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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 98853 queued and waiting for resources
srun: job 98853 has been allocated resources
Running benchmark on hydro03
Epoch [1/64], Step [100/600], Loss: 0.2350
Epoch [1/64], Step [200/600], Loss: 0.0758
Epoch [1/64], Step [300/600], Loss: 0.0667
Epoch [1/64], Step [400/600], Loss: 0.0548
Epoch [1/64], Step [500/600], Loss: 0.0341
Epoch [1/64], Step [600/600], Loss: 0.0607
Epoch [2/64], Step [100/600], Loss: 0.0685
Epoch [2/64], Step [200/600], Loss: 0.0392
Epoch [2/64], Step [300/600], Loss: 0.0402
Epoch [2/64], Step [400/600], Loss: 0.0683
Epoch [2/64], Step [500/600], Loss: 0.0258
Epoch [2/64], Step [600/600], Loss: 0.0143
Epoch [3/64], Step [100/600], Loss: 0.0255
Epoch [3/64], Step [200/600], Loss: 0.0371
Epoch [3/64], Step [300/600], Loss: 0.0903
Epoch [3/64], Step [400/600], Loss: 0.0206
Epoch [3/64], Step [500/600], Loss: 0.0171
Epoch [3/64], Step [600/600], Loss: 0.0745
Epoch [4/64], Step [100/600], Loss: 0.0576
Epoch [4/64], Step [200/600], Loss: 0.0068
Epoch [4/64], Step [300/600], Loss: 0.0448
Epoch [4/64], Step [400/600], Loss: 0.0932
Epoch [4/64], Step [500/600], Loss: 0.0482
Epoch [4/64], Step [600/600], Loss: 0.0172
Epoch [5/64], Step [100/600], Loss: 0.0204
Epoch [5/64], Step [200/600], Loss: 0.0581
Epoch [5/64], Step [300/600], Loss: 0.0298
Epoch [5/64], Step [400/600], Loss: 0.0388
Epoch [5/64], Step [500/600], Loss: 0.0121
Epoch [5/64], Step [600/600], Loss: 0.0305
Epoch [6/64], Step [100/600], Loss: 0.0533
Epoch [6/64], Step [200/600], Loss: 0.0865
Epoch [6/64], Step [300/600], Loss: 0.0178
Epoch [6/64], Step [400/600], Loss: 0.0890
Epoch [6/64], Step [500/600], Loss: 0.0576
Epoch [6/64], Step [600/600], Loss: 0.0212
Epoch [7/64], Step [100/600], Loss: 0.0050
Epoch [7/64], Step [200/600], Loss: 0.0090
Epoch [7/64], Step [300/600], Loss: 0.0013
Epoch [7/64], Step [400/600], Loss: 0.0722
Epoch [7/64], Step [500/600], Loss: 0.0628
Epoch [7/64], Step [600/600], Loss: 0.0428
Epoch [8/64], Step [100/600], Loss: 0.0084
Epoch [8/64], Step [200/600], Loss: 0.0350
Epoch [8/64], Step [300/600], Loss: 0.0074
Epoch [8/64], Step [400/600], Loss: 0.0101
Epoch [8/64], Step [500/600], Loss: 0.0081
Epoch [8/64], Step [600/600], Loss: 0.0696
Epoch [9/64], Step [100/600], Loss: 0.0162
Epoch [9/64], Step [200/600], Loss: 0.0320
Epoch [9/64], Step [300/600], Loss: 0.0222
Epoch [9/64], Step [400/600], Loss: 0.0032
Epoch [9/64], Step [500/600], Loss: 0.0141
Epoch [9/64], Step [600/600], Loss: 0.0212
Epoch [10/64], Step [100/600], Loss: 0.0044
Epoch [10/64], Step [200/600], Loss: 0.0174
Epoch [10/64], Step [300/600], Loss: 0.0093
Epoch [10/64], Step [400/600], Loss: 0.0092
Epoch [10/64], Step [500/600], Loss: 0.0168
Epoch [10/64], Step [600/600], Loss: 0.0095
Epoch [11/64], Step [100/600], Loss: 0.0109
Epoch [11/64], Step [200/600], Loss: 0.0085
Epoch [11/64], Step [300/600], Loss: 0.0083
Epoch [11/64], Step [400/600], Loss: 0.0051
Epoch [11/64], Step [500/600], Loss: 0.0340
Epoch [11/64], Step [600/600], Loss: 0.0161
Epoch [12/64], Step [100/600], Loss: 0.0010
Epoch [12/64], Step [200/600], Loss: 0.0060
Epoch [12/64], Step [300/600], Loss: 0.0455
Epoch [12/64], Step [400/600], Loss: 0.0015
Epoch [12/64], Step [500/600], Loss: 0.0133
Epoch [12/64], Step [600/600], Loss: 0.0034
Epoch [13/64], Step [100/600], Loss: 0.0064
Epoch [13/64], Step [200/600], Loss: 0.0289
