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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 97819 queued and waiting for resources
srun: job 97819 has been allocated resources
Running benchmark on hydro03
Epoch [1/64], Step [100/600], Loss: 0.2060
Epoch [1/64], Step [200/600], Loss: 0.2374
Epoch [1/64], Step [300/600], Loss: 0.0878
Epoch [1/64], Step [400/600], Loss: 0.0709
Epoch [1/64], Step [500/600], Loss: 0.1194
Epoch [1/64], Step [600/600], Loss: 0.0577
Epoch [2/64], Step [100/600], Loss: 0.0464
Epoch [2/64], Step [200/600], Loss: 0.0188
Epoch [2/64], Step [300/600], Loss: 0.0285
Epoch [2/64], Step [400/600], Loss: 0.0288
Epoch [2/64], Step [500/600], Loss: 0.0450
Epoch [2/64], Step [600/600], Loss: 0.0490
Epoch [3/64], Step [100/600], Loss: 0.0082
Epoch [3/64], Step [200/600], Loss: 0.0573
Epoch [3/64], Step [300/600], Loss: 0.0222
Epoch [3/64], Step [400/600], Loss: 0.0317
Epoch [3/64], Step [500/600], Loss: 0.0183
Epoch [3/64], Step [600/600], Loss: 0.0067
Epoch [4/64], Step [100/600], Loss: 0.0152
Epoch [4/64], Step [200/600], Loss: 0.0197
Epoch [4/64], Step [300/600], Loss: 0.0204
Epoch [4/64], Step [400/600], Loss: 0.0947
Epoch [4/64], Step [500/600], Loss: 0.0441
Epoch [4/64], Step [600/600], Loss: 0.0275
Epoch [5/64], Step [100/600], Loss: 0.0336
Epoch [5/64], Step [200/600], Loss: 0.0089
Epoch [5/64], Step [300/600], Loss: 0.0218
Epoch [5/64], Step [400/600], Loss: 0.0077
Epoch [5/64], Step [500/600], Loss: 0.0019
Epoch [5/64], Step [600/600], Loss: 0.0134
Epoch [6/64], Step [100/600], Loss: 0.0084
Epoch [6/64], Step [200/600], Loss: 0.0589
Epoch [6/64], Step [300/600], Loss: 0.0007
Epoch [6/64], Step [400/600], Loss: 0.0370
Epoch [6/64], Step [500/600], Loss: 0.0018
Epoch [6/64], Step [600/600], Loss: 0.0202
Epoch [7/64], Step [100/600], Loss: 0.0042
Epoch [7/64], Step [200/600], Loss: 0.0219
Epoch [7/64], Step [300/600], Loss: 0.0466
Epoch [7/64], Step [400/600], Loss: 0.0364
Epoch [7/64], Step [500/600], Loss: 0.0112
Epoch [7/64], Step [600/600], Loss: 0.0079
Epoch [8/64], Step [100/600], Loss: 0.0043
Epoch [8/64], Step [200/600], Loss: 0.0027
Epoch [8/64], Step [300/600], Loss: 0.0259
Epoch [8/64], Step [400/600], Loss: 0.0423
Epoch [8/64], Step [500/600], Loss: 0.0220
Epoch [8/64], Step [600/600], Loss: 0.0121
Epoch [9/64], Step [100/600], Loss: 0.0173
Epoch [9/64], Step [200/600], Loss: 0.0100
Epoch [9/64], Step [300/600], Loss: 0.0078
Epoch [9/64], Step [400/600], Loss: 0.0159
Epoch [9/64], Step [500/600], Loss: 0.0249
Epoch [9/64], Step [600/600], Loss: 0.0081
Epoch [10/64], Step [100/600], Loss: 0.0111
Epoch [10/64], Step [200/600], Loss: 0.0103
Epoch [10/64], Step [300/600], Loss: 0.0025
Epoch [10/64], Step [400/600], Loss: 0.0039
Epoch [10/64], Step [500/600], Loss: 0.0022
Epoch [10/64], Step [600/600], Loss: 0.0196
Epoch [11/64], Step [100/600], Loss: 0.0010
Epoch [11/64], Step [200/600], Loss: 0.0019
Epoch [11/64], Step [300/600], Loss: 0.0033
Epoch [11/64], Step [400/600], Loss: 0.0017
Epoch [11/64], Step [500/600], Loss: 0.0045
Epoch [11/64], Step [600/600], Loss: 0.0021
Epoch [12/64], Step [100/600], Loss: 0.0161
Epoch [12/64], Step [200/600], Loss: 0.0058
Epoch [12/64], Step [300/600], Loss: 0.0115
Epoch [12/64], Step [400/600], Loss: 0.0007
Epoch [12/64], Step [500/600], Loss: 0.0020
Epoch [12/64], Step [600/600], Loss: 0.0460
Epoch [13/64], Step [100/600], Loss: 0.0011
Epoch [13/64], Step [200/600], Loss: 0.0017
Epoch [13/64], Step [300/600], Loss: 0.0136
Epoch [13/64], Step [400/600], Loss: 0.0294
Epoch [13/64], Step [500/600], Loss: 0.0178
