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 98024 queued and waiting for resources
srun: job 98024 has been allocated resources
Running benchmark on hydro05
Epoch [1/64], Step [100/600], Loss: 0.2485
Epoch [1/64], Step [200/600], Loss: 0.0781
Epoch [1/64], Step [300/600], Loss: 0.1472
Epoch [1/64], Step [400/600], Loss: 0.0999
Epoch [1/64], Step [500/600], Loss: 0.1557
Epoch [1/64], Step [600/600], Loss: 0.0947
Epoch [2/64], Step [100/600], Loss: 0.0578
Epoch [2/64], Step [200/600], Loss: 0.0455
Epoch [2/64], Step [300/600], Loss: 0.0737
Epoch [2/64], Step [400/600], Loss: 0.1022
Epoch [2/64], Step [500/600], Loss: 0.0333
Epoch [2/64], Step [600/600], Loss: 0.0749
Epoch [3/64], Step [100/600], Loss: 0.0475
Epoch [3/64], Step [200/600], Loss: 0.0379
Epoch [3/64], Step [300/600], Loss: 0.1132
Epoch [3/64], Step [400/600], Loss: 0.0546
Epoch [3/64], Step [500/600], Loss: 0.0287
Epoch [3/64], Step [600/600], Loss: 0.1074
Epoch [4/64], Step [100/600], Loss: 0.0275
Epoch [4/64], Step [200/600], Loss: 0.0076
Epoch [4/64], Step [300/600], Loss: 0.0554
Epoch [4/64], Step [400/600], Loss: 0.0310
Epoch [4/64], Step [500/600], Loss: 0.0152
Epoch [4/64], Step [600/600], Loss: 0.0247
Epoch [5/64], Step [100/600], Loss: 0.0114
Epoch [5/64], Step [200/600], Loss: 0.0792
Epoch [5/64], Step [300/600], Loss: 0.0036
Epoch [5/64], Step [400/600], Loss: 0.0160
Epoch [5/64], Step [500/600], Loss: 0.0108
Epoch [5/64], Step [600/600], Loss: 0.0313
Epoch [6/64], Step [100/600], Loss: 0.0350
Epoch [6/64], Step [200/600], Loss: 0.0022
Epoch [6/64], Step [300/600], Loss: 0.0314
Epoch [6/64], Step [400/600], Loss: 0.0444
Epoch [6/64], Step [500/600], Loss: 0.0128
Epoch [6/64], Step [600/600], Loss: 0.0117
Epoch [7/64], Step [100/600], Loss: 0.0136
Epoch [7/64], Step [200/600], Loss: 0.0125
Epoch [7/64], Step [300/600], Loss: 0.0141
Epoch [7/64], Step [400/600], Loss: 0.0283
Epoch [7/64], Step [500/600], Loss: 0.0087
Epoch [7/64], Step [600/600], Loss: 0.0126
Epoch [8/64], Step [100/600], Loss: 0.0262
Epoch [8/64], Step [200/600], Loss: 0.0097
Epoch [8/64], Step [300/600], Loss: 0.0109
Epoch [8/64], Step [400/600], Loss: 0.0304
Epoch [8/64], Step [500/600], Loss: 0.0247
Epoch [8/64], Step [600/600], Loss: 0.0089
Epoch [9/64], Step [100/600], Loss: 0.0033
Epoch [9/64], Step [200/600], Loss: 0.0076
Epoch [9/64], Step [300/600], Loss: 0.0008
Epoch [9/64], Step [400/600], Loss: 0.0058
Epoch [9/64], Step [500/600], Loss: 0.0078
Epoch [9/64], Step [600/600], Loss: 0.0081
Epoch [10/64], Step [100/600], Loss: 0.0061
Epoch [10/64], Step [200/600], Loss: 0.0008
Epoch [10/64], Step [300/600], Loss: 0.0048
Epoch [10/64], Step [400/600], Loss: 0.0085
Epoch [10/64], Step [500/600], Loss: 0.0143
Epoch [10/64], Step [600/600], Loss: 0.0165
Epoch [11/64], Step [100/600], Loss: 0.0081
Epoch [11/64], Step [200/600], Loss: 0.0222
Epoch [11/64], Step [300/600], Loss: 0.0047
Epoch [11/64], Step [400/600], Loss: 0.0131
Epoch [11/64], Step [500/600], Loss: 0.0015
Epoch [11/64], Step [600/600], Loss: 0.0230
Epoch [12/64], Step [100/600], Loss: 0.0179
Epoch [12/64], Step [200/600], Loss: 0.0065
Epoch [12/64], Step [300/600], Loss: 0.0019
Epoch [12/64], Step [400/600], Loss: 0.0349
Epoch [12/64], Step [500/600], Loss: 0.0093
Epoch [12/64], Step [600/600], Loss: 0.0016
Epoch [13/64], Step [100/600], Loss: 0.0012
Epoch [13/64], Step [200/600], Loss: 0.0044