Epoch [13/64], Step [300/600], Loss: 0.0053
Epoch [13/64], Step [400/600], Loss: 0.0032
Epoch [13/64], Step [500/600], Loss: 0.0091
Epoch [13/64], Step [600/600], Loss: 0.0013
Epoch [14/64], Step [100/600], Loss: 0.0091
Epoch [14/64], Step [200/600], Loss: 0.0042
Epoch [14/64], Step [300/600], Loss: 0.0435
Epoch [14/64], Step [400/600], Loss: 0.0134
Epoch [14/64], Step [500/600], Loss: 0.0095
Epoch [14/64], Step [600/600], Loss: 0.0112
Epoch [15/64], Step [100/600], Loss: 0.0013
Epoch [15/64], Step [200/600], Loss: 0.0009
Epoch [15/64], Step [300/600], Loss: 0.0096
Epoch [15/64], Step [400/600], Loss: 0.0321
Epoch [15/64], Step [500/600], Loss: 0.0046
Epoch [15/64], Step [600/600], Loss: 0.0050
Epoch [16/64], Step [100/600], Loss: 0.0006
Epoch [16/64], Step [200/600], Loss: 0.0137
Epoch [16/64], Step [300/600], Loss: 0.0023
Epoch [16/64], Step [400/600], Loss: 0.0025
Epoch [16/64], Step [500/600], Loss: 0.0049
Epoch [16/64], Step [600/600], Loss: 0.0070
Epoch [17/64], Step [100/600], Loss: 0.0007
Epoch [17/64], Step [200/600], Loss: 0.0005
Epoch [17/64], Step [300/600], Loss: 0.0122
Epoch [17/64], Step [400/600], Loss: 0.0098
Epoch [17/64], Step [500/600], Loss: 0.0047
Epoch [17/64], Step [600/600], Loss: 0.0234
Epoch [18/64], Step [100/600], Loss: 0.0004
Epoch [18/64], Step [200/600], Loss: 0.0051
Epoch [18/64], Step [300/600], Loss: 0.0006
Epoch [18/64], Step [400/600], Loss: 0.0023
Epoch [18/64], Step [500/600], Loss: 0.0004
Epoch [18/64], Step [600/600], Loss: 0.0041
Epoch [19/64], Step [100/600], Loss: 0.0006
Epoch [19/64], Step [200/600], Loss: 0.0234
Epoch [19/64], Step [300/600], Loss: 0.0007
Epoch [19/64], Step [400/600], Loss: 0.0012
Epoch [19/64], Step [500/600], Loss: 0.0077
Epoch [19/64], Step [600/600], Loss: 0.0023
Epoch [20/64], Step [100/600], Loss: 0.0014
Epoch [20/64], Step [200/600], Loss: 0.0008
Epoch [20/64], Step [300/600], Loss: 0.0009
Epoch [20/64], Step [400/600], Loss: 0.0049
Epoch [20/64], Step [500/600], Loss: 0.0016
Epoch [20/64], Step [600/600], Loss: 0.0276
Epoch [21/64], Step [100/600], Loss: 0.0021
Epoch [21/64], Step [200/600], Loss: 0.0021
Epoch [21/64], Step [300/600], Loss: 0.0031
Epoch [21/64], Step [400/600], Loss: 0.0017
Epoch [21/64], Step [500/600], Loss: 0.0008
Epoch [21/64], Step [600/600], Loss: 0.0055
Epoch [22/64], Step [100/600], Loss: 0.0012
Epoch [22/64], Step [200/600], Loss: 0.0019
Epoch [22/64], Step [300/600], Loss: 0.0002
Epoch [22/64], Step [400/600], Loss: 0.0009
Epoch [22/64], Step [500/600], Loss: 0.0087
Epoch [22/64], Step [600/600], Loss: 0.0026
Epoch [23/64], Step [100/600], Loss: 0.0006
Epoch [23/64], Step [200/600], Loss: 0.0202
Epoch [23/64], Step [300/600], Loss: 0.0024
Epoch [23/64], Step [400/600], Loss: 0.0057
Epoch [23/64], Step [500/600], Loss: 0.0009
Epoch [23/64], Step [600/600], Loss: 0.0103
Epoch [24/64], Step [100/600], Loss: 0.0004
Epoch [24/64], Step [200/600], Loss: 0.0004
Epoch [24/64], Step [300/600], Loss: 0.0027
Epoch [24/64], Step [400/600], Loss: 0.0015
Epoch [24/64], Step [500/600], Loss: 0.0234
Epoch [24/64], Step [600/600], Loss: 0.0023
Epoch [25/64], Step [100/600], Loss: 0.0048
Epoch [25/64], Step [200/600], Loss: 0.0023
Epoch [25/64], Step [300/600], Loss: 0.0295
Epoch [25/64], Step [400/600], Loss: 0.0004
Epoch [25/64], Step [500/600], Loss: 0.0012
Epoch [25/64], Step [600/600], Loss: 0.0019
Epoch [26/64], Step [100/600], Loss: 0.0003
Epoch [26/64], Step [200/600], Loss: 0.0002
