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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 96861 queued and waiting for resources
srun: job 96861 has been allocated resources
Running benchmark on hydro04
Epoch [1/64], Step [100/600], Loss: 0.2271
Epoch [1/64], Step [200/600], Loss: 0.1237
Epoch [1/64], Step [300/600], Loss: 0.2076
Epoch [1/64], Step [400/600], Loss: 0.2482
Epoch [1/64], Step [500/600], Loss: 0.0404
Epoch [1/64], Step [600/600], Loss: 0.0722
Epoch [2/64], Step [100/600], Loss: 0.1079
Epoch [2/64], Step [200/600], Loss: 0.0956
Epoch [2/64], Step [300/600], Loss: 0.0490
Epoch [2/64], Step [400/600], Loss: 0.0693
Epoch [2/64], Step [500/600], Loss: 0.0496
Epoch [2/64], Step [600/600], Loss: 0.0874
Epoch [3/64], Step [100/600], Loss: 0.0190
Epoch [3/64], Step [200/600], Loss: 0.0949
Epoch [3/64], Step [300/600], Loss: 0.0390
Epoch [3/64], Step [400/600], Loss: 0.0236
Epoch [3/64], Step [500/600], Loss: 0.0320
Epoch [3/64], Step [600/600], Loss: 0.0145
Epoch [4/64], Step [100/600], Loss: 0.0152
Epoch [4/64], Step [200/600], Loss: 0.0252
Epoch [4/64], Step [300/600], Loss: 0.0961
Epoch [4/64], Step [400/600], Loss: 0.0716
Epoch [4/64], Step [500/600], Loss: 0.0150
Epoch [4/64], Step [600/600], Loss: 0.0146
Epoch [5/64], Step [100/600], Loss: 0.0425
Epoch [5/64], Step [200/600], Loss: 0.0162
Epoch [5/64], Step [300/600], Loss: 0.0239
Epoch [5/64], Step [400/600], Loss: 0.0220
Epoch [5/64], Step [500/600], Loss: 0.0142
Epoch [5/64], Step [600/600], Loss: 0.0057
Epoch [6/64], Step [100/600], Loss: 0.0118
Epoch [6/64], Step [200/600], Loss: 0.0162
Epoch [6/64], Step [300/600], Loss: 0.0083
Epoch [6/64], Step [400/600], Loss: 0.0434
Epoch [6/64], Step [500/600], Loss: 0.0130
Epoch [6/64], Step [600/600], Loss: 0.0193
Epoch [7/64], Step [100/600], Loss: 0.0028
Epoch [7/64], Step [200/600], Loss: 0.0094
Epoch [7/64], Step [300/600], Loss: 0.0115
Epoch [7/64], Step [400/600], Loss: 0.0349
Epoch [7/64], Step [500/600], Loss: 0.0014
Epoch [7/64], Step [600/600], Loss: 0.0083
Epoch [8/64], Step [100/600], Loss: 0.0157
Epoch [8/64], Step [200/600], Loss: 0.0140
Epoch [8/64], Step [300/600], Loss: 0.0426
Epoch [8/64], Step [400/600], Loss: 0.0071
Epoch [8/64], Step [500/600], Loss: 0.0028
Epoch [8/64], Step [600/600], Loss: 0.0179
Epoch [9/64], Step [100/600], Loss: 0.0469
Epoch [9/64], Step [200/600], Loss: 0.0156
Epoch [9/64], Step [300/600], Loss: 0.0183
Epoch [9/64], Step [400/600], Loss: 0.0050
Epoch [9/64], Step [500/600], Loss: 0.0200
Epoch [9/64], Step [600/600], Loss: 0.0065
Epoch [10/64], Step [100/600], Loss: 0.0366
Epoch [10/64], Step [200/600], Loss: 0.0216
Epoch [10/64], Step [300/600], Loss: 0.0509
Epoch [10/64], Step [400/600], Loss: 0.0033
Epoch [10/64], Step [500/600], Loss: 0.0134
Epoch [10/64], Step [600/600], Loss: 0.0060
Epoch [11/64], Step [100/600], Loss: 0.0366
Epoch [11/64], Step [200/600], Loss: 0.0032
Epoch [11/64], Step [300/600], Loss: 0.0139