Epoch [13/64], Step [600/600], Loss: 0.0051
Epoch [14/64], Step [100/600], Loss: 0.0021
Epoch [14/64], Step [200/600], Loss: 0.0332
Epoch [14/64], Step [300/600], Loss: 0.0043
Epoch [14/64], Step [400/600], Loss: 0.0045
Epoch [14/64], Step [500/600], Loss: 0.0223
Epoch [14/64], Step [600/600], Loss: 0.0002
Epoch [15/64], Step [100/600], Loss: 0.0054
Epoch [15/64], Step [200/600], Loss: 0.0230
Epoch [15/64], Step [300/600], Loss: 0.0123
Epoch [15/64], Step [400/600], Loss: 0.0131
Epoch [15/64], Step [500/600], Loss: 0.0259
Epoch [15/64], Step [600/600], Loss: 0.0111
Epoch [16/64], Step [100/600], Loss: 0.0011
Epoch [16/64], Step [200/600], Loss: 0.0021
Epoch [16/64], Step [300/600], Loss: 0.0091
Epoch [16/64], Step [400/600], Loss: 0.0082
Epoch [16/64], Step [500/600], Loss: 0.0201
Epoch [16/64], Step [600/600], Loss: 0.0031
Epoch [17/64], Step [100/600], Loss: 0.0037
Epoch [17/64], Step [200/600], Loss: 0.0008
Epoch [17/64], Step [300/600], Loss: 0.0041
Epoch [17/64], Step [400/600], Loss: 0.0164
Epoch [17/64], Step [500/600], Loss: 0.0171
Epoch [17/64], Step [600/600], Loss: 0.0019
Epoch [18/64], Step [100/600], Loss: 0.0004
Epoch [18/64], Step [200/600], Loss: 0.0013
Epoch [18/64], Step [300/600], Loss: 0.0024
Epoch [18/64], Step [400/600], Loss: 0.0027
Epoch [18/64], Step [500/600], Loss: 0.0129
Epoch [18/64], Step [600/600], Loss: 0.0068
Epoch [19/64], Step [100/600], Loss: 0.0002
Epoch [19/64], Step [200/600], Loss: 0.0029
Epoch [19/64], Step [300/600], Loss: 0.0174
Epoch [19/64], Step [400/600], Loss: 0.0015
Epoch [19/64], Step [500/600], Loss: 0.0006
Epoch [19/64], Step [600/600], Loss: 0.0003
Epoch [20/64], Step [100/600], Loss: 0.0002
Epoch [20/64], Step [200/600], Loss: 0.0077
Epoch [20/64], Step [300/600], Loss: 0.0005
Epoch [20/64], Step [400/600], Loss: 0.0015
Epoch [20/64], Step [500/600], Loss: 0.0004
Epoch [20/64], Step [600/600], Loss: 0.0163
Epoch [21/64], Step [100/600], Loss: 0.0009
Epoch [21/64], Step [200/600], Loss: 0.0014
Epoch [21/64], Step [300/600], Loss: 0.0010
Epoch [21/64], Step [400/600], Loss: 0.0013
Epoch [21/64], Step [500/600], Loss: 0.0005
Epoch [21/64], Step [600/600], Loss: 0.0027
Epoch [22/64], Step [100/600], Loss: 0.0017
Epoch [22/64], Step [200/600], Loss: 0.0092
Epoch [22/64], Step [300/600], Loss: 0.0020
Epoch [22/64], Step [400/600], Loss: 0.0008
Epoch [22/64], Step [500/600], Loss: 0.0023
Epoch [22/64], Step [600/600], Loss: 0.0013
Epoch [23/64], Step [100/600], Loss: 0.0005
Epoch [23/64], Step [200/600], Loss: 0.0015
Epoch [23/64], Step [300/600], Loss: 0.0005
Epoch [23/64], Step [400/600], Loss: 0.0001
Epoch [23/64], Step [500/600], Loss: 0.0048
Epoch [23/64], Step [600/600], Loss: 0.0013
Epoch [24/64], Step [100/600], Loss: 0.0019
Epoch [24/64], Step [200/600], Loss: 0.0033
Epoch [24/64], Step [300/600], Loss: 0.0008
Epoch [24/64], Step [400/600], Loss: 0.0017
Epoch [24/64], Step [500/600], Loss: 0.0009
Epoch [24/64], Step [600/600], Loss: 0.0065
Epoch [25/64], Step [100/600], Loss: 0.0000
Epoch [25/64], Step [200/600], Loss: 0.0001
Epoch [25/64], Step [300/600], Loss: 0.0011
Epoch [25/64], Step [400/600], Loss: 0.0003
Epoch [25/64], Step [500/600], Loss: 0.0002
Epoch [25/64], Step [600/600], Loss: 0.0003
Epoch [26/64], Step [100/600], Loss: 0.0021
Epoch [26/64], Step [200/600], Loss: 0.0001
Epoch [26/64], Step [300/600], Loss: 0.0005
Epoch [26/64], Step [400/600], Loss: 0.0004
Epoch [26/64], Step [500/600], Loss: 0.0002
Epoch [26/64], Step [600/600], Loss: 0.0000
Epoch [27/64], Step [100/600], Loss: 0.0007