Epoch [26/64], Step [300/600], Loss: 0.0004
Epoch [26/64], Step [400/600], Loss: 0.0003
Epoch [26/64], Step [500/600], Loss: 0.0002
Epoch [26/64], Step [600/600], Loss: 0.0008
Epoch [27/64], Step [100/600], Loss: 0.0002
Epoch [27/64], Step [200/600], Loss: 0.0012
Epoch [27/64], Step [300/600], Loss: 0.0006
Epoch [27/64], Step [400/600], Loss: 0.0004
Epoch [27/64], Step [500/600], Loss: 0.0002
Epoch [27/64], Step [600/600], Loss: 0.0004
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: 0.0004
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: 0.0002
Epoch [29/64], Step [100/600], Loss: 0.0000
Epoch [29/64], Step [200/600], Loss: 0.0009
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: 0.0007
Epoch [29/64], Step [600/600], Loss: 0.0003
Epoch [30/64], Step [100/600], Loss: 0.0304
Epoch [30/64], Step [200/600], Loss: 0.0205
Epoch [30/64], Step [300/600], Loss: 0.0262
Epoch [30/64], Step [400/600], Loss: 0.0004
Epoch [30/64], Step [500/600], Loss: 0.0003
Epoch [30/64], Step [600/600], Loss: 0.0121
Epoch [31/64], Step [100/600], Loss: 0.0011
Epoch [31/64], Step [200/600], Loss: 0.0002
Epoch [31/64], Step [300/600], Loss: 0.0000
Epoch [31/64], Step [400/600], Loss: 0.0001
Epoch [31/64], Step [500/600], Loss: 0.0060
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: 0.0002
Epoch [32/64], Step [300/600], Loss: 0.0010
Epoch [32/64], Step [400/600], Loss: 0.0005
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: 0.0009
Epoch [33/64], Step [400/600], Loss: 0.0000
Epoch [33/64], Step [500/600], Loss: 0.0001
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: 0.0001
Epoch [34/64], Step [300/600], Loss: 0.0001
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: 0.0001
Epoch [35/64], Step [100/600], Loss: 0.0005
Epoch [35/64], Step [200/600], Loss: 0.0000
Epoch [35/64], Step [300/600], Loss: 0.0003
Epoch [35/64], Step [400/600], Loss: 0.0001
Epoch [35/64], Step [500/600], Loss: 0.0001
Epoch [35/64], Step [600/600], Loss: 0.0004
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: 0.0000
Epoch [36/64], Step [500/600], Loss: 0.0000
Epoch [36/64], Step [600/600], Loss: 0.0001
Epoch [37/64], Step [100/600], Loss: 0.0000
Epoch [37/64], Step [200/600], Loss: 0.0000
Epoch [37/64], Step [300/600], Loss: 0.0002
Epoch [37/64], Step [400/600], Loss: 0.0000
Epoch [37/64], Step [500/600], Loss: 0.0001
Epoch [37/64], Step [600/600], Loss: 0.0002
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: 0.0002
Epoch [38/64], Step [500/600], Loss: 0.0002
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: 0.0039
Epoch [39/64], Step [400/600], Loss: 0.0016
Epoch [39/64], Step [500/600], Loss: 0.0050
Epoch [39/64], Step [600/600], Loss: 0.0019
Epoch [40/64], Step [100/600], Loss: 0.0513
Epoch [40/64], Step [200/600], Loss: 0.0001
Epoch [40/64], Step [300/600], Loss: 0.0002
Epoch [40/64], Step [400/600], Loss: 0.0002
Epoch [40/64], Step [500/600], Loss: 0.0002
Epoch [40/64], Step [600/600], Loss: 0.0028
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: 0.0003
Epoch [41/64], Step [500/600], Loss: 0.0001
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: 0.0008
Epoch [42/64], Step [400/600], Loss: 0.0003
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: 0.0000
Epoch [43/64], Step [200/600], Loss: 0.0001
Epoch [43/64], Step [300/600], Loss: 0.0002
Epoch [43/64], Step [400/600], Loss: 0.0002
Epoch [43/64], Step [500/600], Loss: 0.0002
Epoch [43/64], Step [600/600], Loss: 0.0003
