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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 84166 queued and waiting for resources
srun: job 84166 has been allocated resources
Running benchmark on hydro05
Epoch [1/64], Step [100/600], Loss: 0.3324
Epoch [1/64], Step [200/600], Loss: 0.1225
Epoch [1/64], Step [300/600], Loss: 0.0367
Epoch [1/64], Step [400/600], Loss: 0.0528
Epoch [1/64], Step [500/600], Loss: 0.0359
Epoch [1/64], Step [600/600], Loss: 0.0348
Epoch [2/64], Step [100/600], Loss: 0.0167
Epoch [2/64], Step [200/600], Loss: 0.0886
Epoch [2/64], Step [300/600], Loss: 0.0828
Epoch [2/64], Step [400/600], Loss: 0.0365
Epoch [2/64], Step [500/600], Loss: 0.0299
Epoch [2/64], Step [600/600], Loss: 0.0720
Epoch [3/64], Step [100/600], Loss: 0.0247
Epoch [3/64], Step [200/600], Loss: 0.0096
Epoch [3/64], Step [300/600], Loss: 0.0375
Epoch [3/64], Step [400/600], Loss: 0.0117
Epoch [3/64], Step [500/600], Loss: 0.0798
Epoch [3/64], Step [600/600], Loss: 0.0363
Epoch [4/64], Step [100/600], Loss: 0.0138
Epoch [4/64], Step [200/600], Loss: 0.0109
Epoch [4/64], Step [300/600], Loss: 0.0212
Epoch [4/64], Step [400/600], Loss: 0.0186
Epoch [4/64], Step [500/600], Loss: 0.0091
Epoch [4/64], Step [600/600], Loss: 0.0100
Epoch [5/64], Step [100/600], Loss: 0.0331
Epoch [5/64], Step [200/600], Loss: 0.0091
Epoch [5/64], Step [300/600], Loss: 0.0330
Epoch [5/64], Step [400/600], Loss: 0.0273
Epoch [5/64], Step [500/600], Loss: 0.0209
Epoch [5/64], Step [600/600], Loss: 0.0528
Epoch [6/64], Step [100/600], Loss: 0.0048
Epoch [6/64], Step [200/600], Loss: 0.0495
Epoch [6/64], Step [300/600], Loss: 0.0072
Epoch [6/64], Step [400/600], Loss: 0.0620
Epoch [6/64], Step [500/600], Loss: 0.0062
Epoch [6/64], Step [600/600], Loss: 0.0323
Epoch [7/64], Step [100/600], Loss: 0.0085
Epoch [7/64], Step [200/600], Loss: 0.0235
Epoch [7/64], Step [300/600], Loss: 0.0066
Epoch [7/64], Step [400/600], Loss: 0.0110
Epoch [7/64], Step [500/600], Loss: 0.0557
Epoch [7/64], Step [600/600], Loss: 0.0525
Epoch [8/64], Step [100/600], Loss: 0.0357
Epoch [8/64], Step [200/600], Loss: 0.0035
Epoch [8/64], Step [300/600], Loss: 0.0068
Epoch [8/64], Step [400/600], Loss: 0.0042
Epoch [8/64], Step [500/600], Loss: 0.0066
Epoch [8/64], Step [600/600], Loss: 0.0038
Epoch [9/64], Step [100/600], Loss: 0.0157
Epoch [9/64], Step [200/600], Loss: 0.0107
Epoch [9/64], Step [300/600], Loss: 0.0215
Epoch [9/64], Step [400/600], Loss: 0.0110
Epoch [9/64], Step [500/600], Loss: 0.0048
Epoch [9/64], Step [600/600], Loss: 0.0335
Epoch [10/64], Step [100/600], Loss: 0.0012
Epoch [10/64], Step [200/600], Loss: 0.0102
Epoch [10/64], Step [300/600], Loss: 0.0039
Epoch [10/64], Step [400/600], Loss: 0.1459
Epoch [10/64], Step [500/600], Loss: 0.0006
Epoch [10/64], Step [600/600], Loss: 0.0102
Epoch [11/64], Step [100/600], Loss: 0.0026
Epoch [11/64], Step [200/600], Loss: 0.0081
Epoch [11/64], Step [300/600], Loss: 0.0104
Epoch [11/64], Step [400/600], Loss: 0.0064
Epoch [11/64], Step [500/600], Loss: 0.0114
Epoch [11/64], Step [600/600], Loss: 0.0184
Epoch [12/64], Step [100/600], Loss: 0.0043
Epoch [12/64], Step [200/600], Loss: 0.0046
Epoch [12/64], Step [300/600], Loss: 0.0008
Epoch [12/64], Step [400/600], Loss: 0.0166
Epoch [12/64], Step [500/600], Loss: 0.0043
Epoch [12/64], Step [600/600], Loss: 0.0033
Epoch [13/64], Step [100/600], Loss: 0.0101
Epoch [13/64], Step [200/600], Loss: 0.0229
Epoch [13/64], Step [300/600], Loss: 0.0007
Epoch [13/64], Step [400/600], Loss: 0.0002
Epoch [13/64], Step [500/600], Loss: 0.0218
Epoch [13/64], Step [600/600], Loss: 0.0011
