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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 85039 queued and waiting for resources
srun: job 85039 has been allocated resources
Running benchmark on hydro03
Epoch [1/64], Step [100/600], Loss: 0.2391
Epoch [1/64], Step [200/600], Loss: 0.0831
Epoch [1/64], Step [300/600], Loss: 0.1322
Epoch [1/64], Step [400/600], Loss: 0.1019
Epoch [1/64], Step [500/600], Loss: 0.0680
Epoch [1/64], Step [600/600], Loss: 0.0423
Epoch [2/64], Step [100/600], Loss: 0.0345
Epoch [2/64], Step [200/600], Loss: 0.0424
Epoch [2/64], Step [300/600], Loss: 0.0323
Epoch [2/64], Step [400/600], Loss: 0.0289
Epoch [2/64], Step [500/600], Loss: 0.0233
Epoch [2/64], Step [600/600], Loss: 0.0184
Epoch [3/64], Step [100/600], Loss: 0.0252
Epoch [3/64], Step [200/600], Loss: 0.0288
Epoch [3/64], Step [300/600], Loss: 0.1371
Epoch [3/64], Step [400/600], Loss: 0.0297
Epoch [3/64], Step [500/600], Loss: 0.0125
Epoch [3/64], Step [600/600], Loss: 0.0279
Epoch [4/64], Step [100/600], Loss: 0.0196
Epoch [4/64], Step [200/600], Loss: 0.0924
Epoch [4/64], Step [300/600], Loss: 0.0572
Epoch [4/64], Step [400/600], Loss: 0.0059
Epoch [4/64], Step [500/600], Loss: 0.0447
Epoch [4/64], Step [600/600], Loss: 0.0654
Epoch [5/64], Step [100/600], Loss: 0.0514
Epoch [5/64], Step [200/600], Loss: 0.0079
Epoch [5/64], Step [300/600], Loss: 0.0551
Epoch [5/64], Step [400/600], Loss: 0.0372
Epoch [5/64], Step [500/600], Loss: 0.0663
Epoch [5/64], Step [600/600], Loss: 0.0155
Epoch [6/64], Step [100/600], Loss: 0.0565
Epoch [6/64], Step [200/600], Loss: 0.0294
Epoch [6/64], Step [300/600], Loss: 0.0135
Epoch [6/64], Step [400/600], Loss: 0.0288
Epoch [6/64], Step [500/600], Loss: 0.0096
Epoch [6/64], Step [600/600], Loss: 0.0165
Epoch [7/64], Step [100/600], Loss: 0.0053
Epoch [7/64], Step [200/600], Loss: 0.0046
Epoch [7/64], Step [300/600], Loss: 0.0213
Epoch [7/64], Step [400/600], Loss: 0.0115
Epoch [7/64], Step [500/600], Loss: 0.0114
Epoch [7/64], Step [600/600], Loss: 0.0180
Epoch [8/64], Step [100/600], Loss: 0.0031
Epoch [8/64], Step [200/600], Loss: 0.0116
Epoch [8/64], Step [300/600], Loss: 0.0047
Epoch [8/64], Step [400/600], Loss: 0.0199
Epoch [8/64], Step [500/600], Loss: 0.0025
Epoch [8/64], Step [600/600], Loss: 0.0042
Epoch [9/64], Step [100/600], Loss: 0.0440
Epoch [9/64], Step [200/600], Loss: 0.0071
Epoch [9/64], Step [300/600], Loss: 0.0049
Epoch [9/64], Step [400/600], Loss: 0.0018
Epoch [9/64], Step [500/600], Loss: 0.0012
Epoch [9/64], Step [600/600], Loss: 0.0028
Epoch [10/64], Step [100/600], Loss: 0.0070
Epoch [10/64], Step [200/600], Loss: 0.0328
Epoch [10/64], Step [300/600], Loss: 0.0011
Epoch [10/64], Step [400/600], Loss: 0.0031
Epoch [10/64], Step [500/600], Loss: 0.0024
Epoch [10/64], Step [600/600], Loss: 0.0131
Epoch [11/64], Step [100/600], Loss: 0.0045
Epoch [11/64], Step [200/600], Loss: 0.0057
Epoch [11/64], Step [300/600], Loss: 0.0021
