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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 84820 queued and waiting for resources
srun: job 84820 has been allocated resources
Running benchmark on hydro05
Epoch [1/64], Step [100/600], Loss: 0.2515
Epoch [1/64], Step [200/600], Loss: 0.1069
Epoch [1/64], Step [300/600], Loss: 0.0861
Epoch [1/64], Step [400/600], Loss: 0.0495
Epoch [1/64], Step [500/600], Loss: 0.1021
Epoch [1/64], Step [600/600], Loss: 0.0877
Epoch [2/64], Step [100/600], Loss: 0.0634
Epoch [2/64], Step [200/600], Loss: 0.0414
Epoch [2/64], Step [300/600], Loss: 0.0610
Epoch [2/64], Step [400/600], Loss: 0.0185
Epoch [2/64], Step [500/600], Loss: 0.0179
Epoch [2/64], Step [600/600], Loss: 0.0394
Epoch [3/64], Step [100/600], Loss: 0.0174
Epoch [3/64], Step [200/600], Loss: 0.0166
Epoch [3/64], Step [300/600], Loss: 0.0802
Epoch [3/64], Step [400/600], Loss: 0.0272
Epoch [3/64], Step [500/600], Loss: 0.0139
Epoch [3/64], Step [600/600], Loss: 0.0852
Epoch [4/64], Step [100/600], Loss: 0.0302
Epoch [4/64], Step [200/600], Loss: 0.0320
Epoch [4/64], Step [300/600], Loss: 0.0082
Epoch [4/64], Step [400/600], Loss: 0.0074
Epoch [4/64], Step [500/600], Loss: 0.0306
Epoch [4/64], Step [600/600], Loss: 0.0386
Epoch [5/64], Step [100/600], Loss: 0.0388
Epoch [5/64], Step [200/600], Loss: 0.0068
Epoch [5/64], Step [300/600], Loss: 0.0163
Epoch [5/64], Step [400/600], Loss: 0.0245
Epoch [5/64], Step [500/600], Loss: 0.0082
Epoch [5/64], Step [600/600], Loss: 0.0632
Epoch [6/64], Step [100/600], Loss: 0.0573
Epoch [6/64], Step [200/600], Loss: 0.0082
Epoch [6/64], Step [300/600], Loss: 0.0151
Epoch [6/64], Step [400/600], Loss: 0.0035
Epoch [6/64], Step [500/600], Loss: 0.0089
Epoch [6/64], Step [600/600], Loss: 0.0083
Epoch [7/64], Step [100/600], Loss: 0.0697
Epoch [7/64], Step [200/600], Loss: 0.0212
Epoch [7/64], Step [300/600], Loss: 0.0204
Epoch [7/64], Step [400/600], Loss: 0.0084
Epoch [7/64], Step [500/600], Loss: 0.0148
Epoch [7/64], Step [600/600], Loss: 0.0206
Epoch [8/64], Step [100/600], Loss: 0.0163
Epoch [8/64], Step [200/600], Loss: 0.0024
Epoch [8/64], Step [300/600], Loss: 0.0494
Epoch [8/64], Step [400/600], Loss: 0.0278
Epoch [8/64], Step [500/600], Loss: 0.0081
Epoch [8/64], Step [600/600], Loss: 0.0054
Epoch [9/64], Step [100/600], Loss: 0.0103
Epoch [9/64], Step [200/600], Loss: 0.0083
Epoch [9/64], Step [300/600], Loss: 0.0056
Epoch [9/64], Step [400/600], Loss: 0.0035
Epoch [9/64], Step [500/600], Loss: 0.0403
Epoch [9/64], Step [600/600], Loss: 0.0324
Epoch [10/64], Step [100/600], Loss: 0.0038
Epoch [10/64], Step [200/600], Loss: 0.0138
Epoch [10/64], Step [300/600], Loss: 0.0044
Epoch [10/64], Step [400/600], Loss: 0.0021
Epoch [10/64], Step [500/600], Loss: 0.0065
Epoch [10/64], Step [600/600], Loss: 0.0031
Epoch [11/64], Step [100/600], Loss: 0.0046
Epoch [11/64], Step [200/600], Loss: 0.0061
Epoch [11/64], Step [300/600], Loss: 0.0039
Epoch [11/64], Step [400/600], Loss: 0.0035
Epoch [11/64], Step [500/600], Loss: 0.0008
Epoch [11/64], Step [600/600], Loss: 0.0066
Epoch [12/64], Step [100/600], Loss: 0.0054
Epoch [12/64], Step [200/600], Loss: 0.0313
Epoch [12/64], Step [300/600], Loss: 0.0437
Epoch [12/64], Step [400/600], Loss: 0.0399
Epoch [12/64], Step [500/600], Loss: 0.0016
Epoch [12/64], Step [600/600], Loss: 0.0017
Epoch [13/64], Step [100/600], Loss: 0.0044
Epoch [13/64], Step [200/600], Loss: 0.0080
Epoch [13/64], Step [300/600], Loss: 0.0137
Epoch [13/64], Step [400/600], Loss: 0.0019
Epoch [13/64], Step [500/600], Loss: 0.0270