Epoch [27/64], Step [200/600], Loss: 0.0001
Epoch [27/64], Step [300/600], Loss: 0.0003
Epoch [27/64], Step [400/600], Loss: 0.0003
Epoch [27/64], Step [500/600], Loss: 0.0001
Epoch [27/64], Step [600/600], Loss: 0.0002
Epoch [28/64], Step [100/600], Loss: 0.0001
Epoch [28/64], Step [200/600], Loss: 0.0001
Epoch [28/64], Step [300/600], Loss: 0.0013
Epoch [28/64], Step [400/600], Loss: 0.0002
Epoch [28/64], Step [500/600], Loss: 0.0004
Epoch [28/64], Step [600/600], Loss: 0.0002
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: 0.0001
Epoch [29/64], Step [400/600], Loss: 0.0002
Epoch [29/64], Step [500/600], Loss: 0.0004
Epoch [29/64], Step [600/600], Loss: 0.0028
Epoch [30/64], Step [100/600], Loss: 0.0001
Epoch [30/64], Step [200/600], Loss: 0.0857
Epoch [30/64], Step [300/600], Loss: 0.0001
Epoch [30/64], Step [400/600], Loss: 0.0027
Epoch [30/64], Step [500/600], Loss: 0.0210
Epoch [30/64], Step [600/600], Loss: 0.0300
Epoch [31/64], Step [100/600], Loss: 0.0007
Epoch [31/64], Step [200/600], Loss: 0.0026
Epoch [31/64], Step [300/600], Loss: 0.0042
Epoch [31/64], Step [400/600], Loss: 0.0003
Epoch [31/64], Step [500/600], Loss: 0.0021
Epoch [31/64], Step [600/600], Loss: 0.0005
Epoch [32/64], Step [100/600], Loss: 0.0011
Epoch [32/64], Step [200/600], Loss: 0.0002
Epoch [32/64], Step [300/600], Loss: 0.0013
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: 0.0005
Epoch [33/64], Step [100/600], Loss: 0.0000
Epoch [33/64], Step [200/600], Loss: 0.0002
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: 0.0000
Epoch [33/64], Step [600/600], Loss: 0.0002
Epoch [34/64], Step [100/600], Loss: 0.0002
Epoch [34/64], Step [200/600], Loss: 0.0000
Epoch [34/64], Step [300/600], Loss: 0.0003
Epoch [34/64], Step [400/600], Loss: 0.0003
Epoch [34/64], Step [500/600], Loss: 0.0002
Epoch [34/64], Step [600/600], Loss: 0.0002
Epoch [35/64], Step [100/600], Loss: 0.0000
Epoch [35/64], Step [200/600], Loss: 0.0003
Epoch [35/64], Step [300/600], Loss: 0.0004
Epoch [35/64], Step [400/600], Loss: 0.0002
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: 0.0002
Epoch [36/64], Step [200/600], Loss: 0.0000
Epoch [36/64], Step [300/600], Loss: 0.0001
Epoch [36/64], Step [400/600], Loss: 0.0002
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: 0.0001
Epoch [37/64], Step [200/600], Loss: 0.0001
Epoch [37/64], Step [300/600], Loss: 0.0003
Epoch [37/64], Step [400/600], Loss: 0.0001
Epoch [37/64], Step [500/600], Loss: 0.0000
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: 0.0000
Epoch [38/64], Step [300/600], Loss: 0.0001
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: 0.0000
Epoch [39/64], Step [100/600], Loss: 0.0001
Epoch [39/64], Step [200/600], Loss: 0.0001
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: 0.0001
Epoch [39/64], Step [600/600], Loss: 0.0014
Epoch [40/64], Step [100/600], Loss: 0.0003
Epoch [40/64], Step [200/600], Loss: 0.0127
Epoch [40/64], Step [300/600], Loss: 0.0006
Epoch [40/64], Step [400/600], Loss: 0.0021
Epoch [40/64], Step [500/600], Loss: 0.0027
Epoch [40/64], Step [600/600], Loss: 0.0027
Epoch [41/64], Step [100/600], Loss: 0.0000
Epoch [41/64], Step [200/600], Loss: 0.0004
Epoch [41/64], Step [300/600], Loss: 0.0016
Epoch [41/64], Step [400/600], Loss: 0.0006
Epoch [41/64], Step [500/600], Loss: 0.0007
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: 0.0002
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: 0.0000
Epoch [42/64], Step [600/600], Loss: 0.0001
Epoch [43/64], Step [100/600], Loss: 0.0001