Epoch [11/64], Step [400/600], Loss: 0.0439
Epoch [11/64], Step [500/600], Loss: 0.0145
Epoch [11/64], Step [600/600], Loss: 0.0136
Epoch [12/64], Step [100/600], Loss: 0.0028
Epoch [12/64], Step [200/600], Loss: 0.0057
Epoch [12/64], Step [300/600], Loss: 0.0147
Epoch [12/64], Step [400/600], Loss: 0.0198
Epoch [12/64], Step [500/600], Loss: 0.0206
Epoch [12/64], Step [600/600], Loss: 0.0583
Epoch [13/64], Step [100/600], Loss: 0.0002
Epoch [13/64], Step [200/600], Loss: 0.0022
Epoch [13/64], Step [300/600], Loss: 0.0116
Epoch [13/64], Step [400/600], Loss: 0.0091
Epoch [13/64], Step [500/600], Loss: 0.0007
Epoch [13/64], Step [600/600], Loss: 0.0439
Epoch [14/64], Step [100/600], Loss: 0.0009
Epoch [14/64], Step [200/600], Loss: 0.0028
Epoch [14/64], Step [300/600], Loss: 0.0138
Epoch [14/64], Step [400/600], Loss: 0.0098
Epoch [14/64], Step [500/600], Loss: 0.0057
Epoch [14/64], Step [600/600], Loss: 0.0526
Epoch [15/64], Step [100/600], Loss: 0.0007
Epoch [15/64], Step [200/600], Loss: 0.0006
Epoch [15/64], Step [300/600], Loss: 0.0039
Epoch [15/64], Step [400/600], Loss: 0.0038
Epoch [15/64], Step [500/600], Loss: 0.0026
Epoch [15/64], Step [600/600], Loss: 0.0014
Epoch [16/64], Step [100/600], Loss: 0.0021
Epoch [16/64], Step [200/600], Loss: 0.0002
Epoch [16/64], Step [300/600], Loss: 0.0045
Epoch [16/64], Step [400/600], Loss: 0.0027
Epoch [16/64], Step [500/600], Loss: 0.0065
Epoch [16/64], Step [600/600], Loss: 0.0028
Epoch [17/64], Step [100/600], Loss: 0.0247
Epoch [17/64], Step [200/600], Loss: 0.0014
Epoch [17/64], Step [300/600], Loss: 0.0018
Epoch [17/64], Step [400/600], Loss: 0.0133
Epoch [17/64], Step [500/600], Loss: 0.0003
Epoch [17/64], Step [600/600], Loss: 0.0228
Epoch [18/64], Step [100/600], Loss: 0.0009
Epoch [18/64], Step [200/600], Loss: 0.0015
Epoch [18/64], Step [300/600], Loss: 0.0003
Epoch [18/64], Step [400/600], Loss: 0.0031
Epoch [18/64], Step [500/600], Loss: 0.0027
Epoch [18/64], Step [600/600], Loss: 0.0060
Epoch [19/64], Step [100/600], Loss: 0.0013
Epoch [19/64], Step [200/600], Loss: 0.0008
Epoch [19/64], Step [300/600], Loss: 0.0011
Epoch [19/64], Step [400/600], Loss: 0.0002
Epoch [19/64], Step [500/600], Loss: 0.0018
Epoch [19/64], Step [600/600], Loss: 0.0030
Epoch [20/64], Step [100/600], Loss: 0.0022
Epoch [20/64], Step [200/600], Loss: 0.0036
Epoch [20/64], Step [300/600], Loss: 0.0020
Epoch [20/64], Step [400/600], Loss: 0.0002
Epoch [20/64], Step [500/600], Loss: 0.0144
Epoch [20/64], Step [600/600], Loss: 0.0106
Epoch [21/64], Step [100/600], Loss: 0.0024
Epoch [21/64], Step [200/600], Loss: 0.0012
Epoch [21/64], Step [300/600], Loss: 0.0009
Epoch [21/64], Step [400/600], Loss: 0.0010
Epoch [21/64], Step [500/600], Loss: 0.0057
Epoch [21/64], Step [600/600], Loss: 0.0040
Epoch [22/64], Step [100/600], Loss: 0.0009
Epoch [22/64], Step [200/600], Loss: 0.0004
Epoch [22/64], Step [300/600], Loss: 0.0004