Epoch [13/64], Step [600/600], Loss: 0.0012
Epoch [14/64], Step [100/600], Loss: 0.0042
Epoch [14/64], Step [200/600], Loss: 0.0007
Epoch [14/64], Step [300/600], Loss: 0.0028
Epoch [14/64], Step [400/600], Loss: 0.0025
Epoch [14/64], Step [500/600], Loss: 0.0135
Epoch [14/64], Step [600/600], Loss: 0.0080
Epoch [15/64], Step [100/600], Loss: 0.0050
Epoch [15/64], Step [200/600], Loss: 0.0032
Epoch [15/64], Step [300/600], Loss: 0.0111
Epoch [15/64], Step [400/600], Loss: 0.0082
Epoch [15/64], Step [500/600], Loss: 0.0030
Epoch [15/64], Step [600/600], Loss: 0.0003
Epoch [16/64], Step [100/600], Loss: 0.0023
Epoch [16/64], Step [200/600], Loss: 0.0108
Epoch [16/64], Step [300/600], Loss: 0.0023
Epoch [16/64], Step [400/600], Loss: 0.0050
Epoch [16/64], Step [500/600], Loss: 0.0044
Epoch [16/64], Step [600/600], Loss: 0.0022
Epoch [17/64], Step [100/600], Loss: 0.0014
Epoch [17/64], Step [200/600], Loss: 0.0085
Epoch [17/64], Step [300/600], Loss: 0.0004
Epoch [17/64], Step [400/600], Loss: 0.0012
Epoch [17/64], Step [500/600], Loss: 0.0009
Epoch [17/64], Step [600/600], Loss: 0.0418
Epoch [18/64], Step [100/600], Loss: 0.0090
Epoch [18/64], Step [200/600], Loss: 0.0014
Epoch [18/64], Step [300/600], Loss: 0.0100
Epoch [18/64], Step [400/600], Loss: 0.0006
Epoch [18/64], Step [500/600], Loss: 0.0017
Epoch [18/64], Step [600/600], Loss: 0.0008
Epoch [19/64], Step [100/600], Loss: 0.0004
Epoch [19/64], Step [200/600], Loss: 0.0128
Epoch [19/64], Step [300/600], Loss: 0.0008
Epoch [19/64], Step [400/600], Loss: 0.0011
Epoch [19/64], Step [500/600], Loss: 0.0003
Epoch [19/64], Step [600/600], Loss: 0.0031
Epoch [20/64], Step [100/600], Loss: 0.0001
Epoch [20/64], Step [200/600], Loss: 0.0326
Epoch [20/64], Step [300/600], Loss: 0.0001
Epoch [20/64], Step [400/600], Loss: 0.0524
Epoch [20/64], Step [500/600], Loss: 0.0004
Epoch [20/64], Step [600/600], Loss: 0.0095
Epoch [21/64], Step [100/600], Loss: 0.0004
Epoch [21/64], Step [200/600], Loss: 0.0023
Epoch [21/64], Step [300/600], Loss: 0.0005
Epoch [21/64], Step [400/600], Loss: 0.0026
Epoch [21/64], Step [500/600], Loss: 0.0001
Epoch [21/64], Step [600/600], Loss: 0.0022
Epoch [22/64], Step [100/600], Loss: 0.0001
Epoch [22/64], Step [200/600], Loss: 0.0004
Epoch [22/64], Step [300/600], Loss: 0.0044
Epoch [22/64], Step [400/600], Loss: 0.0013
Epoch [22/64], Step [500/600], Loss: 0.0182
Epoch [22/64], Step [600/600], Loss: 0.0008
Epoch [23/64], Step [100/600], Loss: 0.0003
Epoch [23/64], Step [200/600], Loss: 0.0002
Epoch [23/64], Step [300/600], Loss: 0.0001
Epoch [23/64], Step [400/600], Loss: 0.0090
Epoch [23/64], Step [500/600], Loss: 0.0010
Epoch [23/64], Step [600/600], Loss: 0.0019
Epoch [24/64], Step [100/600], Loss: 0.0042
Epoch [24/64], Step [200/600], Loss: 0.0008
Epoch [24/64], Step [300/600], Loss: 0.0039
Epoch [24/64], Step [400/600], Loss: 0.0082
Epoch [24/64], Step [500/600], Loss: 0.0057
Epoch [24/64], Step [600/600], Loss: 0.0003
Epoch [25/64], Step [100/600], Loss: 0.0022
Epoch [25/64], Step [200/600], Loss: 0.0009
Epoch [25/64], Step [300/600], Loss: 0.0009
Epoch [25/64], Step [400/600], Loss: 0.0004
Epoch [25/64], Step [500/600], Loss: 0.0085
Epoch [25/64], Step [600/600], Loss: 0.0004
Epoch [26/64], Step [100/600], Loss: 0.0010
Epoch [26/64], Step [200/600], Loss: 0.0003
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: 0.0086
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: 0.0009
Epoch [27/64], Step [300/600], Loss: 0.0004
Epoch [27/64], Step [400/600], Loss: 0.0200
Epoch [27/64], Step [500/600], Loss: 0.0015
Epoch [27/64], Step [600/600], Loss: 0.0003
