Skip to content
[Jenkins]Jenkins
Success

Console Output

Download View as plain text
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 96136 queued and waiting for resources
srun: job 96136 has been allocated resources
Running benchmark on hydro05
Epoch [1/64], Step [100/600], Loss: 0.2363
Epoch [1/64], Step [200/600], Loss: 0.1420
Epoch [1/64], Step [300/600], Loss: 0.1596
Epoch [1/64], Step [400/600], Loss: 0.0881
Epoch [1/64], Step [500/600], Loss: 0.0608
Epoch [1/64], Step [600/600], Loss: 0.0573
Epoch [2/64], Step [100/600], Loss: 0.0476
Epoch [2/64], Step [200/600], Loss: 0.0620
Epoch [2/64], Step [300/600], Loss: 0.0576
Epoch [2/64], Step [400/600], Loss: 0.0210
Epoch [2/64], Step [500/600], Loss: 0.0497
Epoch [2/64], Step [600/600], Loss: 0.0591
Epoch [3/64], Step [100/600], Loss: 0.0722
Epoch [3/64], Step [200/600], Loss: 0.0576
Epoch [3/64], Step [300/600], Loss: 0.0095
Epoch [3/64], Step [400/600], Loss: 0.0266
Epoch [3/64], Step [500/600], Loss: 0.0166
Epoch [3/64], Step [600/600], Loss: 0.0514
Epoch [4/64], Step [100/600], Loss: 0.0231
Epoch [4/64], Step [200/600], Loss: 0.0195
Epoch [4/64], Step [300/600], Loss: 0.0064
Epoch [4/64], Step [400/600], Loss: 0.0115
Epoch [4/64], Step [500/600], Loss: 0.0377
Epoch [4/64], Step [600/600], Loss: 0.0076
Epoch [5/64], Step [100/600], Loss: 0.0418
Epoch [5/64], Step [200/600], Loss: 0.0146
Epoch [5/64], Step [300/600], Loss: 0.0956
Epoch [5/64], Step [400/600], Loss: 0.0209
Epoch [5/64], Step [500/600], Loss: 0.0237
Epoch [5/64], Step [600/600], Loss: 0.0359
Epoch [6/64], Step [100/600], Loss: 0.0208
Epoch [6/64], Step [200/600], Loss: 0.0102
Epoch [6/64], Step [300/600], Loss: 0.0049
Epoch [6/64], Step [400/600], Loss: 0.0015
Epoch [6/64], Step [500/600], Loss: 0.0130
Epoch [6/64], Step [600/600], Loss: 0.0288
Epoch [7/64], Step [100/600], Loss: 0.0294
Epoch [7/64], Step [200/600], Loss: 0.0324
Epoch [7/64], Step [300/600], Loss: 0.1992
Epoch [7/64], Step [400/600], Loss: 0.0131
Epoch [7/64], Step [500/600], Loss: 0.0302
Epoch [7/64], Step [600/600], Loss: 0.0097
Epoch [8/64], Step [100/600], Loss: 0.0018
Epoch [8/64], Step [200/600], Loss: 0.0353
Epoch [8/64], Step [300/600], Loss: 0.0035
Epoch [8/64], Step [400/600], Loss: 0.0178
Epoch [8/64], Step [500/600], Loss: 0.0085
Epoch [8/64], Step [600/600], Loss: 0.0031
Epoch [9/64], Step [100/600], Loss: 0.0089
Epoch [9/64], Step [200/600], Loss: 0.0374
Epoch [9/64], Step [300/600], Loss: 0.0105
Epoch [9/64], Step [400/600], Loss: 0.0426
Epoch [9/64], Step [500/600], Loss: 0.0607
Epoch [9/64], Step [600/600], Loss: 0.0215
Epoch [10/64], Step [100/600], Loss: 0.0084
Epoch [10/64], Step [200/600], Loss: 0.0107
Epoch [10/64], Step [300/600], Loss: 0.0027
Epoch [10/64], Step [400/600], Loss: 0.0226
Epoch [10/64], Step [500/600], Loss: 0.0047
Epoch [10/64], Step [600/600], Loss: 0.0139
Epoch [11/64], Step [100/600], Loss: 0.0026
Epoch [11/64], Step [200/600], Loss: 0.0035