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: 0.0003
Epoch [43/64], Step [500/600], Loss: 0.0001
Epoch [43/64], Step [600/600], Loss: 0.0002
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: 0.0001
Epoch [45/64], Step [100/600], Loss: 0.0002
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: 0.0003
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: 0.0001
Epoch [46/64], Step [200/600], Loss: 0.0000
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: 0.0002
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: 0.0001
Epoch [47/64], Step [300/600], Loss: 0.0001
Epoch [47/64], Step [400/600], Loss: 0.0001
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: 0.0000
Epoch [48/64], Step [400/600], Loss: 0.0000
Epoch [48/64], Step [500/600], Loss: 0.0000
Epoch [48/64], Step [600/600], Loss: 0.0021
Epoch [49/64], Step [100/600], Loss: 0.0732
Epoch [49/64], Step [200/600], Loss: 0.0007
Epoch [49/64], Step [300/600], Loss: 0.0000
Epoch [49/64], Step [400/600], Loss: 0.0002
Epoch [49/64], Step [500/600], Loss: 0.0000
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: 0.0007
Epoch [50/64], Step [300/600], Loss: 0.0001
Epoch [50/64], Step [400/600], Loss: 0.0000
Epoch [50/64], Step [500/600], Loss: 0.0001
Epoch [50/64], Step [600/600], Loss: 0.0002
Epoch [51/64], Step [100/600], Loss: 0.0005
Epoch [51/64], Step [200/600], Loss: 0.0000
Epoch [51/64], Step [300/600], Loss: 0.0007
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: 0.0001
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: 0.0002
Epoch [52/64], Step [500/600], Loss: 0.0009
Epoch [52/64], Step [600/600], Loss: 0.0002
Epoch [53/64], Step [100/600], Loss: 0.0003
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: 0.0002
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: 0.0000
Epoch [54/64], Step [600/600], Loss: 0.0000
Epoch [55/64], Step [100/600], Loss: 0.0000
Epoch [55/64], Step [200/600], Loss: 0.0003
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: 0.0000
Epoch [55/64], Step [600/600], Loss: 0.0000
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: 0.0000
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: 0.0001
Epoch [57/64], Step [100/600], Loss: 0.0001
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: 0.0001
Epoch [58/64], Step [200/600], Loss: 0.0001
Epoch [58/64], Step [300/600], Loss: 0.0000
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: 0.0000
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: 0.0000
Epoch [59/64], Step [500/600], Loss: 0.0000
Epoch [59/64], Step [600/600], Loss: 0.0001
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: 0.0045
Epoch [60/64], Step [600/600], Loss: 0.0016
Epoch [61/64], Step [100/600], Loss: 0.0000
Epoch [61/64], Step [200/600], Loss: 0.0015
Epoch [61/64], Step [300/600], Loss: 0.0000
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: 0.0001
Epoch [62/64], Step [100/600], Loss: 0.0001
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: 0.0003
Epoch [62/64], Step [500/600], Loss: 0.0006
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: 0.0003
Epoch [63/64], Step [300/600], Loss: 0.0011
Epoch [63/64], Step [400/600], Loss: 0.0000
Epoch [63/64], Step [500/600], Loss: 0.0028
Epoch [63/64], Step [600/600], Loss: 0.0001
Epoch [64/64], Step [100/600], Loss: 0.0002
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: 0.0001
Epoch [64/64], Step [600/600], Loss: 0.0001
Pytorch test completed in 375.333 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins5833732990333660354.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