Epoch [13/64], Step [300/600], Loss: 0.0009
Epoch [13/64], Step [400/600], Loss: 0.0022
Epoch [13/64], Step [500/600], Loss: 0.0008
Epoch [13/64], Step [600/600], Loss: 0.0010
Epoch [14/64], Step [100/600], Loss: 0.0110
Epoch [14/64], Step [200/600], Loss: 0.0030
Epoch [14/64], Step [300/600], Loss: 0.0030
Epoch [14/64], Step [400/600], Loss: 0.0045
Epoch [14/64], Step [500/600], Loss: 0.0030
Epoch [14/64], Step [600/600], Loss: 0.0064
Epoch [15/64], Step [100/600], Loss: 0.0109
Epoch [15/64], Step [200/600], Loss: 0.0182
Epoch [15/64], Step [300/600], Loss: 0.0033
Epoch [15/64], Step [400/600], Loss: 0.0036
Epoch [15/64], Step [500/600], Loss: 0.0004
Epoch [15/64], Step [600/600], Loss: 0.0093
Epoch [16/64], Step [100/600], Loss: 0.0011
Epoch [16/64], Step [200/600], Loss: 0.0004
Epoch [16/64], Step [300/600], Loss: 0.0011
Epoch [16/64], Step [400/600], Loss: 0.0008
Epoch [16/64], Step [500/600], Loss: 0.0094
Epoch [16/64], Step [600/600], Loss: 0.0116
Epoch [17/64], Step [100/600], Loss: 0.0022
Epoch [17/64], Step [200/600], Loss: 0.0012
Epoch [17/64], Step [300/600], Loss: 0.0007
Epoch [17/64], Step [400/600], Loss: 0.0181
Epoch [17/64], Step [500/600], Loss: 0.0091
Epoch [17/64], Step [600/600], Loss: 0.0002
Epoch [18/64], Step [100/600], Loss: 0.0076
Epoch [18/64], Step [200/600], Loss: 0.0029
Epoch [18/64], Step [300/600], Loss: 0.0022
Epoch [18/64], Step [400/600], Loss: 0.0017
Epoch [18/64], Step [500/600], Loss: 0.0010
Epoch [18/64], Step [600/600], Loss: 0.0003
Epoch [19/64], Step [100/600], Loss: 0.0023
Epoch [19/64], Step [200/600], Loss: 0.0022
Epoch [19/64], Step [300/600], Loss: 0.0006
Epoch [19/64], Step [400/600], Loss: 0.0015
Epoch [19/64], Step [500/600], Loss: 0.0007
Epoch [19/64], Step [600/600], Loss: 0.0034
Epoch [20/64], Step [100/600], Loss: 0.0004
Epoch [20/64], Step [200/600], Loss: 0.0008
Epoch [20/64], Step [300/600], Loss: 0.0027
Epoch [20/64], Step [400/600], Loss: 0.0036
Epoch [20/64], Step [500/600], Loss: 0.0048
Epoch [20/64], Step [600/600], Loss: 0.0033
Epoch [21/64], Step [100/600], Loss: 0.0015
Epoch [21/64], Step [200/600], Loss: 0.0003
Epoch [21/64], Step [300/600], Loss: 0.0003
Epoch [21/64], Step [400/600], Loss: 0.0013
Epoch [21/64], Step [500/600], Loss: 0.0007
Epoch [21/64], Step [600/600], Loss: 0.0313
Epoch [22/64], Step [100/600], Loss: 0.0006
Epoch [22/64], Step [200/600], Loss: 0.0001
Epoch [22/64], Step [300/600], Loss: 0.0011
Epoch [22/64], Step [400/600], Loss: 0.0038
Epoch [22/64], Step [500/600], Loss: 0.0002
Epoch [22/64], Step [600/600], Loss: 0.0003
Epoch [23/64], Step [100/600], Loss: 0.0065
Epoch [23/64], Step [200/600], Loss: 0.0062
Epoch [23/64], Step [300/600], Loss: 0.0005
Epoch [23/64], Step [400/600], Loss: 0.0008
Epoch [23/64], Step [500/600], Loss: 0.0053
Epoch [23/64], Step [600/600], Loss: 0.0001
Epoch [24/64], Step [100/600], Loss: 0.0026
Epoch [24/64], Step [200/600], Loss: 0.0007
Epoch [24/64], Step [300/600], Loss: 0.0177
Epoch [24/64], Step [400/600], Loss: 0.0002
Epoch [24/64], Step [500/600], Loss: 0.0034
Epoch [24/64], Step [600/600], Loss: 0.0006
Epoch [25/64], Step [100/600], Loss: 0.0000
Epoch [25/64], Step [200/600], Loss: 0.0041
Epoch [25/64], Step [300/600], Loss: 0.0054
Epoch [25/64], Step [400/600], Loss: 0.0030
Epoch [25/64], Step [500/600], Loss: 0.0071
Epoch [25/64], Step [600/600], Loss: 0.0009
Epoch [26/64], Step [100/600], Loss: 0.0001
