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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 84448 queued and waiting for resources
srun: job 84448 has been allocated resources
Running benchmark on hydro01
Epoch [1/64], Step [100/600], Loss: 0.2446
Epoch [1/64], Step [200/600], Loss: 0.1303
Epoch [1/64], Step [300/600], Loss: 0.0897
Epoch [1/64], Step [400/600], Loss: 0.1285
Epoch [1/64], Step [500/600], Loss: 0.0364
Epoch [1/64], Step [600/600], Loss: 0.0681
Epoch [2/64], Step [100/600], Loss: 0.0548
Epoch [2/64], Step [200/600], Loss: 0.0257
Epoch [2/64], Step [300/600], Loss: 0.0211
Epoch [2/64], Step [400/600], Loss: 0.1142
Epoch [2/64], Step [500/600], Loss: 0.0528
Epoch [2/64], Step [600/600], Loss: 0.0077
Epoch [3/64], Step [100/600], Loss: 0.0330
Epoch [3/64], Step [200/600], Loss: 0.0604
Epoch [3/64], Step [300/600], Loss: 0.0440
Epoch [3/64], Step [400/600], Loss: 0.0323
Epoch [3/64], Step [500/600], Loss: 0.0169
Epoch [3/64], Step [600/600], Loss: 0.0409
Epoch [4/64], Step [100/600], Loss: 0.0111
Epoch [4/64], Step [200/600], Loss: 0.0225
Epoch [4/64], Step [300/600], Loss: 0.0303
Epoch [4/64], Step [400/600], Loss: 0.0859
Epoch [4/64], Step [500/600], Loss: 0.0412
Epoch [4/64], Step [600/600], Loss: 0.0273
Epoch [5/64], Step [100/600], Loss: 0.0888
Epoch [5/64], Step [200/600], Loss: 0.0636
Epoch [5/64], Step [300/600], Loss: 0.0518
Epoch [5/64], Step [400/600], Loss: 0.0755
Epoch [5/64], Step [500/600], Loss: 0.0118
Epoch [5/64], Step [600/600], Loss: 0.0584
Epoch [6/64], Step [100/600], Loss: 0.0536
Epoch [6/64], Step [200/600], Loss: 0.0644
Epoch [6/64], Step [300/600], Loss: 0.0242
Epoch [6/64], Step [400/600], Loss: 0.0743
Epoch [6/64], Step [500/600], Loss: 0.0349
Epoch [6/64], Step [600/600], Loss: 0.0119
Epoch [7/64], Step [100/600], Loss: 0.0213
Epoch [7/64], Step [200/600], Loss: 0.0057
Epoch [7/64], Step [300/600], Loss: 0.0036
Epoch [7/64], Step [400/600], Loss: 0.0021
Epoch [7/64], Step [500/600], Loss: 0.0046
Epoch [7/64], Step [600/600], Loss: 0.0105
Epoch [8/64], Step [100/600], Loss: 0.0113
Epoch [8/64], Step [200/600], Loss: 0.0136
Epoch [8/64], Step [300/600], Loss: 0.0464
Epoch [8/64], Step [400/600], Loss: 0.0506
Epoch [8/64], Step [500/600], Loss: 0.0321
Epoch [8/64], Step [600/600], Loss: 0.0756
Epoch [9/64], Step [100/600], Loss: 0.0069
Epoch [9/64], Step [200/600], Loss: 0.0067
Epoch [9/64], Step [300/600], Loss: 0.0332
Epoch [9/64], Step [400/600], Loss: 0.0083
Epoch [9/64], Step [500/600], Loss: 0.0056
Epoch [9/64], Step [600/600], Loss: 0.0021
Epoch [10/64], Step [100/600], Loss: 0.0173
Epoch [10/64], Step [200/600], Loss: 0.0039
Epoch [10/64], Step [300/600], Loss: 0.0051
Epoch [10/64], Step [400/600], Loss: 0.0067
Epoch [10/64], Step [500/600], Loss: 0.0022
Epoch [10/64], Step [600/600], Loss: 0.0352
Epoch [11/64], Step [100/600], Loss: 0.0037
Epoch [11/64], Step [200/600], Loss: 0.0035
Epoch [11/64], Step [300/600], Loss: 0.0159
Epoch [11/64], Step [400/600], Loss: 0.0085
Epoch [11/64], Step [500/600], Loss: 0.0037
Epoch [11/64], Step [600/600], Loss: 0.0188
Epoch [12/64], Step [100/600], Loss: 0.0060
Epoch [12/64], Step [200/600], Loss: 0.0249
Epoch [12/64], Step [300/600], Loss: 0.0047
Epoch [12/64], Step [400/600], Loss: 0.0070
Epoch [12/64], Step [500/600], Loss: 0.0358
Epoch [12/64], Step [600/600], Loss: 0.0063
Epoch [13/64], Step [100/600], Loss: 0.0018
Epoch [13/64], Step [200/600], Loss: 0.0050
Epoch [13/64], Step [300/600], Loss: 0.0010
Epoch [13/64], Step [400/600], Loss: 0.0006
Epoch [13/64], Step [500/600], Loss: 0.0354
Epoch [13/64], Step [600/600], Loss: 0.0007
