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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 96304 queued and waiting for resources
srun: job 96304 has been allocated resources
Running benchmark on hydro03
Epoch [1/64], Step [100/600], Loss: 0.1605
Epoch [1/64], Step [200/600], Loss: 0.1440
Epoch [1/64], Step [300/600], Loss: 0.0544
Epoch [1/64], Step [400/600], Loss: 0.0616
Epoch [1/64], Step [500/600], Loss: 0.0980
Epoch [1/64], Step [600/600], Loss: 0.0458
Epoch [2/64], Step [100/600], Loss: 0.0860
Epoch [2/64], Step [200/600], Loss: 0.0153
Epoch [2/64], Step [300/600], Loss: 0.0519
Epoch [2/64], Step [400/600], Loss: 0.0413
Epoch [2/64], Step [500/600], Loss: 0.0539
Epoch [2/64], Step [600/600], Loss: 0.0141
Epoch [3/64], Step [100/600], Loss: 0.0595
Epoch [3/64], Step [200/600], Loss: 0.0399
Epoch [3/64], Step [300/600], Loss: 0.0511
Epoch [3/64], Step [400/600], Loss: 0.0324
Epoch [3/64], Step [500/600], Loss: 0.0172
Epoch [3/64], Step [600/600], Loss: 0.0091
Epoch [4/64], Step [100/600], Loss: 0.0236
Epoch [4/64], Step [200/600], Loss: 0.0125
Epoch [4/64], Step [300/600], Loss: 0.0138
Epoch [4/64], Step [400/600], Loss: 0.0113
Epoch [4/64], Step [500/600], Loss: 0.0247
Epoch [4/64], Step [600/600], Loss: 0.0222
Epoch [5/64], Step [100/600], Loss: 0.0148
Epoch [5/64], Step [200/600], Loss: 0.0166
Epoch [5/64], Step [300/600], Loss: 0.0024
Epoch [5/64], Step [400/600], Loss: 0.0433
Epoch [5/64], Step [500/600], Loss: 0.0147
Epoch [5/64], Step [600/600], Loss: 0.0069
Epoch [6/64], Step [100/600], Loss: 0.0523
Epoch [6/64], Step [200/600], Loss: 0.0332
Epoch [6/64], Step [300/600], Loss: 0.0077
Epoch [6/64], Step [400/600], Loss: 0.0300
Epoch [6/64], Step [500/600], Loss: 0.0694
Epoch [6/64], Step [600/600], Loss: 0.0092
Epoch [7/64], Step [100/600], Loss: 0.0041
Epoch [7/64], Step [200/600], Loss: 0.0166
Epoch [7/64], Step [300/600], Loss: 0.0127
Epoch [7/64], Step [400/600], Loss: 0.0130
Epoch [7/64], Step [500/600], Loss: 0.0049
Epoch [7/64], Step [600/600], Loss: 0.0053
Epoch [8/64], Step [100/600], Loss: 0.0064
Epoch [8/64], Step [200/600], Loss: 0.0201
Epoch [8/64], Step [300/600], Loss: 0.0453
Epoch [8/64], Step [400/600], Loss: 0.0257
Epoch [8/64], Step [500/600], Loss: 0.0045
Epoch [8/64], Step [600/600], Loss: 0.0177
Epoch [9/64], Step [100/600], Loss: 0.0221
Epoch [9/64], Step [200/600], Loss: 0.0331
Epoch [9/64], Step [300/600], Loss: 0.0044
Epoch [9/64], Step [400/600], Loss: 0.0016
Epoch [9/64], Step [500/600], Loss: 0.0252
Epoch [9/64], Step [600/600], Loss: 0.0465
Epoch [10/64], Step [100/600], Loss: 0.0014
Epoch [10/64], Step [200/600], Loss: 0.0050
Epoch [10/64], Step [300/600], Loss: 0.0016
Epoch [10/64], Step [400/600], Loss: 0.0225
Epoch [10/64], Step [500/600], Loss: 0.0040
Epoch [10/64], Step [600/600], Loss: 0.0956
Epoch [11/64], Step [100/600], Loss: 0.0018
Epoch [11/64], Step [200/600], Loss: 0.0050