Epoch [11/64], Step [300/600], Loss: 0.0085
Epoch [11/64], Step [400/600], Loss: 0.0183
Epoch [11/64], Step [500/600], Loss: 0.0055
Epoch [11/64], Step [600/600], Loss: 0.0031
Epoch [12/64], Step [100/600], Loss: 0.0029
Epoch [12/64], Step [200/600], Loss: 0.0031
Epoch [12/64], Step [300/600], Loss: 0.0181
Epoch [12/64], Step [400/600], Loss: 0.0098
Epoch [12/64], Step [500/600], Loss: 0.0153
Epoch [12/64], Step [600/600], Loss: 0.0695
Epoch [13/64], Step [100/600], Loss: 0.0164
Epoch [13/64], Step [200/600], Loss: 0.0017
Epoch [13/64], Step [300/600], Loss: 0.0035
Epoch [13/64], Step [400/600], Loss: 0.0018
Epoch [13/64], Step [500/600], Loss: 0.0018
Epoch [13/64], Step [600/600], Loss: 0.0026
Epoch [14/64], Step [100/600], Loss: 0.0027
Epoch [14/64], Step [200/600], Loss: 0.0041
Epoch [14/64], Step [300/600], Loss: 0.0010
Epoch [14/64], Step [400/600], Loss: 0.0241
Epoch [14/64], Step [500/600], Loss: 0.0021
Epoch [14/64], Step [600/600], Loss: 0.0083
Epoch [15/64], Step [100/600], Loss: 0.0055
Epoch [15/64], Step [200/600], Loss: 0.0003
Epoch [15/64], Step [300/600], Loss: 0.0020
Epoch [15/64], Step [400/600], Loss: 0.0019
Epoch [15/64], Step [500/600], Loss: 0.0090
Epoch [15/64], Step [600/600], Loss: 0.0042
Epoch [16/64], Step [100/600], Loss: 0.0083
Epoch [16/64], Step [200/600], Loss: 0.0177
Epoch [16/64], Step [300/600], Loss: 0.0039
Epoch [16/64], Step [400/600], Loss: 0.0036
Epoch [16/64], Step [500/600], Loss: 0.0014
Epoch [16/64], Step [600/600], Loss: 0.0089
Epoch [17/64], Step [100/600], Loss: 0.0021
Epoch [17/64], Step [200/600], Loss: 0.0006
Epoch [17/64], Step [300/600], Loss: 0.0018
Epoch [17/64], Step [400/600], Loss: 0.0011
Epoch [17/64], Step [500/600], Loss: 0.0026
Epoch [17/64], Step [600/600], Loss: 0.0084
Epoch [18/64], Step [100/600], Loss: 0.0013
Epoch [18/64], Step [200/600], Loss: 0.0067
Epoch [18/64], Step [300/600], Loss: 0.0008
Epoch [18/64], Step [400/600], Loss: 0.0007
Epoch [18/64], Step [500/600], Loss: 0.0044
Epoch [18/64], Step [600/600], Loss: 0.0007
Epoch [19/64], Step [100/600], Loss: 0.0020
Epoch [19/64], Step [200/600], Loss: 0.0007
Epoch [19/64], Step [300/600], Loss: 0.0001
Epoch [19/64], Step [400/600], Loss: 0.0008
Epoch [19/64], Step [500/600], Loss: 0.0016
Epoch [19/64], Step [600/600], Loss: 0.0006
Epoch [20/64], Step [100/600], Loss: 0.0011
Epoch [20/64], Step [200/600], Loss: 0.0007
Epoch [20/64], Step [300/600], Loss: 0.0028
Epoch [20/64], Step [400/600], Loss: 0.0018
Epoch [20/64], Step [500/600], Loss: 0.0101
Epoch [20/64], Step [600/600], Loss: 0.0047
Epoch [21/64], Step [100/600], Loss: 0.0014
Epoch [21/64], Step [200/600], Loss: 0.0001
Epoch [21/64], Step [300/600], Loss: 0.0011
Epoch [21/64], Step [400/600], Loss: 0.0007
Epoch [21/64], Step [500/600], Loss: 0.0101
Epoch [21/64], Step [600/600], Loss: 0.0186
Epoch [22/64], Step [100/600], Loss: 0.0025
Epoch [22/64], Step [200/600], Loss: 0.0002