Epoch [14/64], Step [100/600], Loss: 0.0208
Epoch [14/64], Step [200/600], Loss: 0.0252
Epoch [14/64], Step [300/600], Loss: 0.0015
Epoch [14/64], Step [400/600], Loss: 0.0020
Epoch [14/64], Step [500/600], Loss: 0.0016
Epoch [14/64], Step [600/600], Loss: 0.0148
Epoch [15/64], Step [100/600], Loss: 0.0013
Epoch [15/64], Step [200/600], Loss: 0.0044
Epoch [15/64], Step [300/600], Loss: 0.0006
Epoch [15/64], Step [400/600], Loss: 0.0002
Epoch [15/64], Step [500/600], Loss: 0.0051
Epoch [15/64], Step [600/600], Loss: 0.0258
Epoch [16/64], Step [100/600], Loss: 0.0097
Epoch [16/64], Step [200/600], Loss: 0.0014
Epoch [16/64], Step [300/600], Loss: 0.0020
Epoch [16/64], Step [400/600], Loss: 0.0019
Epoch [16/64], Step [500/600], Loss: 0.0061
Epoch [16/64], Step [600/600], Loss: 0.0018
Epoch [17/64], Step [100/600], Loss: 0.0035
Epoch [17/64], Step [200/600], Loss: 0.0009
Epoch [17/64], Step [300/600], Loss: 0.0107
Epoch [17/64], Step [400/600], Loss: 0.0094
Epoch [17/64], Step [500/600], Loss: 0.0035
Epoch [17/64], Step [600/600], Loss: 0.0008
Epoch [18/64], Step [100/600], Loss: 0.0011
Epoch [18/64], Step [200/600], Loss: 0.0051
Epoch [18/64], Step [300/600], Loss: 0.0007
Epoch [18/64], Step [400/600], Loss: 0.0009
Epoch [18/64], Step [500/600], Loss: 0.0019
Epoch [18/64], Step [600/600], Loss: 0.0197
Epoch [19/64], Step [100/600], Loss: 0.0004
Epoch [19/64], Step [200/600], Loss: 0.0033
Epoch [19/64], Step [300/600], Loss: 0.0016
Epoch [19/64], Step [400/600], Loss: 0.0006
Epoch [19/64], Step [500/600], Loss: 0.0012
Epoch [19/64], Step [600/600], Loss: 0.0032
Epoch [20/64], Step [100/600], Loss: 0.0037
Epoch [20/64], Step [200/600], Loss: 0.0005
Epoch [20/64], Step [300/600], Loss: 0.0018
Epoch [20/64], Step [400/600], Loss: 0.0049
Epoch [20/64], Step [500/600], Loss: 0.0021
Epoch [20/64], Step [600/600], Loss: 0.0003
Epoch [21/64], Step [100/600], Loss: 0.0006
Epoch [21/64], Step [200/600], Loss: 0.0004
Epoch [21/64], Step [300/600], Loss: 0.0079
Epoch [21/64], Step [400/600], Loss: 0.0047
Epoch [21/64], Step [500/600], Loss: 0.0009
Epoch [21/64], Step [600/600], Loss: 0.0020
Epoch [22/64], Step [100/600], Loss: 0.0013
Epoch [22/64], Step [200/600], Loss: 0.0191
Epoch [22/64], Step [300/600], Loss: 0.0013
Epoch [22/64], Step [400/600], Loss: 0.0020
Epoch [22/64], Step [500/600], Loss: 0.0013
Epoch [22/64], Step [600/600], Loss: 0.0003
Epoch [23/64], Step [100/600], Loss: 0.0018
Epoch [23/64], Step [200/600], Loss: 0.0007
Epoch [23/64], Step [300/600], Loss: 0.0006
Epoch [23/64], Step [400/600], Loss: 0.0006
Epoch [23/64], Step [500/600], Loss: 0.0001
Epoch [23/64], Step [600/600], Loss: 0.0009
Epoch [24/64], Step [100/600], Loss: 0.0007
Epoch [24/64], Step [200/600], Loss: 0.0002
Epoch [24/64], Step [300/600], Loss: 0.0001
Epoch [24/64], Step [400/600], Loss: 0.0002
Epoch [24/64], Step [500/600], Loss: 0.0003
Epoch [24/64], Step [600/600], Loss: 0.0003
Epoch [25/64], Step [100/600], Loss: 0.0346
Epoch [25/64], Step [200/600], Loss: 0.0071
Epoch [25/64], Step [300/600], Loss: 0.0384
Epoch [25/64], Step [400/600], Loss: 0.0025
Epoch [25/64], Step [500/600], Loss: 0.0017
Epoch [25/64], Step [600/600], Loss: 0.0038
Epoch [26/64], Step [100/600], Loss: 0.0005
Epoch [26/64], Step [200/600], Loss: 0.0006
Epoch [26/64], Step [300/600], Loss: 0.0003
Epoch [26/64], Step [400/600], Loss: 0.0006
Epoch [26/64], Step [500/600], Loss: 0.0017
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: 0.0013
Epoch [27/64], Step [300/600], Loss: 0.0001
Epoch [27/64], Step [400/600], Loss: 0.0007
Epoch [27/64], Step [500/600], Loss: 0.0000