Epoch [22/64], Step [400/600], Loss: 0.0007
Epoch [22/64], Step [500/600], Loss: 0.0007
Epoch [22/64], Step [600/600], Loss: 0.0001
Epoch [23/64], Step [100/600], Loss: 0.0019
Epoch [23/64], Step [200/600], Loss: 0.0006
Epoch [23/64], Step [300/600], Loss: 0.0065
Epoch [23/64], Step [400/600], Loss: 0.0002
Epoch [23/64], Step [500/600], Loss: 0.0022
Epoch [23/64], Step [600/600], Loss: 0.0008
Epoch [24/64], Step [100/600], Loss: 0.0006
Epoch [24/64], Step [200/600], Loss: 0.0007
Epoch [24/64], Step [300/600], Loss: 0.0085
Epoch [24/64], Step [400/600], Loss: 0.0004
Epoch [24/64], Step [500/600], Loss: 0.0007
Epoch [24/64], Step [600/600], Loss: 0.0001
Epoch [25/64], Step [100/600], Loss: 0.0044
Epoch [25/64], Step [200/600], Loss: 0.0001
Epoch [25/64], Step [300/600], Loss: 0.0000
Epoch [25/64], Step [400/600], Loss: 0.0003
Epoch [25/64], Step [500/600], Loss: 0.0022
Epoch [25/64], Step [600/600], Loss: 0.0014
Epoch [26/64], Step [100/600], Loss: 0.0002
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: 0.0001
Epoch [26/64], Step [600/600], Loss: 0.0007
Epoch [27/64], Step [100/600], Loss: 0.0002
Epoch [27/64], Step [200/600], Loss: 0.0028
Epoch [27/64], Step [300/600], Loss: 0.0001
Epoch [27/64], Step [400/600], Loss: 0.0003
Epoch [27/64], Step [500/600], Loss: 0.0012
Epoch [27/64], Step [600/600], Loss: 0.0156
Epoch [28/64], Step [100/600], Loss: 0.0004
Epoch [28/64], Step [200/600], Loss: 0.0015
Epoch [28/64], Step [300/600], Loss: 0.0020
Epoch [28/64], Step [400/600], Loss: 0.0001
Epoch [28/64], Step [500/600], Loss: 0.0005
Epoch [28/64], Step [600/600], Loss: 0.0031
Epoch [29/64], Step [100/600], Loss: 0.0002
Epoch [29/64], Step [200/600], Loss: 0.0015
Epoch [29/64], Step [300/600], Loss: 0.0001
Epoch [29/64], Step [400/600], Loss: 0.0000
Epoch [29/64], Step [500/600], Loss: 0.0002
Epoch [29/64], Step [600/600], Loss: 0.0003
Epoch [30/64], Step [100/600], Loss: 0.0004
Epoch [30/64], Step [200/600], Loss: 0.0005
Epoch [30/64], Step [300/600], Loss: 0.0001
Epoch [30/64], Step [400/600], Loss: 0.0002
Epoch [30/64], Step [500/600], Loss: 0.0001
Epoch [30/64], Step [600/600], Loss: 0.0004
Epoch [31/64], Step [100/600], Loss: 0.0001
Epoch [31/64], Step [200/600], Loss: 0.0002
Epoch [31/64], Step [300/600], Loss: 0.0003
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: 0.0003
Epoch [32/64], Step [100/600], Loss: 0.0003
Epoch [32/64], Step [200/600], Loss: 0.0001
Epoch [32/64], Step [300/600], Loss: 0.0000
Epoch [32/64], Step [400/600], Loss: 0.0003
Epoch [32/64], Step [500/600], Loss: 0.0006
Epoch [32/64], Step [600/600], Loss: 0.0003
Epoch [33/64], Step [100/600], Loss: 0.0001
Epoch [33/64], Step [200/600], Loss: 0.0014
Epoch [33/64], Step [300/600], Loss: 0.0001
Epoch [33/64], Step [400/600], Loss: 0.0002
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: 0.0001
Epoch [34/64], Step [200/600], Loss: 0.0003
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: 0.0000