Epoch [22/64], Step [300/600], Loss: 0.0008
Epoch [22/64], Step [400/600], Loss: 0.0104
Epoch [22/64], Step [500/600], Loss: 0.0014
Epoch [22/64], Step [600/600], Loss: 0.0102
Epoch [23/64], Step [100/600], Loss: 0.0027
Epoch [23/64], Step [200/600], Loss: 0.0004
Epoch [23/64], Step [300/600], Loss: 0.0004
Epoch [23/64], Step [400/600], Loss: 0.0010
Epoch [23/64], Step [500/600], Loss: 0.0011
Epoch [23/64], Step [600/600], Loss: 0.0003
Epoch [24/64], Step [100/600], Loss: 0.0005
Epoch [24/64], Step [200/600], Loss: 0.0060
Epoch [24/64], Step [300/600], Loss: 0.0011
Epoch [24/64], Step [400/600], Loss: 0.0004
Epoch [24/64], Step [500/600], Loss: 0.0017
Epoch [24/64], Step [600/600], Loss: 0.0267
Epoch [25/64], Step [100/600], Loss: 0.0002
Epoch [25/64], Step [200/600], Loss: 0.0004
Epoch [25/64], Step [300/600], Loss: 0.0025
Epoch [25/64], Step [400/600], Loss: 0.0004
Epoch [25/64], Step [500/600], Loss: 0.0004
Epoch [25/64], Step [600/600], Loss: 0.0017
Epoch [26/64], Step [100/600], Loss: 0.0014
Epoch [26/64], Step [200/600], Loss: 0.0006
Epoch [26/64], Step [300/600], Loss: 0.0000
Epoch [26/64], Step [400/600], Loss: 0.0001
Epoch [26/64], Step [500/600], Loss: 0.0134
Epoch [26/64], Step [600/600], Loss: 0.0006
Epoch [27/64], Step [100/600], Loss: 0.0009
Epoch [27/64], Step [200/600], Loss: 0.0007
Epoch [27/64], Step [300/600], Loss: 0.0042
Epoch [27/64], Step [400/600], Loss: 0.0022
Epoch [27/64], Step [500/600], Loss: 0.0181
Epoch [27/64], Step [600/600], Loss: 0.0034
Epoch [28/64], Step [100/600], Loss: 0.0047
Epoch [28/64], Step [200/600], Loss: 0.0004
Epoch [28/64], Step [300/600], Loss: 0.0221
Epoch [28/64], Step [400/600], Loss: 0.0033
Epoch [28/64], Step [500/600], Loss: 0.0013
Epoch [28/64], Step [600/600], Loss: 0.0005
Epoch [29/64], Step [100/600], Loss: 0.0001
Epoch [29/64], Step [200/600], Loss: 0.0017
Epoch [29/64], Step [300/600], Loss: 0.0002
Epoch [29/64], Step [400/600], Loss: 0.0007
Epoch [29/64], Step [500/600], Loss: 0.0002
Epoch [29/64], Step [600/600], Loss: 0.0004
Epoch [30/64], Step [100/600], Loss: 0.0001
Epoch [30/64], Step [200/600], Loss: 0.0006
Epoch [30/64], Step [300/600], Loss: 0.0004
Epoch [30/64], Step [400/600], Loss: 0.0004
Epoch [30/64], Step [500/600], Loss: 0.0001
Epoch [30/64], Step [600/600], Loss: 0.0006
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: 0.0002
Epoch [31/64], Step [400/600], Loss: 0.0003
Epoch [31/64], Step [500/600], Loss: 0.0005
Epoch [31/64], Step [600/600], Loss: 0.0000
Epoch [32/64], Step [100/600], Loss: 0.0000
Epoch [32/64], Step [200/600], Loss: 0.0004
Epoch [32/64], Step [300/600], Loss: 0.0001
Epoch [32/64], Step [400/600], Loss: 0.0000
Epoch [32/64], Step [500/600], Loss: 0.0005
Epoch [32/64], Step [600/600], Loss: 0.0000
Epoch [33/64], Step [100/600], Loss: 0.0000
Epoch [33/64], Step [200/600], Loss: 0.0001
Epoch [33/64], Step [300/600], Loss: 0.0001
Epoch [33/64], Step [400/600], Loss: 0.0018