Epoch [14/64], Step [100/600], Loss: 0.0149
Epoch [14/64], Step [200/600], Loss: 0.0010
Epoch [14/64], Step [300/600], Loss: 0.0173
Epoch [14/64], Step [400/600], Loss: 0.0551
Epoch [14/64], Step [500/600], Loss: 0.0014
Epoch [14/64], Step [600/600], Loss: 0.0030
Epoch [15/64], Step [100/600], Loss: 0.0040
Epoch [15/64], Step [200/600], Loss: 0.0046
Epoch [15/64], Step [300/600], Loss: 0.0018
Epoch [15/64], Step [400/600], Loss: 0.0051
Epoch [15/64], Step [500/600], Loss: 0.0009
Epoch [15/64], Step [600/600], Loss: 0.0053
Epoch [16/64], Step [100/600], Loss: 0.0070
Epoch [16/64], Step [200/600], Loss: 0.0080
Epoch [16/64], Step [300/600], Loss: 0.0010
Epoch [16/64], Step [400/600], Loss: 0.0007
Epoch [16/64], Step [500/600], Loss: 0.0070
Epoch [16/64], Step [600/600], Loss: 0.0029
Epoch [17/64], Step [100/600], Loss: 0.0039
Epoch [17/64], Step [200/600], Loss: 0.0020
Epoch [17/64], Step [300/600], Loss: 0.0003
Epoch [17/64], Step [400/600], Loss: 0.0011
Epoch [17/64], Step [500/600], Loss: 0.0022
Epoch [17/64], Step [600/600], Loss: 0.0060
Epoch [18/64], Step [100/600], Loss: 0.0098
Epoch [18/64], Step [200/600], Loss: 0.0038
Epoch [18/64], Step [300/600], Loss: 0.0047
Epoch [18/64], Step [400/600], Loss: 0.0022
Epoch [18/64], Step [500/600], Loss: 0.0029
Epoch [18/64], Step [600/600], Loss: 0.0005
Epoch [19/64], Step [100/600], Loss: 0.0009
Epoch [19/64], Step [200/600], Loss: 0.0019
Epoch [19/64], Step [300/600], Loss: 0.0021
Epoch [19/64], Step [400/600], Loss: 0.0041
Epoch [19/64], Step [500/600], Loss: 0.0007
Epoch [19/64], Step [600/600], Loss: 0.0087
Epoch [20/64], Step [100/600], Loss: 0.0010
Epoch [20/64], Step [200/600], Loss: 0.0025
Epoch [20/64], Step [300/600], Loss: 0.0024
Epoch [20/64], Step [400/600], Loss: 0.0444
Epoch [20/64], Step [500/600], Loss: 0.0016
Epoch [20/64], Step [600/600], Loss: 0.0034
Epoch [21/64], Step [100/600], Loss: 0.0010
Epoch [21/64], Step [200/600], Loss: 0.0022
Epoch [21/64], Step [300/600], Loss: 0.0003
Epoch [21/64], Step [400/600], Loss: 0.0017
Epoch [21/64], Step [500/600], Loss: 0.0052
Epoch [21/64], Step [600/600], Loss: 0.0080
Epoch [22/64], Step [100/600], Loss: 0.0028
Epoch [22/64], Step [200/600], Loss: 0.0012
Epoch [22/64], Step [300/600], Loss: 0.0020
Epoch [22/64], Step [400/600], Loss: 0.0005
Epoch [22/64], Step [500/600], Loss: 0.0006
Epoch [22/64], Step [600/600], Loss: 0.0003
Epoch [23/64], Step [100/600], Loss: 0.0011
Epoch [23/64], Step [200/600], Loss: 0.0007
Epoch [23/64], Step [300/600], Loss: 0.0003
Epoch [23/64], Step [400/600], Loss: 0.0004
Epoch [23/64], Step [500/600], Loss: 0.0003
Epoch [23/64], Step [600/600], Loss: 0.0004
Epoch [24/64], Step [100/600], Loss: 0.0002
Epoch [24/64], Step [200/600], Loss: 0.0006
Epoch [24/64], Step [300/600], Loss: 0.0005
Epoch [24/64], Step [400/600], Loss: 0.0030
Epoch [24/64], Step [500/600], Loss: 0.0006
Epoch [24/64], Step [600/600], Loss: 0.0006
Epoch [25/64], Step [100/600], Loss: 0.0011
Epoch [25/64], Step [200/600], Loss: 0.0004
Epoch [25/64], Step [300/600], Loss: 0.0074
Epoch [25/64], Step [400/600], Loss: 0.0018
Epoch [25/64], Step [500/600], Loss: 0.0055
Epoch [25/64], Step [600/600], Loss: 0.0013
Epoch [26/64], Step [100/600], Loss: 0.0005
Epoch [26/64], Step [200/600], Loss: 0.0001
Epoch [26/64], Step [300/600], Loss: 0.0010
Epoch [26/64], Step [400/600], Loss: 0.0012
Epoch [26/64], Step [500/600], Loss: 0.0004
Epoch [26/64], Step [600/600], Loss: 0.0003
Epoch [27/64], Step [100/600], Loss: 0.0002
Epoch [27/64], Step [200/600], Loss: 0.0006
Epoch [27/64], Step [300/600], Loss: 0.0006