Epoch [27/64], Step [600/600], Loss: 0.0005
Epoch [28/64], Step [100/600], Loss: 0.0002
Epoch [28/64], Step [200/600], Loss: 0.0004
Epoch [28/64], Step [300/600], Loss: 0.0008
Epoch [28/64], Step [400/600], Loss: 0.0002
Epoch [28/64], Step [500/600], Loss: 0.0006
Epoch [28/64], Step [600/600], Loss: 0.0000
Epoch [29/64], Step [100/600], Loss: 0.0005
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: 0.0003
Epoch [29/64], Step [500/600], Loss: 0.0001
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: 0.0293
Epoch [30/64], Step [300/600], Loss: 0.0031
Epoch [30/64], Step [400/600], Loss: 0.0454
Epoch [30/64], Step [500/600], Loss: 0.0002
Epoch [30/64], Step [600/600], Loss: 0.0003
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: 0.0001
Epoch [31/64], Step [400/600], Loss: 0.0006
Epoch [31/64], Step [500/600], Loss: 0.0048
Epoch [31/64], Step [600/600], Loss: 0.0071
Epoch [32/64], Step [100/600], Loss: 0.0014
Epoch [32/64], Step [200/600], Loss: 0.0004
Epoch [32/64], Step [300/600], Loss: 0.0016
Epoch [32/64], Step [400/600], Loss: 0.0024
Epoch [32/64], Step [500/600], Loss: 0.0001
Epoch [32/64], Step [600/600], Loss: 0.0005
Epoch [33/64], Step [100/600], Loss: 0.0001
Epoch [33/64], Step [200/600], Loss: 0.0000
Epoch [33/64], Step [300/600], Loss: 0.0003
Epoch [33/64], Step [400/600], Loss: 0.0022
Epoch [33/64], Step [500/600], Loss: 0.0006
Epoch [33/64], Step [600/600], Loss: 0.0028
Epoch [34/64], Step [100/600], Loss: 0.0004
Epoch [34/64], Step [200/600], Loss: 0.0002
Epoch [34/64], Step [300/600], Loss: 0.0011
Epoch [34/64], Step [400/600], Loss: 0.0000
Epoch [34/64], Step [500/600], Loss: 0.0035
Epoch [34/64], Step [600/600], Loss: 0.0096
Epoch [35/64], Step [100/600], Loss: 0.0002
Epoch [35/64], Step [200/600], Loss: 0.0153
Epoch [35/64], Step [300/600], Loss: 0.0014
Epoch [35/64], Step [400/600], Loss: 0.0019
Epoch [35/64], Step [500/600], Loss: 0.0001
Epoch [35/64], Step [600/600], Loss: 0.0003
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: 0.0002
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: 0.0032
Epoch [37/64], Step [100/600], Loss: 0.0003
Epoch [37/64], Step [200/600], Loss: 0.0004
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: 0.0001
Epoch [37/64], Step [600/600], Loss: 0.0001
Epoch [38/64], Step [100/600], Loss: 0.0001
Epoch [38/64], Step [200/600], Loss: 0.0001
Epoch [38/64], Step [300/600], Loss: 0.0000
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: 0.0000
Epoch [39/64], Step [100/600], Loss: 0.0001
Epoch [39/64], Step [200/600], Loss: 0.0000
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: 0.0001
Epoch [40/64], Step [100/600], Loss: 0.0000
Epoch [40/64], Step [200/600], Loss: 0.0000
Epoch [40/64], Step [300/600], Loss: 0.0001
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: 0.0003
Epoch [41/64], Step [100/600], Loss: 0.0001
Epoch [41/64], Step [200/600], Loss: 0.0085
Epoch [41/64], Step [300/600], Loss: 0.0110
Epoch [41/64], Step [400/600], Loss: 0.0162
Epoch [41/64], Step [500/600], Loss: 0.0010
Epoch [41/64], Step [600/600], Loss: 0.0002
Epoch [42/64], Step [100/600], Loss: 0.0000
Epoch [42/64], Step [200/600], Loss: 0.0003
Epoch [42/64], Step [300/600], Loss: 0.0000
Epoch [42/64], Step [400/600], Loss: 0.0066
Epoch [42/64], Step [500/600], Loss: 0.0023
Epoch [42/64], Step [600/600], Loss: 0.0003
Epoch [43/64], Step [100/600], Loss: 0.0002
Epoch [43/64], Step [200/600], Loss: 0.0000
Epoch [43/64], Step [300/600], Loss: 0.0003
Epoch [43/64], Step [400/600], Loss: 0.0022
Epoch [43/64], Step [500/600], Loss: 0.0000
Epoch [43/64], Step [600/600], Loss: 0.0001
