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 97420 queued and waiting for resources
srun: job 97420 has been allocated resources
Running benchmark on hydro06
Epoch [1/64], Step [100/600], Loss: 0.2118
Epoch [1/64], Step [200/600], Loss: 0.0801
Epoch [1/64], Step [300/600], Loss: 0.1100
Epoch [1/64], Step [400/600], Loss: 0.0915
Epoch [1/64], Step [500/600], Loss: 0.0597
Epoch [1/64], Step [600/600], Loss: 0.0489
Epoch [2/64], Step [100/600], Loss: 0.0350
Epoch [2/64], Step [200/600], Loss: 0.0592
Epoch [2/64], Step [300/600], Loss: 0.0114
Epoch [2/64], Step [400/600], Loss: 0.0986
Epoch [2/64], Step [500/600], Loss: 0.1087
Epoch [2/64], Step [600/600], Loss: 0.0904
Epoch [3/64], Step [100/600], Loss: 0.0221
Epoch [3/64], Step [200/600], Loss: 0.0885
Epoch [3/64], Step [300/600], Loss: 0.0438
Epoch [3/64], Step [400/600], Loss: 0.0275
Epoch [3/64], Step [500/600], Loss: 0.0968
Epoch [3/64], Step [600/600], Loss: 0.0640
Epoch [4/64], Step [100/600], Loss: 0.0233
Epoch [4/64], Step [200/600], Loss: 0.0091
Epoch [4/64], Step [300/600], Loss: 0.0145
Epoch [4/64], Step [400/600], Loss: 0.0061
Epoch [4/64], Step [500/600], Loss: 0.0142
Epoch [4/64], Step [600/600], Loss: 0.0097
Epoch [5/64], Step [100/600], Loss: 0.0107
Epoch [5/64], Step [200/600], Loss: 0.0067
Epoch [5/64], Step [300/600], Loss: 0.0207
Epoch [5/64], Step [400/600], Loss: 0.0052
Epoch [5/64], Step [500/600], Loss: 0.0100
Epoch [5/64], Step [600/600], Loss: 0.0159
Epoch [6/64], Step [100/600], Loss: 0.0224
Epoch [6/64], Step [200/600], Loss: 0.0058
Epoch [6/64], Step [300/600], Loss: 0.0336
Epoch [6/64], Step [400/600], Loss: 0.0290
Epoch [6/64], Step [500/600], Loss: 0.0249
Epoch [6/64], Step [600/600], Loss: 0.0368
Epoch [7/64], Step [100/600], Loss: 0.0053
Epoch [7/64], Step [200/600], Loss: 0.0304
Epoch [7/64], Step [300/600], Loss: 0.0417
Epoch [7/64], Step [400/600], Loss: 0.0051
Epoch [7/64], Step [500/600], Loss: 0.0066
Epoch [7/64], Step [600/600], Loss: 0.0095
Epoch [8/64], Step [100/600], Loss: 0.0171
Epoch [8/64], Step [200/600], Loss: 0.0062
Epoch [8/64], Step [300/600], Loss: 0.0299
Epoch [8/64], Step [400/600], Loss: 0.0207
Epoch [8/64], Step [500/600], Loss: 0.0051
Epoch [8/64], Step [600/600], Loss: 0.0013
Epoch [9/64], Step [100/600], Loss: 0.0033
Epoch [9/64], Step [200/600], Loss: 0.0023
Epoch [9/64], Step [300/600], Loss: 0.0204
Epoch [9/64], Step [400/600], Loss: 0.0148
Epoch [9/64], Step [500/600], Loss: 0.0019
Epoch [9/64], Step [600/600], Loss: 0.0022
Epoch [10/64], Step [100/600], Loss: 0.0121
Epoch [10/64], Step [200/600], Loss: 0.0079
Epoch [10/64], Step [300/600], Loss: 0.0070
Epoch [10/64], Step [400/600], Loss: 0.0045
Epoch [10/64], Step [500/600], Loss: 0.2143
Epoch [10/64], Step [600/600], Loss: 0.0017
Epoch [11/64], Step [100/600], Loss: 0.0047
Epoch [11/64], Step [200/600], Loss: 0.0007
Epoch [11/64], Step [300/600], Loss: 0.0027
Epoch [11/64], Step [400/600], Loss: 0.0067
Epoch [11/64], Step [500/600], Loss: 0.0047
Epoch [11/64], Step [600/600], Loss: 0.0165
Epoch [12/64], Step [100/600], Loss: 0.0073
Epoch [12/64], Step [200/600], Loss: 0.0021
Epoch [12/64], Step [300/600], Loss: 0.0037
Epoch [12/64], Step [400/600], Loss: 0.0022
Epoch [12/64], Step [500/600], Loss: 0.0021
Epoch [12/64], Step [600/600], Loss: 0.0011
Epoch [13/64], Step [100/600], Loss: 0.0115
Epoch [13/64], Step [200/600], Loss: 0.0033
Epoch [13/64], Step [300/600], Loss: 0.0071
