0001
Epoch [48/64], Step [600/600], Loss: 0.0001
Epoch [49/64], Step [100/600], Loss: 0.0000
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: 0.0001
Epoch [50/64], Step [100/600], Loss: 0.0000
Epoch [50/64], Step [200/600], Loss: 0.0000
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: 0.0001
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: 0.0000
Epoch [51/64], Step [400/600], Loss: 0.0000
Epoch [51/64], Step [500/600], Loss: 0.0000
Epoch [51/64], Step [600/600], Loss: 0.0001
Epoch [52/64], Step [100/600], Loss: 0.0001
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: 0.0002
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: 0.0430
Epoch [53/64], Step [200/600], Loss: 0.0003
Epoch [53/64], Step [300/600], Loss: 0.0001
Epoch [53/64], Step [400/600], Loss: 0.0004
Epoch [53/64], Step [500/600], Loss: 0.0000
Epoch [53/64], Step [600/600], Loss: 0.0108
Epoch [54/64], Step [100/600], Loss: 0.0010
Epoch [54/64], Step [200/600], Loss: 0.0008
Epoch [54/64], Step [300/600], Loss: 0.0162
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: 0.0000
Epoch [55/64], Step [200/600], Loss: 0.0000
Epoch [55/64], Step [300/600], Loss: 0.0001
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: 0.0001
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: 0.0001
Epoch [57/64], Step [300/600], Loss: 0.0001
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: 0.0000
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: 0.0000
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: 0.0000
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: 0.0000
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: 0.0001
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: 0.0000
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: 0.0000
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: 0.0000
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 432.479 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins7358262455969227828.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