Epoch [27/64], Step [400/600], Loss: 0.0015
Epoch [27/64], Step [500/600], Loss: 0.0001
Epoch [27/64], Step [600/600], Loss: 0.0001
Epoch [28/64], Step [100/600], Loss: 0.0018
Epoch [28/64], Step [200/600], Loss: 0.0003
Epoch [28/64], Step [300/600], Loss: 0.0009
Epoch [28/64], Step [400/600], Loss: 0.0005
Epoch [28/64], Step [500/600], Loss: 0.0037
Epoch [28/64], Step [600/600], Loss: 0.0016
Epoch [29/64], Step [100/600], Loss: 0.0022
Epoch [29/64], Step [200/600], Loss: 0.0020
Epoch [29/64], Step [300/600], Loss: 0.0002
Epoch [29/64], Step [400/600], Loss: 0.0002
Epoch [29/64], Step [500/600], Loss: 0.0002
Epoch [29/64], Step [600/600], Loss: 0.0042
Epoch [30/64], Step [100/600], Loss: 0.0000
Epoch [30/64], Step [200/600], Loss: 0.0000
Epoch [30/64], Step [300/600], Loss: 0.0001
Epoch [30/64], Step [400/600], Loss: 0.0011
Epoch [30/64], Step [500/600], Loss: 0.0002
Epoch [30/64], Step [600/600], Loss: 0.0016
Epoch [31/64], Step [100/600], Loss: 0.0001
Epoch [31/64], Step [200/600], Loss: 0.0004
Epoch [31/64], Step [300/600], Loss: 0.0000
Epoch [31/64], Step [400/600], Loss: 0.0003
Epoch [31/64], Step [500/600], Loss: 0.0006
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: 0.0003
Epoch [32/64], Step [400/600], Loss: 0.0000
Epoch [32/64], Step [500/600], Loss: 0.0002
Epoch [32/64], Step [600/600], Loss: 0.0006
Epoch [33/64], Step [100/600], Loss: 0.0009
Epoch [33/64], Step [200/600], Loss: 0.0001
Epoch [33/64], Step [300/600], Loss: 0.0002
Epoch [33/64], Step [400/600], Loss: 0.0023
Epoch [33/64], Step [500/600], Loss: 0.0011
Epoch [33/64], Step [600/600], Loss: 0.0000
Epoch [34/64], Step [100/600], Loss: 0.0114
Epoch [34/64], Step [200/600], Loss: 0.0054
Epoch [34/64], Step [300/600], Loss: 0.0034
Epoch [34/64], Step [400/600], Loss: 0.0002
Epoch [34/64], Step [500/600], Loss: 0.0001
Epoch [34/64], Step [600/600], Loss: 0.0003
Epoch [35/64], Step [100/600], Loss: 0.0002
Epoch [35/64], Step [200/600], Loss: 0.0002
Epoch [35/64], Step [300/600], Loss: 0.0003
Epoch [35/64], Step [400/600], Loss: 0.0000
Epoch [35/64], Step [500/600], Loss: 0.0012
Epoch [35/64], Step [600/600], Loss: 0.0011
Epoch [36/64], Step [100/600], Loss: 0.0003
Epoch [36/64], Step [200/600], Loss: 0.0003
Epoch [36/64], Step [300/600], Loss: 0.0001
Epoch [36/64], Step [400/600], Loss: 0.0011
Epoch [36/64], Step [500/600], Loss: 0.0001
Epoch [36/64], Step [600/600], Loss: 0.0009
Epoch [37/64], Step [100/600], Loss: 0.0000
Epoch [37/64], Step [200/600], Loss: 0.0003
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: 0.0002
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: 0.0000
Epoch [38/64], Step [300/600], Loss: 0.0003
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: 0.0001
Epoch [39/64], Step [100/600], Loss: 0.0002
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: 0.0002
Epoch [40/64], Step [100/600], Loss: 0.0001
Epoch [40/64], Step [200/600], Loss: 0.0002
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: 0.0001
Epoch [40/64], Step [600/600], Loss: 0.0001
Epoch [41/64], Step [100/600], Loss: 0.0002
Epoch [41/64], Step [200/600], Loss: 0.0003
Epoch [41/64], Step [300/600], Loss: 0.0000
Epoch [41/64], Step [400/600], Loss: 0.0000
Epoch [41/64], Step [500/600], Loss: 0.0000
Epoch [41/64], Step [600/600], Loss: 0.0000
Epoch [42/64], Step [100/600], Loss: 0.0001
Epoch [42/64], Step [200/600], Loss: 0.0000
Epoch [42/64], Step [300/600], Loss: 0.0000
Epoch [42/64], Step [400/600], Loss: 0.0380
Epoch [42/64], Step [500/600], Loss: 0.0021
Epoch [42/64], Step [600/600], Loss: 0.0227
Epoch [43/64], Step [100/600], Loss: 0.0907