Epoch [28/64], Step [100/600], Loss: 0.0008
Epoch [28/64], Step [200/600], Loss: 0.0004
Epoch [28/64], Step [300/600], Loss: 0.0079
Epoch [28/64], Step [400/600], Loss: 0.0061
Epoch [28/64], Step [500/600], Loss: 0.0003
Epoch [28/64], Step [600/600], Loss: 0.0008
Epoch [29/64], Step [100/600], Loss: 0.0009
Epoch [29/64], Step [200/600], Loss: 0.0004
Epoch [29/64], Step [300/600], Loss: 0.0001
Epoch [29/64], Step [400/600], Loss: 0.0003
Epoch [29/64], Step [500/600], Loss: 0.0006
Epoch [29/64], Step [600/600], Loss: 0.0086
Epoch [30/64], Step [100/600], Loss: 0.0070
Epoch [30/64], Step [200/600], Loss: 0.0083
Epoch [30/64], Step [300/600], Loss: 0.0002
Epoch [30/64], Step [400/600], Loss: 0.0022
Epoch [30/64], Step [500/600], Loss: 0.0015
Epoch [30/64], Step [600/600], Loss: 0.0001
Epoch [31/64], Step [100/600], Loss: 0.0002
Epoch [31/64], Step [200/600], Loss: 0.0001
Epoch [31/64], Step [300/600], Loss: 0.0001
Epoch [31/64], Step [400/600], Loss: 0.0014
Epoch [31/64], Step [500/600], Loss: 0.0005
Epoch [31/64], Step [600/600], Loss: 0.0001
Epoch [32/64], Step [100/600], Loss: 0.0002
Epoch [32/64], Step [200/600], Loss: 0.0002
Epoch [32/64], Step [300/600], Loss: 0.0007
Epoch [32/64], Step [400/600], Loss: 0.0001
Epoch [32/64], Step [500/600], Loss: 0.0003
Epoch [32/64], Step [600/600], Loss: 0.0008
Epoch [33/64], Step [100/600], Loss: 0.0001
Epoch [33/64], Step [200/600], Loss: 0.0008
Epoch [33/64], Step [300/600], Loss: 0.0100
Epoch [33/64], Step [400/600], Loss: 0.0213
Epoch [33/64], Step [500/600], Loss: 0.0025
Epoch [33/64], Step [600/600], Loss: 0.0021
Epoch [34/64], Step [100/600], Loss: 0.0022
Epoch [34/64], Step [200/600], Loss: 0.0004
Epoch [34/64], Step [300/600], Loss: 0.0002
Epoch [34/64], Step [400/600], Loss: 0.0002
Epoch [34/64], Step [500/600], Loss: 0.0002
Epoch [34/64], Step [600/600], Loss: 0.0071
Epoch [35/64], Step [100/600], Loss: 0.0001
Epoch [35/64], Step [200/600], Loss: 0.0004
Epoch [35/64], Step [300/600], Loss: 0.0001
Epoch [35/64], Step [400/600], Loss: 0.0004
Epoch [35/64], Step [500/600], Loss: 0.0000
Epoch [35/64], Step [600/600], Loss: 0.0002
Epoch [36/64], Step [100/600], Loss: 0.0000
Epoch [36/64], Step [200/600], Loss: 0.0001
Epoch [36/64], Step [300/600], Loss: 0.0035
Epoch [36/64], Step [400/600], Loss: 0.0002
Epoch [36/64], Step [500/600], Loss: 0.0006
Epoch [36/64], Step [600/600], Loss: 0.0000
Epoch [37/64], Step [100/600], Loss: 0.0001
Epoch [37/64], Step [200/600], Loss: 0.0000
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: 0.0000
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: 0.0002
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: 0.0002
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: 0.0000
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: 0.0001
Epoch [40/64], Step [200/600], Loss: 0.0000
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: 0.0001
Epoch [40/64], Step [600/600], Loss: 0.0001
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: 0.0001
Epoch [41/64], Step [400/600], Loss: 0.0000
Epoch [41/64], Step [500/600], Loss: 0.0000
Epoch [41/64], Step [600/600], Loss: 0.0001
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: 0.0000
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: 0.0000
Epoch [43/64], Step [500/600], Loss: 0.0000
Epoch [43/64], Step [600/600], Loss: 0.0186
Epoch [44/64], Step [100/600], Loss: 0.0001
Epoch [44/64], Step [200/600], Loss: 0.0011
Epoch [44/64], Step [300/600], Loss: 0.0017
Epoch [44/64], Step [400/600], Loss: 0.0001
Epoch [44/64], Step [500/600], Loss: 0.0484