Epoch [26/64], Step [200/600], Loss: 0.0001
Epoch [26/64], Step [300/600], Loss: 0.0151
Epoch [26/64], Step [400/600], Loss: 0.0005
Epoch [26/64], Step [500/600], Loss: 0.0002
Epoch [26/64], Step [600/600], Loss: 0.0009
Epoch [27/64], Step [100/600], Loss: 0.0003
Epoch [27/64], Step [200/600], Loss: 0.0003
Epoch [27/64], Step [300/600], Loss: 0.0004
Epoch [27/64], Step [400/600], Loss: 0.0001
Epoch [27/64], Step [500/600], Loss: 0.0347
Epoch [27/64], Step [600/600], Loss: 0.0002
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: 0.0005
Epoch [28/64], Step [400/600], Loss: 0.0203
Epoch [28/64], Step [500/600], Loss: 0.0001
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: 0.0002
Epoch [29/64], Step [400/600], Loss: 0.0002
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: 0.0000
Epoch [30/64], Step [200/600], Loss: 0.0000
Epoch [30/64], Step [300/600], Loss: 0.0003
Epoch [30/64], Step [400/600], Loss: 0.0001
Epoch [30/64], Step [500/600], Loss: 0.0002
Epoch [30/64], Step [600/600], Loss: 0.0000
Epoch [31/64], Step [100/600], Loss: 0.0001
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: 0.0001
Epoch [31/64], Step [500/600], Loss: 0.0001
Epoch [31/64], Step [600/600], Loss: 0.0001
Epoch [32/64], Step [100/600], Loss: 0.0001
Epoch [32/64], Step [200/600], Loss: 0.0002
Epoch [32/64], Step [300/600], Loss: 0.0006
Epoch [32/64], Step [400/600], Loss: 0.0000
Epoch [32/64], Step [500/600], Loss: 0.0001
Epoch [32/64], Step [600/600], Loss: 0.0074
Epoch [33/64], Step [100/600], Loss: 0.0187
Epoch [33/64], Step [200/600], Loss: 0.0125
Epoch [33/64], Step [300/600], Loss: 0.0066
Epoch [33/64], Step [400/600], Loss: 0.0007
Epoch [33/64], Step [500/600], Loss: 0.0005
Epoch [33/64], Step [600/600], Loss: 0.0015
Epoch [34/64], Step [100/600], Loss: 0.0010
Epoch [34/64], Step [200/600], Loss: 0.0004
Epoch [34/64], Step [300/600], Loss: 0.0000
Epoch [34/64], Step [400/600], Loss: 0.0000
Epoch [34/64], Step [500/600], Loss: 0.0003
Epoch [34/64], Step [600/600], Loss: 0.0056
Epoch [35/64], Step [100/600], Loss: 0.0000
Epoch [35/64], Step [200/600], Loss: 0.0006
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: 0.0001
Epoch [35/64], Step [600/600], Loss: 0.0001
Epoch [36/64], Step [100/600], Loss: 0.0003
Epoch [36/64], Step [200/600], Loss: 0.0000
Epoch [36/64], Step [300/600], Loss: 0.0003
Epoch [36/64], Step [400/600], Loss: 0.0001
Epoch [36/64], Step [500/600], Loss: 0.0002
Epoch [36/64], Step [600/600], Loss: 0.0001
Epoch [37/64], Step [100/600], Loss: 0.0000
Epoch [37/64], Step [200/600], Loss: 0.0000
Epoch [37/64], Step [300/600], Loss: 0.0002
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: 0.0000
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: 0.0002
Epoch [38/64], Step [400/600], Loss: 0.0000
Epoch [38/64], Step [500/600], Loss: 0.0001
Epoch [38/64], Step [600/600], Loss: 0.0002
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: 0.0001
Epoch [40/64], Step [100/600], Loss: 0.0000
Epoch [40/64], Step [200/600], Loss: 0.0001
Epoch [40/64], Step [300/600], Loss: 0.0000
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: 0.0000
Epoch [41/64], Step [100/600], Loss: 0.0001
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: 0.0001
Epoch [41/64], Step [600/600], Loss: 0.0000
Epoch [42/64], Step [100/600], Loss: 0.0000
Epoch [42/64], Step [200/600], Loss: 0.0000