Epoch [43/64], Step [200/600], Loss: 0.0002
Epoch [43/64], Step [300/600], Loss: 0.0011
Epoch [43/64], Step [400/600], Loss: 0.0006
Epoch [43/64], Step [500/600], Loss: 0.0002
Epoch [43/64], Step [600/600], Loss: 0.0006
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: 0.0005
Epoch [44/64], Step [400/600], Loss: 0.0013
Epoch [44/64], Step [500/600], Loss: 0.0001
Epoch [44/64], Step [600/600], Loss: 0.0011
Epoch [45/64], Step [100/600], Loss: 0.0069
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: 0.0000
Epoch [45/64], Step [500/600], Loss: 0.0000
Epoch [45/64], Step [600/600], Loss: 0.0004
Epoch [46/64], Step [100/600], Loss: 0.0002
Epoch [46/64], Step [200/600], Loss: 0.0000
Epoch [46/64], Step [300/600], Loss: 0.0004
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: 0.0001
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: 0.0001
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: 0.0001
Epoch [48/64], Step [500/600], Loss: 0.0001
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: 0.0001
Epoch [49/64], Step [300/600], Loss: 0.0001
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: 0.0001
Epoch [50/64], Step [100/600], Loss: 0.0000
Epoch [50/64], Step [200/600], Loss: 0.0000
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: 0.0001
Epoch [50/64], Step [600/600], Loss: 0.0001
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: 0.0001
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: 0.0001
Epoch [52/64], Step [300/600], Loss: 0.0001
Epoch [52/64], Step [400/600], Loss: 0.0000
Epoch [52/64], Step [500/600], Loss: 0.0043
Epoch [52/64], Step [600/600], Loss: 0.0007
Epoch [53/64], Step [100/600], Loss: 0.0006
Epoch [53/64], Step [200/600], Loss: 0.0004
Epoch [53/64], Step [300/600], Loss: 0.0235
Epoch [53/64], Step [400/600], Loss: 0.0043
Epoch [53/64], Step [500/600], Loss: 0.0008
Epoch [53/64], Step [600/600], Loss: 0.0002
Epoch [54/64], Step [100/600], Loss: 0.0003
Epoch [54/64], Step [200/600], Loss: 0.0001
Epoch [54/64], Step [300/600], Loss: 0.0016
Epoch [54/64], Step [400/600], Loss: 0.0001
Epoch [54/64], Step [500/600], Loss: 0.0002
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: 0.0000
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: 0.0001
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: 0.0004
Epoch [56/64], Step [400/600], Loss: 0.0002
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: 0.0000
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: 0.0003
Epoch [58/64], Step [200/600], Loss: 0.0002
Epoch [58/64], Step [300/600], Loss: 0.0000
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: 0.0001
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: 0.0001
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: 0.0001
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: 0.0001
Epoch [60/64], Step [500/600], Loss: 0.0003
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: 0.0002
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: 0.0000
Epoch [62/64], Step [100/600], Loss: 0.0000
Epoch [62/64], Step [200/600], Loss: 0.0001
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: 0.0002
Epoch [63/64], Step [500/600], Loss: 0.0002
Epoch [63/64], Step [600/600], Loss: 0.0000
Epoch [64/64], Step [100/600], Loss: 0.0001
Epoch [64/64], Step [200/600], Loss: 0.0000
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: 0.0000
Epoch [64/64], Step [600/600], Loss: 0.0000
Pytorch test completed in 438.023 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins5887330316556132733.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