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: 0.0000
Epoch [44/64], Step [600/600], Loss: 0.0002
Epoch [45/64], Step [100/600], Loss: 0.0001
Epoch [45/64], Step [200/600], Loss: 0.0001
Epoch [45/64], Step [300/600], Loss: 0.0002
Epoch [45/64], Step [400/600], Loss: 0.0001
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: 0.0001
Epoch [46/64], Step [300/600], Loss: 0.0001
Epoch [46/64], Step [400/600], Loss: 0.0002
Epoch [46/64], Step [500/600], Loss: 0.0001
Epoch [46/64], Step [600/600], Loss: 0.0000
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: 0.0000
Epoch [47/64], Step [400/600], Loss: 0.0000
Epoch [47/64], Step [500/600], Loss: 0.0001
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: 0.0000
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: 0.0001
Epoch [49/64], Step [100/600], Loss: 0.0001
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: 0.0192
Epoch [49/64], Step [600/600], Loss: 0.0033
Epoch [50/64], Step [100/600], Loss: 0.0327
Epoch [50/64], Step [200/600], Loss: 0.0003
Epoch [50/64], Step [300/600], Loss: 0.0036
Epoch [50/64], Step [400/600], Loss: 0.0017
Epoch [50/64], Step [500/600], Loss: 0.0003
Epoch [50/64], Step [600/600], Loss: 0.0059
Epoch [51/64], Step [100/600], Loss: 0.0012
Epoch [51/64], Step [200/600], Loss: 0.0003
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: 0.0002
Epoch [52/64], Step [100/600], Loss: 0.0001
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: 0.0000
Epoch [52/64], Step [500/600], Loss: 0.0000
Epoch [52/64], Step [600/600], Loss: 0.0001
Epoch [53/64], Step [100/600], Loss: 0.0001
Epoch [53/64], Step [200/600], Loss: 0.0000
Epoch [53/64], Step [300/600], Loss: 0.0001
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: 0.0003
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: 0.0001
Epoch [54/64], Step [500/600], Loss: 0.0000
Epoch [54/64], Step [600/600], Loss: 0.0001
Epoch [55/64], Step [100/600], Loss: 0.0000
Epoch [55/64], Step [200/600], Loss: 0.0001
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: 0.0001
Epoch [55/64], Step [600/600], Loss: 0.0002
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: 0.0000
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: 0.0000
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: 0.0000
Epoch [58/64], Step [400/600], Loss: 0.0002
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: 0.0001
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: 0.0000
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: 0.0000
Epoch [60/64], Step [500/600], Loss: 0.0001
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: 0.0000
Epoch [61/64], Step [500/600], Loss: 0.0001
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: 0.0000
Epoch [62/64], Step [300/600], Loss: 0.0001
Epoch [62/64], Step [400/600], Loss: 0.0429
Epoch [62/64], Step [500/600], Loss: 0.0032
Epoch [62/64], Step [600/600], Loss: 0.0044
Epoch [63/64], Step [100/600], Loss: 0.0003
Epoch [63/64], Step [200/600], Loss: 0.0005
Epoch [63/64], Step [300/600], Loss: 0.0006
Epoch [63/64], Step [400/600], Loss: 0.0155
Epoch [63/64], Step [500/600], Loss: 0.0022
Epoch [63/64], Step [600/600], Loss: 0.0019
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: 0.0001
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: 0.0000
Pytorch test completed in 378.886 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins8502000314740912625.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