Epoch [11/64], Step [400/600], Loss: 0.0149
Epoch [11/64], Step [500/600], Loss: 0.0016
Epoch [11/64], Step [600/600], Loss: 0.0412
Epoch [12/64], Step [100/600], Loss: 0.0059
Epoch [12/64], Step [200/600], Loss: 0.0019
Epoch [12/64], Step [300/600], Loss: 0.0049
Epoch [12/64], Step [400/600], Loss: 0.0144
Epoch [12/64], Step [500/600], Loss: 0.0034
Epoch [12/64], Step [600/600], Loss: 0.0122
Epoch [13/64], Step [100/600], Loss: 0.0036
Epoch [13/64], Step [200/600], Loss: 0.0132
Epoch [13/64], Step [300/600], Loss: 0.0051
Epoch [13/64], Step [400/600], Loss: 0.0004
Epoch [13/64], Step [500/600], Loss: 0.0178
Epoch [13/64], Step [600/600], Loss: 0.0014
Epoch [14/64], Step [100/600], Loss: 0.0003
Epoch [14/64], Step [200/600], Loss: 0.0083
Epoch [14/64], Step [300/600], Loss: 0.0021
Epoch [14/64], Step [400/600], Loss: 0.0012
Epoch [14/64], Step [500/600], Loss: 0.0013
Epoch [14/64], Step [600/600], Loss: 0.0022
Epoch [15/64], Step [100/600], Loss: 0.0050
Epoch [15/64], Step [200/600], Loss: 0.0009
Epoch [15/64], Step [300/600], Loss: 0.0079
Epoch [15/64], Step [400/600], Loss: 0.0023
Epoch [15/64], Step [500/600], Loss: 0.0443
Epoch [15/64], Step [600/600], Loss: 0.0026
Epoch [16/64], Step [100/600], Loss: 0.0009
Epoch [16/64], Step [200/600], Loss: 0.0110
Epoch [16/64], Step [300/600], Loss: 0.0015
Epoch [16/64], Step [400/600], Loss: 0.0029
Epoch [16/64], Step [500/600], Loss: 0.0070
Epoch [16/64], Step [600/600], Loss: 0.0046
Epoch [17/64], Step [100/600], Loss: 0.0014
Epoch [17/64], Step [200/600], Loss: 0.0005
Epoch [17/64], Step [300/600], Loss: 0.0004
Epoch [17/64], Step [400/600], Loss: 0.0068
Epoch [17/64], Step [500/600], Loss: 0.0319
Epoch [17/64], Step [600/600], Loss: 0.0004
Epoch [18/64], Step [100/600], Loss: 0.0035
Epoch [18/64], Step [200/600], Loss: 0.0074
Epoch [18/64], Step [300/600], Loss: 0.0029
Epoch [18/64], Step [400/600], Loss: 0.0039
Epoch [18/64], Step [500/600], Loss: 0.0005
Epoch [18/64], Step [600/600], Loss: 0.0086
Epoch [19/64], Step [100/600], Loss: 0.0009
Epoch [19/64], Step [200/600], Loss: 0.0003
Epoch [19/64], Step [300/600], Loss: 0.0022
Epoch [19/64], Step [400/600], Loss: 0.0026
Epoch [19/64], Step [500/600], Loss: 0.0004
Epoch [19/64], Step [600/600], Loss: 0.0109
Epoch [20/64], Step [100/600], Loss: 0.0009
Epoch [20/64], Step [200/600], Loss: 0.0003
Epoch [20/64], Step [300/600], Loss: 0.0001
Epoch [20/64], Step [400/600], Loss: 0.0005
Epoch [20/64], Step [500/600], Loss: 0.0024
Epoch [20/64], Step [600/600], Loss: 0.0023
Epoch [21/64], Step [100/600], Loss: 0.0061
Epoch [21/64], Step [200/600], Loss: 0.0031
Epoch [21/64], Step [300/600], Loss: 0.0018
Epoch [21/64], Step [400/600], Loss: 0.0016
Epoch [21/64], Step [500/600], Loss: 0.0040
Epoch [21/64], Step [600/600], Loss: 0.0207
Epoch [22/64], Step [100/600], Loss: 0.0004
Epoch [22/64], Step [200/600], Loss: 0.0002
Epoch [22/64], Step [300/600], Loss: 0.0025