Epoch [44/64], Step [100/600], Loss: 0.0002
Epoch [44/64], Step [200/600], Loss: 0.0006
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: 0.0002
Epoch [44/64], Step [600/600], Loss: 0.0003
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: 0.0003
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: 0.0000
Epoch [46/64], Step [100/600], Loss: 0.0002
Epoch [46/64], Step [200/600], Loss: 0.0002
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: 0.0002
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: 0.0000
Epoch [47/64], Step [300/600], Loss: 0.0001
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: 0.0000
Epoch [48/64], Step [100/600], Loss: 0.0001
Epoch [48/64], Step [200/600], Loss: 0.0002
Epoch [48/64], Step [300/600], Loss: 0.0000
Epoch [48/64], Step [400/600], Loss: 0.0001
Epoch [48/64], Step [500/600], Loss: 0.0000
Epoch [48/64], Step [600/600], Loss: 0.0000
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: 0.0048
Epoch [49/64], Step [600/600], Loss: 0.0055
Epoch [50/64], Step [100/600], Loss: 0.0030
Epoch [50/64], Step [200/600], Loss: 0.0081
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: 0.0003
Epoch [50/64], Step [600/600], Loss: 0.0001
Epoch [51/64], Step [100/600], Loss: 0.0000
Epoch [51/64], Step [200/600], Loss: 0.0004
Epoch [51/64], Step [300/600], Loss: 0.0004
Epoch [51/64], Step [400/600], Loss: 0.0004
Epoch [51/64], Step [500/600], Loss: 0.0000
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: 0.0002
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: 0.0001
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: 0.0001
Epoch [53/64], Step [400/600], Loss: 0.0000
Epoch [53/64], Step [500/600], Loss: 0.0003
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: 0.0000
Epoch [54/64], Step [300/600], Loss: 0.0001
Epoch [54/64], Step [400/600], Loss: 0.0003
Epoch [54/64], Step [500/600], Loss: 0.0000
Epoch [54/64], Step [600/600], Loss: 0.0003
Epoch [55/64], Step [100/600], Loss: 0.0000
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: 0.0001
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: 0.0000
Epoch [56/64], Step [300/600], Loss: 0.0000
Epoch [56/64], Step [400/600], Loss: 0.0000
Epoch [56/64], Step [500/600], Loss: 0.0001
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: 0.0001
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: 0.0000
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: 0.0000
Epoch [60/64], Step [600/600], Loss: 0.0402
Epoch [61/64], Step [100/600], Loss: 0.0000
Epoch [61/64], Step [200/600], Loss: 0.0202
Epoch [61/64], Step [300/600], Loss: 0.0004
Epoch [61/64], Step [400/600], Loss: 0.0016
Epoch [61/64], Step [500/600], Loss: 0.0002
Epoch [61/64], Step [600/600], Loss: 0.0007
Epoch [62/64], Step [100/600], Loss: 0.0002
Epoch [62/64], Step [200/600], Loss: 0.0002
Epoch [62/64], Step [300/600], Loss: 0.0000
Epoch [62/64], Step [400/600], Loss: 0.0001
Epoch [62/64], Step [500/600], Loss: 0.0001
Epoch [62/64], Step [600/600], Loss: 0.0003
Epoch [63/64], Step [100/600], Loss: 0.0000
Epoch [63/64], Step [200/600], Loss: 0.0002
Epoch [63/64], Step [300/600], Loss: 0.0000
Epoch [63/64], Step [400/600], Loss: 0.0000
Epoch [63/64], Step [500/600], Loss: 0.0000
Epoch [63/64], Step [600/600], Loss: 0.0003
Epoch [64/64], Step [100/600], Loss: 0.0000
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: 0.0002
Epoch [64/64], Step [500/600], Loss: 0.0001
Epoch [64/64], Step [600/600], Loss: 0.0001
Pytorch test completed in 437.022 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins3178075954749696526.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