Epoch [13/64], Step [400/600], Loss: 0.0008
Epoch [13/64], Step [500/600], Loss: 0.0031
Epoch [13/64], Step [600/600], Loss: 0.0562
Epoch [14/64], Step [100/600], Loss: 0.0021
Epoch [14/64], Step [200/600], Loss: 0.0017
Epoch [14/64], Step [300/600], Loss: 0.0034
Epoch [14/64], Step [400/600], Loss: 0.0008
Epoch [14/64], Step [500/600], Loss: 0.0027
Epoch [14/64], Step [600/600], Loss: 0.0045
Epoch [15/64], Step [100/600], Loss: 0.0008
Epoch [15/64], Step [200/600], Loss: 0.0004
Epoch [15/64], Step [300/600], Loss: 0.0003
Epoch [15/64], Step [400/600], Loss: 0.0004
Epoch [15/64], Step [500/600], Loss: 0.0421
Epoch [15/64], Step [600/600], Loss: 0.0008
Epoch [16/64], Step [100/600], Loss: 0.0007
Epoch [16/64], Step [200/600], Loss: 0.0073
Epoch [16/64], Step [300/600], Loss: 0.0018
Epoch [16/64], Step [400/600], Loss: 0.0015
Epoch [16/64], Step [500/600], Loss: 0.0010
Epoch [16/64], Step [600/600], Loss: 0.0062
Epoch [17/64], Step [100/600], Loss: 0.0021
Epoch [17/64], Step [200/600], Loss: 0.0067
Epoch [17/64], Step [300/600], Loss: 0.0042
Epoch [17/64], Step [400/600], Loss: 0.0014
Epoch [17/64], Step [500/600], Loss: 0.0042
Epoch [17/64], Step [600/600], Loss: 0.0006
Epoch [18/64], Step [100/600], Loss: 0.0001
Epoch [18/64], Step [200/600], Loss: 0.0008
Epoch [18/64], Step [300/600], Loss: 0.0065
Epoch [18/64], Step [400/600], Loss: 0.0011
Epoch [18/64], Step [500/600], Loss: 0.0045
Epoch [18/64], Step [600/600], Loss: 0.0023
Epoch [19/64], Step [100/600], Loss: 0.0004
Epoch [19/64], Step [200/600], Loss: 0.0014
Epoch [19/64], Step [300/600], Loss: 0.0018
Epoch [19/64], Step [400/600], Loss: 0.0005
Epoch [19/64], Step [500/600], Loss: 0.0022
Epoch [19/64], Step [600/600], Loss: 0.0042
Epoch [20/64], Step [100/600], Loss: 0.0054
Epoch [20/64], Step [200/600], Loss: 0.0021
Epoch [20/64], Step [300/600], Loss: 0.0003
Epoch [20/64], Step [400/600], Loss: 0.0013
Epoch [20/64], Step [500/600], Loss: 0.0002
Epoch [20/64], Step [600/600], Loss: 0.0019
Epoch [21/64], Step [100/600], Loss: 0.0049
Epoch [21/64], Step [200/600], Loss: 0.0043
Epoch [21/64], Step [300/600], Loss: 0.0008
Epoch [21/64], Step [400/600], Loss: 0.0004
Epoch [21/64], Step [500/600], Loss: 0.0004
Epoch [21/64], Step [600/600], Loss: 0.0003
Epoch [22/64], Step [100/600], Loss: 0.0062
Epoch [22/64], Step [200/600], Loss: 0.0001
Epoch [22/64], Step [300/600], Loss: 0.0009
Epoch [22/64], Step [400/600], Loss: 0.0038
Epoch [22/64], Step [500/600], Loss: 0.0032
Epoch [22/64], Step [600/600], Loss: 0.0009
Epoch [23/64], Step [100/600], Loss: 0.0017
Epoch [23/64], Step [200/600], Loss: 0.0115
Epoch [23/64], Step [300/600], Loss: 0.0027
Epoch [23/64], Step [400/600], Loss: 0.0037
Epoch [23/64], Step [500/600], Loss: 0.0002
Epoch [23/64], Step [600/600], Loss: 0.0023
Epoch [24/64], Step [100/600], Loss: 0.0004
Epoch [24/64], Step [200/600], Loss: 0.0002
Epoch [24/64], Step [300/600], Loss: 0.0006
Epoch [24/64], Step [400/600], Loss: 0.0005
Epoch [24/64], Step [500/600], Loss: 0.0012
Epoch [24/64], Step [600/600], Loss: 0.0009
Epoch [25/64], Step [100/600], Loss: 0.0002
Epoch [25/64], Step [200/600], Loss: 0.0003
Epoch [25/64], Step [300/600], Loss: 0.0000
Epoch [25/64], Step [400/600], Loss: 0.0000
Epoch [25/64], Step [500/600], Loss: 0.0000
Epoch [25/64], Step [600/600], Loss: 0.0001
Epoch [26/64], Step [100/600], Loss: 0.0002
Epoch [26/64], Step [200/600], Loss: 0.0001
Epoch [26/64], Step [300/600], Loss: 0.0001