Epoch [22/64], Step [400/600], Loss: 0.0025
Epoch [22/64], Step [500/600], Loss: 0.0101
Epoch [22/64], Step [600/600], Loss: 0.0000
Epoch [23/64], Step [100/600], Loss: 0.0005
Epoch [23/64], Step [200/600], Loss: 0.0016
Epoch [23/64], Step [300/600], Loss: 0.0007
Epoch [23/64], Step [400/600], Loss: 0.0030
Epoch [23/64], Step [500/600], Loss: 0.0008
Epoch [23/64], Step [600/600], Loss: 0.0004
Epoch [24/64], Step [100/600], Loss: 0.0125
Epoch [24/64], Step [200/600], Loss: 0.0035
Epoch [24/64], Step [300/600], Loss: 0.0235
Epoch [24/64], Step [400/600], Loss: 0.0026
Epoch [24/64], Step [500/600], Loss: 0.0008
Epoch [24/64], Step [600/600], Loss: 0.0004
Epoch [25/64], Step [100/600], Loss: 0.0037
Epoch [25/64], Step [200/600], Loss: 0.0014
Epoch [25/64], Step [300/600], Loss: 0.0001
Epoch [25/64], Step [400/600], Loss: 0.0071
Epoch [25/64], Step [500/600], Loss: 0.0004
Epoch [25/64], Step [600/600], Loss: 0.0010
Epoch [26/64], Step [100/600], Loss: 0.0294
Epoch [26/64], Step [200/600], Loss: 0.0007
Epoch [26/64], Step [300/600], Loss: 0.0011
Epoch [26/64], Step [400/600], Loss: 0.0011
Epoch [26/64], Step [500/600], Loss: 0.0005
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: 0.0002
Epoch [27/64], Step [300/600], Loss: 0.0005
Epoch [27/64], Step [400/600], Loss: 0.0007
Epoch [27/64], Step [500/600], Loss: 0.0003
Epoch [27/64], Step [600/600], Loss: 0.0001
Epoch [28/64], Step [100/600], Loss: 0.0004
Epoch [28/64], Step [200/600], Loss: 0.0003
Epoch [28/64], Step [300/600], Loss: 0.0004
Epoch [28/64], Step [400/600], Loss: 0.0003
Epoch [28/64], Step [500/600], Loss: 0.0006
Epoch [28/64], Step [600/600], Loss: 0.0003
Epoch [29/64], Step [100/600], Loss: 0.0000
Epoch [29/64], Step [200/600], Loss: 0.0001
Epoch [29/64], Step [300/600], Loss: 0.0000
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: 0.0002
Epoch [30/64], Step [100/600], Loss: 0.0000
Epoch [30/64], Step [200/600], Loss: 0.0010
Epoch [30/64], Step [300/600], Loss: 0.0001
Epoch [30/64], Step [400/600], Loss: 0.0000
Epoch [30/64], Step [500/600], Loss: 0.0001
Epoch [30/64], Step [600/600], Loss: 0.0038
Epoch [31/64], Step [100/600], Loss: 0.0143
Epoch [31/64], Step [200/600], Loss: 0.0072
Epoch [31/64], Step [300/600], Loss: 0.0026
Epoch [31/64], Step [400/600], Loss: 0.0060
Epoch [31/64], Step [500/600], Loss: 0.0002
Epoch [31/64], Step [600/600], Loss: 0.0025
Epoch [32/64], Step [100/600], Loss: 0.0045
Epoch [32/64], Step [200/600], Loss: 0.0001
Epoch [32/64], Step [300/600], Loss: 0.0003
Epoch [32/64], Step [400/600], Loss: 0.0004
Epoch [32/64], Step [500/600], Loss: 0.0002
Epoch [32/64], Step [600/600], Loss: 0.0002
Epoch [33/64], Step [100/600], Loss: 0.0002
Epoch [33/64], Step [200/600], Loss: 0.0001
Epoch [33/64], Step [300/600], Loss: 0.0000
Epoch [33/64], Step [400/600], Loss: 0.0001
Epoch [33/64], Step [500/600], Loss: 0.0001
Epoch [33/64], Step [600/600], Loss: 0.0006
Epoch [34/64], Step [100/600], Loss: 0.0003