Epoch [11/64], Step [300/600], Loss: 0.0100
Epoch [11/64], Step [400/600], Loss: 0.0099
Epoch [11/64], Step [500/600], Loss: 0.0061
Epoch [11/64], Step [600/600], Loss: 0.0332
Epoch [12/64], Step [100/600], Loss: 0.0106
Epoch [12/64], Step [200/600], Loss: 0.0018
Epoch [12/64], Step [300/600], Loss: 0.0060
Epoch [12/64], Step [400/600], Loss: 0.0135
Epoch [12/64], Step [500/600], Loss: 0.0100
Epoch [12/64], Step [600/600], Loss: 0.0170
Epoch [13/64], Step [100/600], Loss: 0.0039
Epoch [13/64], Step [200/600], Loss: 0.0153
Epoch [13/64], Step [300/600], Loss: 0.0212
Epoch [13/64], Step [400/600], Loss: 0.0008
Epoch [13/64], Step [500/600], Loss: 0.0002
Epoch [13/64], Step [600/600], Loss: 0.0008
Epoch [14/64], Step [100/600], Loss: 0.0032
Epoch [14/64], Step [200/600], Loss: 0.0014
Epoch [14/64], Step [300/600], Loss: 0.0024
Epoch [14/64], Step [400/600], Loss: 0.0118
Epoch [14/64], Step [500/600], Loss: 0.0064
Epoch [14/64], Step [600/600], Loss: 0.0032
Epoch [15/64], Step [100/600], Loss: 0.0120
Epoch [15/64], Step [200/600], Loss: 0.0016
Epoch [15/64], Step [300/600], Loss: 0.0030
Epoch [15/64], Step [400/600], Loss: 0.0098
Epoch [15/64], Step [500/600], Loss: 0.0022
Epoch [15/64], Step [600/600], Loss: 0.0012
Epoch [16/64], Step [100/600], Loss: 0.0029
Epoch [16/64], Step [200/600], Loss: 0.0010
Epoch [16/64], Step [300/600], Loss: 0.0055
Epoch [16/64], Step [400/600], Loss: 0.0007
Epoch [16/64], Step [500/600], Loss: 0.0082
Epoch [16/64], Step [600/600], Loss: 0.0006
Epoch [17/64], Step [100/600], Loss: 0.0006
Epoch [17/64], Step [200/600], Loss: 0.0018
Epoch [17/64], Step [300/600], Loss: 0.0014
Epoch [17/64], Step [400/600], Loss: 0.0018
Epoch [17/64], Step [500/600], Loss: 0.0020
Epoch [17/64], Step [600/600], Loss: 0.0004
Epoch [18/64], Step [100/600], Loss: 0.0063
Epoch [18/64], Step [200/600], Loss: 0.0078
Epoch [18/64], Step [300/600], Loss: 0.0113
Epoch [18/64], Step [400/600], Loss: 0.0028
Epoch [18/64], Step [500/600], Loss: 0.0141
Epoch [18/64], Step [600/600], Loss: 0.0102
Epoch [19/64], Step [100/600], Loss: 0.0041
Epoch [19/64], Step [200/600], Loss: 0.0014
Epoch [19/64], Step [300/600], Loss: 0.0057
Epoch [19/64], Step [400/600], Loss: 0.0005
Epoch [19/64], Step [500/600], Loss: 0.0001
Epoch [19/64], Step [600/600], Loss: 0.0131
Epoch [20/64], Step [100/600], Loss: 0.0019
Epoch [20/64], Step [200/600], Loss: 0.0015
Epoch [20/64], Step [300/600], Loss: 0.0030
Epoch [20/64], Step [400/600], Loss: 0.0187
Epoch [20/64], Step [500/600], Loss: 0.0122
Epoch [20/64], Step [600/600], Loss: 0.0164
Epoch [21/64], Step [100/600], Loss: 0.0001
Epoch [21/64], Step [200/600], Loss: 0.0160
Epoch [21/64], Step [300/600], Loss: 0.0014
Epoch [21/64], Step [400/600], Loss: 0.0074
Epoch [21/64], Step [500/600], Loss: 0.0011
Epoch [21/64], Step [600/600], Loss: 0.0001
Epoch [22/64], Step [100/600], Loss: 0.0009
Epoch [22/64], Step [200/600], Loss: 0.0084
