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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 96741 queued and waiting for resources
srun: job 96741 has been allocated resources
Running benchmark on hydro04
Epoch [1/64], Step [100/600], Loss: 0.1669
Epoch [1/64], Step [200/600], Loss: 0.1456
Epoch [1/64], Step [300/600], Loss: 0.0908
Epoch [1/64], Step [400/600], Loss: 0.0491
Epoch [1/64], Step [500/600], Loss: 0.0167
Epoch [1/64], Step [600/600], Loss: 0.0214
Epoch [2/64], Step [100/600], Loss: 0.0446
Epoch [2/64], Step [200/600], Loss: 0.0473
Epoch [2/64], Step [300/600], Loss: 0.0263
Epoch [2/64], Step [400/600], Loss: 0.0142
Epoch [2/64], Step [500/600], Loss: 0.0632
Epoch [2/64], Step [600/600], Loss: 0.0547
Epoch [3/64], Step [100/600], Loss: 0.0299
Epoch [3/64], Step [200/600], Loss: 0.0485
Epoch [3/64], Step [300/600], Loss: 0.0350
Epoch [3/64], Step [400/600], Loss: 0.0337
Epoch [3/64], Step [500/600], Loss: 0.0085
Epoch [3/64], Step [600/600], Loss: 0.0319
Epoch [4/64], Step [100/600], Loss: 0.0164
Epoch [4/64], Step [200/600], Loss: 0.0169
Epoch [4/64], Step [300/600], Loss: 0.0093
Epoch [4/64], Step [400/600], Loss: 0.0411
Epoch [4/64], Step [500/600], Loss: 0.0461
Epoch [4/64], Step [600/600], Loss: 0.0115
Epoch [5/64], Step [100/600], Loss: 0.0018
Epoch [5/64], Step [200/600], Loss: 0.0161
Epoch [5/64], Step [300/600], Loss: 0.0374
Epoch [5/64], Step [400/600], Loss: 0.0103
Epoch [5/64], Step [500/600], Loss: 0.0343
Epoch [5/64], Step [600/600], Loss: 0.0130
Epoch [6/64], Step [100/600], Loss: 0.0069
Epoch [6/64], Step [200/600], Loss: 0.0339
Epoch [6/64], Step [300/600], Loss: 0.0454
Epoch [6/64], Step [400/600], Loss: 0.0471
Epoch [6/64], Step [500/600], Loss: 0.0274
Epoch [6/64], Step [600/600], Loss: 0.0165
Epoch [7/64], Step [100/600], Loss: 0.0135
Epoch [7/64], Step [200/600], Loss: 0.0174
Epoch [7/64], Step [300/600], Loss: 0.0508
Epoch [7/64], Step [400/600], Loss: 0.0132
Epoch [7/64], Step [500/600], Loss: 0.0165
Epoch [7/64], Step [600/600], Loss: 0.0327
Epoch [8/64], Step [100/600], Loss: 0.0022
Epoch [8/64], Step [200/600], Loss: 0.0144
Epoch [8/64], Step [300/600], Loss: 0.0431
Epoch [8/64], Step [400/600], Loss: 0.0022
Epoch [8/64], Step [500/600], Loss: 0.0181
Epoch [8/64], Step [600/600], Loss: 0.0009
Epoch [9/64], Step [100/600], Loss: 0.0073
Epoch [9/64], Step [200/600], Loss: 0.0025
Epoch [9/64], Step [300/600], Loss: 0.0092
Epoch [9/64], Step [400/600], Loss: 0.0120
Epoch [9/64], Step [500/600], Loss: 0.0011
Epoch [9/64], Step [600/600], Loss: 0.0038
Epoch [10/64], Step [100/600], Loss: 0.0008
Epoch [10/64], Step [200/600], Loss: 0.0057
Epoch [10/64], Step [300/600], Loss: 0.0169
Epoch [10/64], Step [400/600], Loss: 0.0282
Epoch [10/64], Step [500/600], Loss: 0.0014
Epoch [10/64], Step [600/600], Loss: 0.0023
Epoch [11/64], Step [100/600], Loss: 0.0019
Epoch [11/64], Step [200/600], Loss: 0.0007
Epoch [11/64], Step [300/600], Loss: 0.0063
Epoch [11/64], Step [400/600], Loss: 0.0121
Epoch [11/64], Step [500/600], Loss: 0.0102
Epoch [11/64], Step [600/600], Loss: 0.0222
Epoch [12/64], Step [100/600], Loss: 0.0079
Epoch [12/64], Step [200/600], Loss: 0.0078
Epoch [12/64], Step [300/600], Loss: 0.0186
Epoch [12/64], Step [400/600], Loss: 0.0033
Epoch [12/64], Step [500/600], Loss: 0.0024