Epoch [34/64], Step [200/600], Loss: 0.0000
Epoch [34/64], Step [300/600], Loss: 0.0001
Epoch [34/64], Step [400/600], Loss: 0.0006
Epoch [34/64], Step [500/600], Loss: 0.0001
Epoch [34/64], Step [600/600], Loss: 0.0005
Epoch [35/64], Step [100/600], Loss: 0.0002
Epoch [35/64], Step [200/600], Loss: 0.0000
Epoch [35/64], Step [300/600], Loss: 0.0002
Epoch [35/64], Step [400/600], Loss: 0.0001
Epoch [35/64], Step [500/600], Loss: 0.0004
Epoch [35/64], Step [600/600], Loss: 0.0002
Epoch [36/64], Step [100/600], Loss: 0.0004
Epoch [36/64], Step [200/600], Loss: 0.0000
Epoch [36/64], Step [300/600], Loss: 0.0002
Epoch [36/64], Step [400/600], Loss: 0.0005
Epoch [36/64], Step [500/600], Loss: 0.0000
Epoch [36/64], Step [600/600], Loss: 0.0002
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: 0.0000
Epoch [37/64], Step [400/600], Loss: 0.0002
Epoch [37/64], Step [500/600], Loss: 0.0010
Epoch [37/64], Step [600/600], Loss: 0.0001
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: 0.0000
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: 0.0020
Epoch [39/64], Step [100/600], Loss: 0.0006
Epoch [39/64], Step [200/600], Loss: 0.0064
Epoch [39/64], Step [300/600], Loss: 0.0235
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: 0.0001
Epoch [40/64], Step [100/600], Loss: 0.0004
Epoch [40/64], Step [200/600], Loss: 0.0027
Epoch [40/64], Step [300/600], Loss: 0.0002
Epoch [40/64], Step [400/600], Loss: 0.0001
Epoch [40/64], Step [500/600], Loss: 0.0010
Epoch [40/64], Step [600/600], Loss: 0.0001
Epoch [41/64], Step [100/600], Loss: 0.0003
Epoch [41/64], Step [200/600], Loss: 0.0003
Epoch [41/64], Step [300/600], Loss: 0.0003
Epoch [41/64], Step [400/600], Loss: 0.0002
Epoch [41/64], Step [500/600], Loss: 0.0004
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: 0.0001
Epoch [42/64], Step [300/600], Loss: 0.0002
Epoch [42/64], Step [400/600], Loss: 0.0001
Epoch [42/64], Step [500/600], Loss: 0.0002
Epoch [42/64], Step [600/600], Loss: 0.0002
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: 0.0007
Epoch [43/64], Step [400/600], Loss: 0.0000
Epoch [43/64], Step [500/600], Loss: 0.0002
Epoch [43/64], Step [600/600], Loss: 0.0001
Epoch [44/64], Step [100/600], Loss: 0.0001
Epoch [44/64], Step [200/600], Loss: 0.0000
Epoch [44/64], Step [300/600], Loss: 0.0003
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: 0.0001
Epoch [45/64], Step [100/600], Loss: 0.0000
Epoch [45/64], Step [200/600], Loss: 0.0001
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: 0.0000
Epoch [45/64], Step [600/600], Loss: 0.0000
Epoch [46/64], Step [100/600], Loss: 0.0001
Epoch [46/64], Step [200/600], Loss: 0.0000
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: 0.0001
Epoch [46/64], Step [600/600], Loss: 0.0003
Epoch [47/64], Step [100/600], Loss: 0.0000
Epoch [47/64], Step [200/600], Loss: 0.0001
Epoch [47/64], Step [300/600], Loss: 0.0002