Epoch [22/64], Step [300/600], Loss: 0.0001
Epoch [22/64], Step [400/600], Loss: 0.0003
Epoch [22/64], Step [500/600], Loss: 0.0007
Epoch [22/64], Step [600/600], Loss: 0.0007
Epoch [23/64], Step [100/600], Loss: 0.0045
Epoch [23/64], Step [200/600], Loss: 0.0021
Epoch [23/64], Step [300/600], Loss: 0.0004
Epoch [23/64], Step [400/600], Loss: 0.0014
Epoch [23/64], Step [500/600], Loss: 0.0018
Epoch [23/64], Step [600/600], Loss: 0.0008
Epoch [24/64], Step [100/600], Loss: 0.0006
Epoch [24/64], Step [200/600], Loss: 0.0002
Epoch [24/64], Step [300/600], Loss: 0.0125
Epoch [24/64], Step [400/600], Loss: 0.0001
Epoch [24/64], Step [500/600], Loss: 0.0010
Epoch [24/64], Step [600/600], Loss: 0.0080
Epoch [25/64], Step [100/600], Loss: 0.0024
Epoch [25/64], Step [200/600], Loss: 0.0012
Epoch [25/64], Step [300/600], Loss: 0.0025
Epoch [25/64], Step [400/600], Loss: 0.0015
Epoch [25/64], Step [500/600], Loss: 0.0003
Epoch [25/64], Step [600/600], Loss: 0.0015
Epoch [26/64], Step [100/600], Loss: 0.0002
Epoch [26/64], Step [200/600], Loss: 0.0005
Epoch [26/64], Step [300/600], Loss: 0.0097
Epoch [26/64], Step [400/600], Loss: 0.0037
Epoch [26/64], Step [500/600], Loss: 0.0000
Epoch [26/64], Step [600/600], Loss: 0.0019
Epoch [27/64], Step [100/600], Loss: 0.0018
Epoch [27/64], Step [200/600], Loss: 0.0001
Epoch [27/64], Step [300/600], Loss: 0.0011
Epoch [27/64], Step [400/600], Loss: 0.0117
Epoch [27/64], Step [500/600], Loss: 0.0124
Epoch [27/64], Step [600/600], Loss: 0.0005
Epoch [28/64], Step [100/600], Loss: 0.0027
Epoch [28/64], Step [200/600], Loss: 0.0003
Epoch [28/64], Step [300/600], Loss: 0.0001
Epoch [28/64], Step [400/600], Loss: 0.0021
Epoch [28/64], Step [500/600], Loss: 0.0003
Epoch [28/64], Step [600/600], Loss: 0.0063
Epoch [29/64], Step [100/600], Loss: 0.0001
Epoch [29/64], Step [200/600], Loss: 0.0003
Epoch [29/64], Step [300/600], Loss: 0.0003
Epoch [29/64], Step [400/600], Loss: 0.0000
Epoch [29/64], Step [500/600], Loss: 0.0001
Epoch [29/64], Step [600/600], Loss: 0.0008
Epoch [30/64], Step [100/600], Loss: 0.0007
Epoch [30/64], Step [200/600], Loss: 0.0000
Epoch [30/64], Step [300/600], Loss: 0.0002
Epoch [30/64], Step [400/600], Loss: 0.0002
Epoch [30/64], Step [500/600], Loss: 0.0001
Epoch [30/64], Step [600/600], Loss: 0.0001
Epoch [31/64], Step [100/600], Loss: 0.0000
Epoch [31/64], Step [200/600], Loss: 0.0005
Epoch [31/64], Step [300/600], Loss: 0.0001
Epoch [31/64], Step [400/600], Loss: 0.0002
Epoch [31/64], Step [500/600], Loss: 0.0001
Epoch [31/64], Step [600/600], Loss: 0.0000
Epoch [32/64], Step [100/600], Loss: 0.0001
Epoch [32/64], Step [200/600], Loss: 0.0001
Epoch [32/64], Step [300/600], Loss: 0.0004
Epoch [32/64], Step [400/600], Loss: 0.0000
Epoch [32/64], Step [500/600], Loss: 0.0001
Epoch [32/64], Step [600/600], Loss: 0.0620
Epoch [33/64], Step [100/600], Loss: 0.0444
Epoch [33/64], Step [200/600], Loss: 0.0005