Epoch [44/64], Step [600/600], Loss: 0.0000
Epoch [45/64], Step [100/600], Loss: 0.0000
Epoch [45/64], Step [200/600], Loss: 0.0010
Epoch [45/64], Step [300/600], Loss: 0.0006
Epoch [45/64], Step [400/600], Loss: 0.0001
Epoch [45/64], Step [500/600], Loss: 0.0001
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: 0.0038
Epoch [46/64], Step [300/600], Loss: 0.0000
Epoch [46/64], Step [400/600], Loss: 0.0000
Epoch [46/64], Step [500/600], Loss: 0.0003
Epoch [46/64], Step [600/600], Loss: 0.0000
Epoch [47/64], Step [100/600], Loss: 0.0001
Epoch [47/64], Step [200/600], Loss: 0.0008
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: 0.0002
Epoch [47/64], Step [600/600], Loss: 0.0001
Epoch [48/64], Step [100/600], Loss: 0.0001
Epoch [48/64], Step [200/600], Loss: 0.0001
Epoch [48/64], Step [300/600], Loss: 0.0002
Epoch [48/64], Step [400/600], Loss: 0.0002
Epoch [48/64], Step [500/600], Loss: 0.0003
Epoch [48/64], Step [600/600], Loss: 0.0002
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: 0.0001
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: 0.0001
Epoch [50/64], Step [400/600], Loss: 0.0001
Epoch [50/64], Step [500/600], Loss: 0.0000
Epoch [50/64], Step [600/600], Loss: 0.0000
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: 0.0001
Epoch [51/64], Step [500/600], Loss: 0.0001
Epoch [51/64], Step [600/600], Loss: 0.0001
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: 0.0000
Epoch [52/64], Step [400/600], Loss: 0.0001
Epoch [52/64], Step [500/600], Loss: 0.0001
Epoch [52/64], Step [600/600], Loss: 0.0000
Epoch [53/64], Step [100/600], Loss: 0.0000
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: 0.0000
Epoch [53/64], Step [500/600], Loss: 0.0000
Epoch [53/64], Step [600/600], Loss: 0.0007
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: 0.0004
Epoch [54/64], Step [500/600], Loss: 0.0019
Epoch [54/64], Step [600/600], Loss: 0.0071
Epoch [55/64], Step [100/600], Loss: 0.0005
Epoch [55/64], Step [200/600], Loss: 0.0074
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: 0.0000
Epoch [56/64], Step [100/600], Loss: 0.0001
Epoch [56/64], Step [200/600], Loss: 0.0004
Epoch [56/64], Step [300/600], Loss: 0.0000
Epoch [56/64], Step [400/600], Loss: 0.0001
Epoch [56/64], Step [500/600], Loss: 0.0015
Epoch [56/64], Step [600/600], Loss: 0.0000
Epoch [57/64], Step [100/600], Loss: 0.0009
Epoch [57/64], Step [200/600], Loss: 0.0002
Epoch [57/64], Step [300/600], Loss: 0.0002
Epoch [57/64], Step [400/600], Loss: 0.0005
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: 0.0001
Epoch [58/64], Step [200/600], Loss: 0.0000
Epoch [58/64], Step [300/600], Loss: 0.0007
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: 0.0000
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: 0.0002
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: 0.0001
Epoch [60/64], Step [500/600], Loss: 0.0001
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: 0.0000
Epoch [61/64], Step [500/600], Loss: 0.0000
Epoch [61/64], Step [600/600], Loss: 0.0000
Epoch [62/64], Step [100/600], Loss: 0.0001
Epoch [62/64], Step [200/600], Loss: 0.0001
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: 0.0001
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: 0.0001
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: 0.0002
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: 0.0012
Epoch [64/64], Step [600/600], Loss: 0.0301
Pytorch test completed in 440.223 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins6806710746414470659.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