Epoch [12/64], Step [600/600], Loss: 0.0084
Epoch [13/64], Step [100/600], Loss: 0.0013
Epoch [13/64], Step [200/600], Loss: 0.0004
Epoch [13/64], Step [300/600], Loss: 0.0010
Epoch [13/64], Step [400/600], Loss: 0.0018
Epoch [13/64], Step [500/600], Loss: 0.0049
Epoch [13/64], Step [600/600], Loss: 0.0238
Epoch [14/64], Step [100/600], Loss: 0.0030
Epoch [14/64], Step [200/600], Loss: 0.0017
Epoch [14/64], Step [300/600], Loss: 0.0007
Epoch [14/64], Step [400/600], Loss: 0.0020
Epoch [14/64], Step [500/600], Loss: 0.0198
Epoch [14/64], Step [600/600], Loss: 0.0017
Epoch [15/64], Step [100/600], Loss: 0.0017
Epoch [15/64], Step [200/600], Loss: 0.0016
Epoch [15/64], Step [300/600], Loss: 0.0013
Epoch [15/64], Step [400/600], Loss: 0.0011
Epoch [15/64], Step [500/600], Loss: 0.0028
Epoch [15/64], Step [600/600], Loss: 0.0053
Epoch [16/64], Step [100/600], Loss: 0.0008
Epoch [16/64], Step [200/600], Loss: 0.0063
Epoch [16/64], Step [300/600], Loss: 0.0003
Epoch [16/64], Step [400/600], Loss: 0.0009
Epoch [16/64], Step [500/600], Loss: 0.0030
Epoch [16/64], Step [600/600], Loss: 0.0071
Epoch [17/64], Step [100/600], Loss: 0.0002
Epoch [17/64], Step [200/600], Loss: 0.0005
Epoch [17/64], Step [300/600], Loss: 0.0010
Epoch [17/64], Step [400/600], Loss: 0.0019
Epoch [17/64], Step [500/600], Loss: 0.0046
Epoch [17/64], Step [600/600], Loss: 0.0337
Epoch [18/64], Step [100/600], Loss: 0.0098
Epoch [18/64], Step [200/600], Loss: 0.0075
Epoch [18/64], Step [300/600], Loss: 0.0024
Epoch [18/64], Step [400/600], Loss: 0.0160
Epoch [18/64], Step [500/600], Loss: 0.0005
Epoch [18/64], Step [600/600], Loss: 0.0021
Epoch [19/64], Step [100/600], Loss: 0.0127
Epoch [19/64], Step [200/600], Loss: 0.0020
Epoch [19/64], Step [300/600], Loss: 0.0034
Epoch [19/64], Step [400/600], Loss: 0.0002
Epoch [19/64], Step [500/600], Loss: 0.0005
Epoch [19/64], Step [600/600], Loss: 0.0023
Epoch [20/64], Step [100/600], Loss: 0.0002
Epoch [20/64], Step [200/600], Loss: 0.0037
Epoch [20/64], Step [300/600], Loss: 0.0004
Epoch [20/64], Step [400/600], Loss: 0.0007
Epoch [20/64], Step [500/600], Loss: 0.0011
Epoch [20/64], Step [600/600], Loss: 0.0021
Epoch [21/64], Step [100/600], Loss: 0.0005
Epoch [21/64], Step [200/600], Loss: 0.0009
Epoch [21/64], Step [300/600], Loss: 0.0004
Epoch [21/64], Step [400/600], Loss: 0.0008
Epoch [21/64], Step [500/600], Loss: 0.0086
Epoch [21/64], Step [600/600], Loss: 0.0004
Epoch [22/64], Step [100/600], Loss: 0.0004
Epoch [22/64], Step [200/600], Loss: 0.0004
Epoch [22/64], Step [300/600], Loss: 0.0001
Epoch [22/64], Step [400/600], Loss: 0.0017
Epoch [22/64], Step [500/600], Loss: 0.0019
Epoch [22/64], Step [600/600], Loss: 0.0003
Epoch [23/64], Step [100/600], Loss: 0.0010
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: 0.0035
Epoch [23/64], Step [500/600], Loss: 0.0490
Epoch [23/64], Step [600/600], Loss: 0.0035
Epoch [24/64], Step [100/600], Loss: 0.0138
Epoch [24/64], Step [200/600], Loss: 0.0052
Epoch [24/64], Step [300/600], Loss: 0.0009
Epoch [24/64], Step [400/600], Loss: 0.0014
Epoch [24/64], Step [500/600], Loss: 0.0039
Epoch [24/64], Step [600/600], Loss: 0.0001
Epoch [25/64], Step [100/600], Loss: 0.0002
Epoch [25/64], Step [200/600], Loss: 0.0002
Epoch [25/64], Step [300/600], Loss: 0.0003
Epoch [25/64], Step [400/600], Loss: 0.0002