Epoch [33/64], Step [300/600], Loss: 0.0112
Epoch [33/64], Step [400/600], Loss: 0.0002
Epoch [33/64], Step [500/600], Loss: 0.0003
Epoch [33/64], Step [600/600], Loss: 0.0002
Epoch [34/64], Step [100/600], Loss: 0.0001
Epoch [34/64], Step [200/600], Loss: 0.0002
Epoch [34/64], Step [300/600], Loss: 0.0001
Epoch [34/64], Step [400/600], Loss: 0.0020
Epoch [34/64], Step [500/600], Loss: 0.0049
Epoch [34/64], Step [600/600], Loss: 0.0000
Epoch [35/64], Step [100/600], Loss: 0.0003
Epoch [35/64], Step [200/600], Loss: 0.0012
Epoch [35/64], Step [300/600], Loss: 0.0000
Epoch [35/64], Step [400/600], Loss: 0.0002
Epoch [35/64], Step [500/600], Loss: 0.0003
Epoch [35/64], Step [600/600], Loss: 0.0001
Epoch [36/64], Step [100/600], Loss: 0.0002
Epoch [36/64], Step [200/600], Loss: 0.0001
Epoch [36/64], Step [300/600], Loss: 0.0001
Epoch [36/64], Step [400/600], Loss: 0.0001
Epoch [36/64], Step [500/600], Loss: 0.0000
Epoch [36/64], Step [600/600], Loss: 0.0000
Epoch [37/64], Step [100/600], Loss: 0.0002
Epoch [37/64], Step [200/600], Loss: 0.0002
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: 0.0000
Epoch [37/64], Step [600/600], Loss: 0.0000
Epoch [38/64], Step [100/600], Loss: 0.0001
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: 0.0008
Epoch [38/64], Step [500/600], Loss: 0.0001
Epoch [38/64], Step [600/600], Loss: 0.0003
Epoch [39/64], Step [100/600], Loss: 0.0000
Epoch [39/64], Step [200/600], Loss: 0.0002
Epoch [39/64], Step [300/600], Loss: 0.0001
Epoch [39/64], Step [400/600], Loss: 0.0001
Epoch [39/64], Step [500/600], Loss: 0.0001
Epoch [39/64], Step [600/600], Loss: 0.0000
Epoch [40/64], Step [100/600], Loss: 0.0001
Epoch [40/64], Step [200/600], Loss: 0.0001
Epoch [40/64], Step [300/600], Loss: 0.0000
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: 0.0002
Epoch [41/64], Step [100/600], Loss: 0.0005
Epoch [41/64], Step [200/600], Loss: 0.0013
Epoch [41/64], Step [300/600], Loss: 0.0019
Epoch [41/64], Step [400/600], Loss: 0.0053
Epoch [41/64], Step [500/600], Loss: 0.0000
Epoch [41/64], Step [600/600], Loss: 0.0003
Epoch [42/64], Step [100/600], Loss: 0.0007
Epoch [42/64], Step [200/600], Loss: 0.0003
Epoch [42/64], Step [300/600], Loss: 0.0002
Epoch [42/64], Step [400/600], Loss: 0.0003
Epoch [42/64], Step [500/600], Loss: 0.0001
Epoch [42/64], Step [600/600], Loss: 0.0001
Epoch [43/64], Step [100/600], Loss: 0.0002
Epoch [43/64], Step [200/600], Loss: 0.0001
Epoch [43/64], Step [300/600], Loss: 0.0001
Epoch [43/64], Step [400/600], Loss: 0.0002
Epoch [43/64], Step [500/600], Loss: 0.0001
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: 0.0001
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: 0.0001
Epoch [44/64], Step [600/600], Loss: 0.0000
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: 0.0000
Epoch [45/64], Step [400/600], Loss: 0.0000
Epoch [45/64], Step [500/600], Loss: 0.0001
Epoch [45/64], Step [600/600], Loss: 0.0000