Epoch [42/64], Step [300/600], Loss: 0.0845
Epoch [42/64], Step [400/600], Loss: 0.0360
Epoch [42/64], Step [500/600], Loss: 0.0108
Epoch [42/64], Step [600/600], Loss: 0.0078
Epoch [43/64], Step [100/600], Loss: 0.0003
Epoch [43/64], Step [200/600], Loss: 0.0134
Epoch [43/64], Step [300/600], Loss: 0.0056
Epoch [43/64], Step [400/600], Loss: 0.0007
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: 0.0000
Epoch [44/64], Step [200/600], Loss: 0.0002
Epoch [44/64], Step [300/600], Loss: 0.0018
Epoch [44/64], Step [400/600], Loss: 0.0002
Epoch [44/64], Step [500/600], Loss: 0.0001
Epoch [44/64], Step [600/600], Loss: 0.0001
Epoch [45/64], Step [100/600], Loss: 0.0001
Epoch [45/64], Step [200/600], Loss: 0.0003
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: 0.0000
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: 0.0001
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: 0.0002
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: 0.0001
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: 0.0001
Epoch [48/64], Step [400/600], Loss: 0.0001
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: 0.0001
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: 0.0001
Epoch [50/64], Step [200/600], Loss: 0.0000
Epoch [50/64], Step [300/600], Loss: 0.0000
Epoch [50/64], Step [400/600], Loss: 0.0002
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: 0.0003
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: 0.0001
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: 0.0001
Epoch [52/64], Step [300/600], Loss: 0.0001
Epoch [52/64], Step [400/600], Loss: 0.0003
Epoch [52/64], Step [500/600], Loss: 0.0000
Epoch [52/64], Step [600/600], Loss: 0.0000
Epoch [53/64], Step [100/600], Loss: 0.0001
Epoch [53/64], Step [200/600], Loss: 0.0001
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: 0.0000
Epoch [53/64], Step [600/600], Loss: 0.0817
Epoch [54/64], Step [100/600], Loss: 0.0003
Epoch [54/64], Step [200/600], Loss: 0.0017
Epoch [54/64], Step [300/600], Loss: 0.0034
Epoch [54/64], Step [400/600], Loss: 0.0004
Epoch [54/64], Step [500/600], Loss: 0.0010
Epoch [54/64], Step [600/600], Loss: 0.0006
Epoch [55/64], Step [100/600], Loss: 0.0026
Epoch [55/64], Step [200/600], Loss: 0.0001
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: 0.0007
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: 0.0003
Epoch [56/64], Step [400/600], Loss: 0.0000
Epoch [56/64], Step [500/600], Loss: 0.0001
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: 0.0000
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: 0.0001
Epoch [58/64], Step [500/600], Loss: 0.0000
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: 0.0001
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: 0.0001
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: 0.0001
Epoch [60/64], Step [400/600], Loss: 0.0001
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: 0.0001
Epoch [61/64], Step [400/600], Loss: 0.0000
Epoch [61/64], Step [500/600], Loss: 0.0000
Epoch [61/64], Step [600/600], Loss: 0.0002
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: 0.0001
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: 0.0001
Epoch [64/64], Step [600/600], Loss: 0.0000
Pytorch test completed in 370.744 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins9864074567788099006.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