Epoch [25/64], Step [500/600], Loss: 0.0011
Epoch [25/64], Step [600/600], Loss: 0.0002
Epoch [26/64], Step [100/600], Loss: 0.0002
Epoch [26/64], Step [200/600], Loss: 0.0000
Epoch [26/64], Step [300/600], Loss: 0.0003
Epoch [26/64], Step [400/600], Loss: 0.0001
Epoch [26/64], Step [500/600], Loss: 0.0002
Epoch [26/64], Step [600/600], Loss: 0.0005
Epoch [27/64], Step [100/600], Loss: 0.0000
Epoch [27/64], Step [200/600], Loss: 0.0001
Epoch [27/64], Step [300/600], Loss: 0.0000
Epoch [27/64], Step [400/600], Loss: 0.0002
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: 0.0003
Epoch [28/64], Step [200/600], Loss: 0.0000
Epoch [28/64], Step [300/600], Loss: 0.0001
Epoch [28/64], Step [400/600], Loss: 0.0002
Epoch [28/64], Step [500/600], Loss: 0.0002
Epoch [28/64], Step [600/600], Loss: 0.0001
Epoch [29/64], Step [100/600], Loss: 0.0102
Epoch [29/64], Step [200/600], Loss: 0.0001
Epoch [29/64], Step [300/600], Loss: 0.0198
Epoch [29/64], Step [400/600], Loss: 0.0004
Epoch [29/64], Step [500/600], Loss: 0.0126
Epoch [29/64], Step [600/600], Loss: 0.0065
Epoch [30/64], Step [100/600], Loss: 0.0002
Epoch [30/64], Step [200/600], Loss: 0.0003
Epoch [30/64], Step [300/600], Loss: 0.0005
Epoch [30/64], Step [400/600], Loss: 0.0000
Epoch [30/64], Step [500/600], Loss: 0.0004
Epoch [30/64], Step [600/600], Loss: 0.0002
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: 0.0009
Epoch [31/64], Step [400/600], Loss: 0.0000
Epoch [31/64], Step [500/600], Loss: 0.0003
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: 0.0000
Epoch [32/64], Step [300/600], Loss: 0.0001
Epoch [32/64], Step [400/600], Loss: 0.0001
Epoch [32/64], Step [500/600], Loss: 0.0004
Epoch [32/64], Step [600/600], Loss: 0.0000
Epoch [33/64], Step [100/600], Loss: 0.0000
Epoch [33/64], Step [200/600], Loss: 0.0000
Epoch [33/64], Step [300/600], Loss: 0.0001
Epoch [33/64], Step [400/600], Loss: 0.0003
Epoch [33/64], Step [500/600], Loss: 0.0001
Epoch [33/64], Step [600/600], Loss: 0.0000
Epoch [34/64], Step [100/600], Loss: 0.0002
Epoch [34/64], Step [200/600], Loss: 0.0002
Epoch [34/64], Step [300/600], Loss: 0.0000
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: 0.0001
Epoch [35/64], Step [400/600], Loss: 0.0000
Epoch [35/64], Step [500/600], Loss: 0.0000
Epoch [35/64], Step [600/600], Loss: 0.0000
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: 0.0000
Epoch [36/64], Step [400/600], Loss: 0.0001
Epoch [36/64], Step [500/600], Loss: 0.0001
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: 0.0001
Epoch [37/64], Step [300/600], Loss: 0.0001
Epoch [37/64], Step [400/600], Loss: 0.0001
Epoch [37/64], Step [500/600], Loss: 0.0138
Epoch [37/64], Step [600/600], Loss: 0.0175
Epoch [38/64], Step [100/600], Loss: 0.0017
Epoch [38/64], Step [200/600], Loss: 0.0008
Epoch [38/64], Step [300/600], Loss: 0.0109
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: 0.0001
Epoch [39/64], Step [100/600], Loss: 0.0021
Epoch [39/64], Step [200/600], Loss: 0.0001
Epoch [39/64], Step [300/600], Loss: 0.0021
Epoch [39/64], Step [400/600], Loss: 0.0002
Epoch [39/64], Step [500/600], Loss: 0.0004
Epoch [39/64], Step [600/600], Loss: 0.0000
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: 0.0004
Epoch [40/64], Step [400/600], Loss: 0.0002
Epoch [40/64], Step [500/600], Loss: 0.0001