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: 0.0001
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: 0.0000
Epoch [49/64], Step [100/600], Loss: 0.0007
Epoch [49/64], Step [200/600], Loss: 0.0018
Epoch [49/64], Step [300/600], Loss: 0.0154
Epoch [49/64], Step [400/600], Loss: 0.0096
Epoch [49/64], Step [500/600], Loss: 0.0118
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: 0.0008
Epoch [50/64], Step [300/600], Loss: 0.0001
Epoch [50/64], Step [400/600], Loss: 0.0002
Epoch [50/64], Step [500/600], Loss: 0.0004
Epoch [50/64], Step [600/600], Loss: 0.0005
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: 0.0001
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: 0.0000
Epoch [52/64], Step [100/600], Loss: 0.0003
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: 0.0001
Epoch [52/64], Step [500/600], Loss: 0.0001
Epoch [52/64], Step [600/600], Loss: 0.0001
Epoch [53/64], Step [100/600], Loss: 0.0000
Epoch [53/64], Step [200/600], Loss: 0.0002
Epoch [53/64], Step [300/600], Loss: 0.0001
Epoch [53/64], Step [400/600], Loss: 0.0000
Epoch [53/64], Step [500/600], Loss: 0.0002
Epoch [53/64], Step [600/600], Loss: 0.0002
Epoch [54/64], Step [100/600], Loss: 0.0000
Epoch [54/64], Step [200/600], Loss: 0.0000
Epoch [54/64], Step [300/600], Loss: 0.0001
Epoch [54/64], Step [400/600], Loss: 0.0000
Epoch [54/64], Step [500/600], Loss: 0.0003
Epoch [54/64], Step [600/600], Loss: 0.0000
Epoch [55/64], Step [100/600], Loss: 0.0001
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: 0.0001
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: 0.0000
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: 0.0000
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: 0.0000
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: 0.0001
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: 0.0001
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: 0.0501
Epoch [60/64], Step [100/600], Loss: 0.0008
Epoch [60/64], Step [200/600], Loss: 0.0002
Epoch [60/64], Step [300/600], Loss: 0.0054
Epoch [60/64], Step [400/600], Loss: 0.0004
Epoch [60/64], Step [500/600], Loss: 0.0013
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: 0.0001
Epoch [61/64], Step [300/600], Loss: 0.0000
Epoch [61/64], Step [400/600], Loss: 0.0009
Epoch [61/64], Step [500/600], Loss: 0.0000
Epoch [61/64], Step [600/600], Loss: 0.0001
Epoch [62/64], Step [100/600], Loss: 0.0001
Epoch [62/64], Step [200/600], Loss: 0.0003
Epoch [62/64], Step [300/600], Loss: 0.0000
Epoch [62/64], Step [400/600], Loss: 0.0003
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: 0.0000
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: 0.0001
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: 0.0001
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: 0.0001
Pytorch test completed in 363.656 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins16881522386808429256.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