Epoch [26/64], Step [400/600], Loss: 0.0002
Epoch [26/64], Step [500/600], Loss: 0.0003
Epoch [26/64], Step [600/600], Loss: 0.0000
Epoch [27/64], Step [100/600], Loss: 0.0003
Epoch [27/64], Step [200/600], Loss: 0.0000
Epoch [27/64], Step [300/600], Loss: 0.0001
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: 0.0001
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: 0.0001
Epoch [28/64], Step [500/600], Loss: 0.0086
Epoch [28/64], Step [600/600], Loss: 0.0048
Epoch [29/64], Step [100/600], Loss: 0.0098
Epoch [29/64], Step [200/600], Loss: 0.0003
Epoch [29/64], Step [300/600], Loss: 0.0021
Epoch [29/64], Step [400/600], Loss: 0.0010
Epoch [29/64], Step [500/600], Loss: 0.0009
Epoch [29/64], Step [600/600], Loss: 0.0151
Epoch [30/64], Step [100/600], Loss: 0.0010
Epoch [30/64], Step [200/600], Loss: 0.0013
Epoch [30/64], Step [300/600], Loss: 0.0001
Epoch [30/64], Step [400/600], Loss: 0.0026
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: 0.0000
Epoch [31/64], Step [300/600], Loss: 0.0001
Epoch [31/64], Step [400/600], Loss: 0.0003
Epoch [31/64], Step [500/600], Loss: 0.0002
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: 0.0003
Epoch [32/64], Step [300/600], Loss: 0.0001
Epoch [32/64], Step [400/600], Loss: 0.0002
Epoch [32/64], Step [500/600], Loss: 0.0001
Epoch [32/64], Step [600/600], Loss: 0.0004
Epoch [33/64], Step [100/600], Loss: 0.0001
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: 0.0001
Epoch [33/64], Step [500/600], Loss: 0.0002
Epoch [33/64], Step [600/600], Loss: 0.0002
Epoch [34/64], Step [100/600], Loss: 0.0000
Epoch [34/64], Step [200/600], Loss: 0.0001
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: 0.0023
Epoch [34/64], Step [600/600], Loss: 0.0003
Epoch [35/64], Step [100/600], Loss: 0.0004
Epoch [35/64], Step [200/600], Loss: 0.0001
Epoch [35/64], Step [300/600], Loss: 0.0000
Epoch [35/64], Step [400/600], Loss: 0.0000
Epoch [35/64], Step [500/600], Loss: 0.0001
Epoch [35/64], Step [600/600], Loss: 0.0002
Epoch [36/64], Step [100/600], Loss: 0.0000
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: 0.0001
Epoch [36/64], Step [500/600], Loss: 0.0002
Epoch [36/64], Step [600/600], Loss: 0.0008
Epoch [37/64], Step [100/600], Loss: 0.0000
Epoch [37/64], Step [200/600], Loss: 0.0012
Epoch [37/64], Step [300/600], Loss: 0.0049
Epoch [37/64], Step [400/600], Loss: 0.0015
Epoch [37/64], Step [500/600], Loss: 0.0105
Epoch [37/64], Step [600/600], Loss: 0.0007
Epoch [38/64], Step [100/600], Loss: 0.0003
Epoch [38/64], Step [200/600], Loss: 0.0002
Epoch [38/64], Step [300/600], Loss: 0.0003
Epoch [38/64], Step [400/600], Loss: 0.0026
Epoch [38/64], Step [500/600], Loss: 0.0239
Epoch [38/64], Step [600/600], Loss: 0.0116
Epoch [39/64], Step [100/600], Loss: 0.0004
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: 0.0001
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: 0.0000
Epoch [40/64], Step [200/600], Loss: 0.0001
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: 0.0004
Epoch [41/64], Step [100/600], Loss: 0.0000
Epoch [41/64], Step [200/600], Loss: 0.0001
Epoch [41/64], Step [300/600], Loss: 0.0000
Epoch [41/64], Step [400/600], Loss: 0.0001
Epoch [41/64], Step [500/600], Loss: 0.0005
Epoch [41/64], Step [600/600], Loss: 0.0002
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: 0.0002
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: 0.0001
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: 0.0000