Epoch [46/64], Step [100/600], Loss: 0.0000
Epoch [46/64], Step [200/600], Loss: 0.0000
Epoch [46/64], Step [300/600], Loss: 0.0002
Epoch [46/64], Step [400/600], Loss: 0.0001
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: 0.0000
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: 0.0001
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: 0.0001
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: 0.0001
Epoch [49/64], Step [500/600], Loss: 0.0001
Epoch [49/64], Step [600/600], Loss: 0.0002
Epoch [50/64], Step [100/600], Loss: 0.0000
Epoch [50/64], Step [200/600], Loss: 0.0001
Epoch [50/64], Step [300/600], Loss: 0.0001
Epoch [50/64], Step [400/600], Loss: 0.0396
Epoch [50/64], Step [500/600], Loss: 0.0090
Epoch [50/64], Step [600/600], Loss: 0.0003
Epoch [51/64], Step [100/600], Loss: 0.0003
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: 0.0000
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: 0.0030
Epoch [52/64], Step [200/600], Loss: 0.0005
Epoch [52/64], Step [300/600], Loss: 0.0001
Epoch [52/64], Step [400/600], Loss: 0.0002
Epoch [52/64], Step [500/600], Loss: 0.0000
Epoch [52/64], Step [600/600], Loss: 0.0003
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: 0.0000
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: 0.0000
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: 0.0003
Epoch [54/64], Step [500/600], Loss: 0.0000
Epoch [54/64], Step [600/600], Loss: 0.0008
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: 0.0000
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: 0.0000
Epoch [56/64], Step [400/600], Loss: 0.0001
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: 0.0001
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: 0.0001
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: 0.0001
Epoch [58/64], Step [400/600], Loss: 0.0001
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: 0.0000
Epoch [59/64], Step [200/600], Loss: 0.0001
Epoch [59/64], Step [300/600], Loss: 0.0000
Epoch [59/64], Step [400/600], Loss: 0.0001
Epoch [59/64], Step [500/600], Loss: 0.0001
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: 0.0001
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: 0.0000
Epoch [61/64], Step [600/600], Loss: 0.0002
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: 0.0000
Epoch [62/64], Step [400/600], Loss: 0.0000
Epoch [62/64], Step [500/600], Loss: 0.0000
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: 0.0006
Epoch [63/64], Step [500/600], Loss: 0.0015
Epoch [63/64], Step [600/600], Loss: 0.0087
Epoch [64/64], Step [100/600], Loss: 0.0025
Epoch [64/64], Step [200/600], Loss: 0.0005
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: 0.0001
Epoch [64/64], Step [600/600], Loss: 0.0024
Pytorch test completed in 435.446 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins4406068941232045698.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