Epoch [34/64], Step [600/600], Loss: 0.0000
Epoch [35/64], Step [100/600], Loss: 0.0001
Epoch [35/64], Step [200/600], Loss: 0.0001
Epoch [35/64], Step [300/600], Loss: 0.0004
Epoch [35/64], Step [400/600], Loss: 0.0000
Epoch [35/64], Step [500/600], Loss: 0.0003
Epoch [35/64], Step [600/600], Loss: 0.0001
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: 0.0000
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: 0.0002
Epoch [37/64], Step [100/600], Loss: 0.0769
Epoch [37/64], Step [200/600], Loss: 0.0030
Epoch [37/64], Step [300/600], Loss: 0.0057
Epoch [37/64], Step [400/600], Loss: 0.0001
Epoch [37/64], Step [500/600], Loss: 0.0029
Epoch [37/64], Step [600/600], Loss: 0.0003
Epoch [38/64], Step [100/600], Loss: 0.0000
Epoch [38/64], Step [200/600], Loss: 0.0047
Epoch [38/64], Step [300/600], Loss: 0.0004
Epoch [38/64], Step [400/600], Loss: 0.0003
Epoch [38/64], Step [500/600], Loss: 0.0003
Epoch [38/64], Step [600/600], Loss: 0.0001
Epoch [39/64], Step [100/600], Loss: 0.0002
Epoch [39/64], Step [200/600], Loss: 0.0002
Epoch [39/64], Step [300/600], Loss: 0.0001
Epoch [39/64], Step [400/600], Loss: 0.0000
Epoch [39/64], Step [500/600], Loss: 0.0010
Epoch [39/64], Step [600/600], Loss: 0.0001
Epoch [40/64], Step [100/600], Loss: 0.0002
Epoch [40/64], Step [200/600], Loss: 0.0002
Epoch [40/64], Step [300/600], Loss: 0.0000
Epoch [40/64], Step [400/600], Loss: 0.0001
Epoch [40/64], Step [500/600], Loss: 0.0001
Epoch [40/64], Step [600/600], Loss: 0.0006
Epoch [41/64], Step [100/600], Loss: 0.0001
Epoch [41/64], Step [200/600], Loss: 0.0001
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: 0.0003
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: 0.0001
Epoch [42/64], Step [400/600], Loss: 0.0002
Epoch [42/64], Step [500/600], Loss: 0.0001
Epoch [42/64], Step [600/600], Loss: 0.0000
Epoch [43/64], Step [100/600], Loss: 0.0001
Epoch [43/64], Step [200/600], Loss: 0.0002
Epoch [43/64], Step [300/600], Loss: 0.0001
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: 0.0000
Epoch [44/64], Step [200/600], Loss: 0.0001
Epoch [44/64], Step [300/600], Loss: 0.0000
Epoch [44/64], Step [400/600], Loss: 0.0000
Epoch [44/64], Step [500/600], Loss: 0.0000
Epoch [44/64], Step [600/600], Loss: 0.0003
Epoch [45/64], Step [100/600], Loss: 0.0001
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: 0.0002
Epoch [45/64], Step [500/600], Loss: 0.0003
Epoch [45/64], Step [600/600], Loss: 0.0001
Epoch [46/64], Step [100/600], Loss: 0.1226
Epoch [46/64], Step [200/600], Loss: 0.0017
Epoch [46/64], Step [300/600], Loss: 0.0073
Epoch [46/64], Step [400/600], Loss: 0.0028
Epoch [46/64], Step [500/600], Loss: 0.0005
Epoch [46/64], Step [600/600], Loss: 0.0012
Epoch [47/64], Step [100/600], Loss: 0.0001
Epoch [47/64], Step [200/600], Loss: 0.0000
Epoch [47/64], Step [300/600], Loss: 0.0000
Epoch [47/64], Step [400/600], Loss: 0.0002