Epoch [43/64], Step [400/600], Loss: 0.0000
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: 0.0000
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: 0.0002
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: 0.0001
Epoch [45/64], Step [300/600], Loss: 0.0001
Epoch [45/64], Step [400/600], Loss: 0.0001
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: 0.0001
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: 0.0000
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: 0.0000
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: 0.0002
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: 0.0003
Epoch [48/64], Step [400/600], Loss: 0.0001
Epoch [48/64], Step [500/600], Loss: 0.0050
Epoch [48/64], Step [600/600], Loss: 0.0456
Epoch [49/64], Step [100/600], Loss: 0.0520
Epoch [49/64], Step [200/600], Loss: 0.0364
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: 0.0000
Epoch [49/64], Step [600/600], Loss: 0.0016
Epoch [50/64], Step [100/600], Loss: 0.0000
Epoch [50/64], Step [200/600], Loss: 0.0004
Epoch [50/64], Step [300/600], Loss: 0.0000
Epoch [50/64], Step [400/600], Loss: 0.0005
Epoch [50/64], Step [500/600], Loss: 0.0001
Epoch [50/64], Step [600/600], Loss: 0.0008
Epoch [51/64], Step [100/600], Loss: 0.0001
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: 0.0001
Epoch [51/64], Step [500/600], Loss: 0.0000
Epoch [51/64], Step [600/600], Loss: 0.0000
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: 0.0002
Epoch [52/64], Step [400/600], Loss: 0.0006
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: 0.0000
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: 0.0002
Epoch [53/64], Step [500/600], Loss: 0.0000
Epoch [53/64], Step [600/600], Loss: 0.0001
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: 0.0001
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: 0.0001
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: 0.0001
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: 0.0000
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: 0.0000
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: 0.0000
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: 0.0302
Epoch [59/64], Step [500/600], Loss: 0.0051
Epoch [59/64], Step [600/600], Loss: 0.0010
Epoch [60/64], Step [100/600], Loss: 0.0007
Epoch [60/64], Step [200/600], Loss: 0.0001
Epoch [60/64], Step [300/600], Loss: 0.0034
Epoch [60/64], Step [400/600], Loss: 0.0008
Epoch [60/64], Step [500/600], Loss: 0.0003
Epoch [60/64], Step [600/600], Loss: 0.0180
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: 0.0012
Epoch [61/64], Step [400/600], Loss: 0.0008
Epoch [61/64], Step [500/600], Loss: 0.0003
Epoch [61/64], Step [600/600], Loss: 0.0004
Epoch [62/64], Step [100/600], Loss: 0.0002
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: 0.0002
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: 0.0001
Epoch [63/64], Step [300/600], Loss: 0.0000
Epoch [63/64], Step [400/600], Loss: 0.0001
Epoch [63/64], Step [500/600], Loss: 0.0000
Epoch [63/64], Step [600/600], Loss: 0.0000
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: 0.0001
Epoch [64/64], Step [400/600], Loss: 0.0001
Epoch [64/64], Step [500/600], Loss: 0.0001
Epoch [64/64], Step [600/600], Loss: 0.0000
Pytorch test completed in 380.070 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins6161838393532299907.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