Epoch [40/64], Step [600/600], Loss: 0.0002
Epoch [41/64], Step [100/600], Loss: 0.0002
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: 0.0002
Epoch [41/64], Step [600/600], Loss: 0.0003
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: 0.0001
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: 0.0000
Epoch [43/64], Step [100/600], Loss: 0.0003
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: 0.0000
Epoch [43/64], Step [500/600], Loss: 0.0000
Epoch [43/64], Step [600/600], Loss: 0.0002
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: 0.0000
Epoch [44/64], Step [400/600], Loss: 0.0001
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: 0.0000
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: 0.0000
Epoch [45/64], Step [500/600], Loss: 0.0000
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: 0.0000
Epoch [46/64], Step [300/600], Loss: 0.0000
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: 0.0001
Epoch [47/64], Step [100/600], Loss: 0.0000
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: 0.0000
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: 0.0000
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: 0.0000
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: 0.0212
Epoch [50/64], Step [200/600], Loss: 0.0236
Epoch [50/64], Step [300/600], Loss: 0.0077
Epoch [50/64], Step [400/600], Loss: 0.0000
Epoch [50/64], Step [500/600], Loss: 0.0013
Epoch [50/64], Step [600/600], Loss: 0.0000
Epoch [51/64], Step [100/600], Loss: 0.0007
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: 0.0013
Epoch [51/64], Step [500/600], Loss: 0.0003
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: 0.0000
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: 0.0013
Epoch [52/64], Step [600/600], Loss: 0.0002
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: 0.0006
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: 0.0000
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: 0.0000
Epoch [54/64], Step [400/600], Loss: 0.0000
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: 0.0001
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: 0.0000
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: 0.0000
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: 0.0001
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: 0.0001
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: 0.0000
Epoch [58/64], Step [600/600], Loss: 0.0001
Epoch [59/64], Step [100/600], Loss: 0.0001
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: 0.0002
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: 0.0000
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: 0.0000
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: 0.0123
Epoch [62/64], Step [400/600], Loss: 0.0047
Epoch [62/64], Step [500/600], Loss: 0.0002
Epoch [62/64], Step [600/600], Loss: 0.0005
Epoch [63/64], Step [100/600], Loss: 0.0111
Epoch [63/64], Step [200/600], Loss: 0.0002
Epoch [63/64], Step [300/600], Loss: 0.0020
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: 0.0001
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: 0.0007
Epoch [64/64], Step [600/600], Loss: 0.0001
Pytorch test completed in 435.819 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins554536121037706145.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