Epoch [47/64], Step [500/600], Loss: 0.0000
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: 0.0007
Epoch [48/64], Step [400/600], Loss: 0.0000
Epoch [48/64], Step [500/600], Loss: 0.0014
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: 0.0001
Epoch [49/64], Step [400/600], Loss: 0.0000
Epoch [49/64], Step [500/600], Loss: 0.0001
Epoch [49/64], Step [600/600], Loss: 0.0012
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: 0.0002
Epoch [50/64], Step [400/600], Loss: 0.0002
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: 0.0094
Epoch [51/64], Step [200/600], Loss: 0.0001
Epoch [51/64], Step [300/600], Loss: 0.0036
Epoch [51/64], Step [400/600], Loss: 0.0022
Epoch [51/64], Step [500/600], Loss: 0.0007
Epoch [51/64], Step [600/600], Loss: 0.0068
Epoch [52/64], Step [100/600], Loss: 0.0034
Epoch [52/64], Step [200/600], Loss: 0.0003
Epoch [52/64], Step [300/600], Loss: 0.0001
Epoch [52/64], Step [400/600], Loss: 0.0001
Epoch [52/64], Step [500/600], Loss: 0.0028
Epoch [52/64], Step [600/600], Loss: 0.0002
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: 0.0002
Epoch [53/64], Step [400/600], Loss: 0.0000
Epoch [53/64], Step [500/600], Loss: 0.0001
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: 0.0001
Epoch [54/64], Step [400/600], Loss: 0.0002
Epoch [54/64], Step [500/600], Loss: 0.0001
Epoch [54/64], Step [600/600], Loss: 0.0004
Epoch [55/64], Step [100/600], Loss: 0.0007
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: 0.0000
Epoch [55/64], Step [500/600], Loss: 0.0001
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: 0.0000
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: 0.0001
Epoch [57/64], Step [400/600], Loss: 0.0000
Epoch [57/64], Step [500/600], Loss: 0.0003
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: 0.0001
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: 0.0001
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: 0.0028
Epoch [61/64], Step [200/600], Loss: 0.0513
Epoch [61/64], Step [300/600], Loss: 0.0001
Epoch [61/64], Step [400/600], Loss: 0.0001
Epoch [61/64], Step [500/600], Loss: 0.0002
Epoch [61/64], Step [600/600], Loss: 0.0000
Epoch [62/64], Step [100/600], Loss: 0.0008
Epoch [62/64], Step [200/600], Loss: 0.0000
Epoch [62/64], Step [300/600], Loss: 0.0140
Epoch [62/64], Step [400/600], Loss: 0.0000
Epoch [62/64], Step [500/600], Loss: 0.0008
Epoch [62/64], Step [600/600], Loss: 0.0005
Epoch [63/64], Step [100/600], Loss: 0.0002
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: 0.0001
Epoch [63/64], Step [500/600], Loss: 0.0005
Epoch [63/64], Step [600/600], Loss: 0.0002
Epoch [64/64], Step [100/600], Loss: 0.0007
Epoch [64/64], Step [200/600], Loss: 0.0000
Epoch [64/64], Step [300/600], Loss: 0.0002
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 426.383 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins6308828073474352221.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