Epoch [33/64], Step [500/600], Loss: 0.0237
Epoch [33/64], Step [600/600], Loss: 0.0034
Epoch [34/64], Step [100/600], Loss: 0.0006
Epoch [34/64], Step [200/600], Loss: 0.0010
Epoch [34/64], Step [300/600], Loss: 0.0001
Epoch [34/64], Step [400/600], Loss: 0.0025
Epoch [34/64], Step [500/600], Loss: 0.0002
Epoch [34/64], Step [600/600], Loss: 0.0002
Epoch [35/64], Step [100/600], Loss: 0.0012
Epoch [35/64], Step [200/600], Loss: 0.0066
Epoch [35/64], Step [300/600], Loss: 0.0018
Epoch [35/64], Step [400/600], Loss: 0.0001
Epoch [35/64], Step [500/600], Loss: 0.0000
Epoch [35/64], Step [600/600], Loss: 0.0001
Epoch [36/64], Step [100/600], Loss: 0.0010
Epoch [36/64], Step [200/600], Loss: 0.0001
Epoch [36/64], Step [300/600], Loss: 0.0015
Epoch [36/64], Step [400/600], Loss: 0.0010
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: 0.0001
Epoch [37/64], Step [200/600], Loss: 0.0000
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: 0.0000
Epoch [37/64], Step [600/600], Loss: 0.0001
Epoch [38/64], Step [100/600], Loss: 0.0004
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: 0.0001
Epoch [38/64], Step [500/600], Loss: 0.0001
Epoch [38/64], Step [600/600], Loss: 0.0000
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: 0.0005
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: 0.0001
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: 0.0001
Epoch [41/64], Step [100/600], Loss: 0.0001
Epoch [41/64], Step [200/600], Loss: 0.0001
Epoch [41/64], Step [300/600], Loss: 0.0001
Epoch [41/64], Step [400/600], Loss: 0.0000
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: 0.0000
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: 0.0869
Epoch [43/64], Step [100/600], Loss: 0.0007
Epoch [43/64], Step [200/600], Loss: 0.0470
Epoch [43/64], Step [300/600], Loss: 0.0026
Epoch [43/64], Step [400/600], Loss: 0.0007
Epoch [43/64], Step [500/600], Loss: 0.0309
Epoch [43/64], Step [600/600], Loss: 0.0033
Epoch [44/64], Step [100/600], Loss: 0.0315
Epoch [44/64], Step [200/600], Loss: 0.0011
Epoch [44/64], Step [300/600], Loss: 0.0007
Epoch [44/64], Step [400/600], Loss: 0.0003
Epoch [44/64], Step [500/600], Loss: 0.0000
Epoch [44/64], Step [600/600], Loss: 0.0001
Epoch [45/64], Step [100/600], Loss: 0.0001
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: 0.0001
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: 0.0000
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: 0.0001
Epoch [47/64], Step [500/600], Loss: 0.0003
Epoch [47/64], Step [600/600], Loss: 0.0002
Epoch [48/64], Step [100/600], Loss: 0.0001
Epoch [48/64], Step [200/600], Loss: 0.0001
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: 0.0004
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: 0.0000
Epoch [49/64], Step [600/600], Loss: 0.0001
Epoch [50/64], Step [100/600], Loss: 0.0001
Epoch [50/64], Step [200/600], Loss: 0.0000
Epoch [50/64], Step [300/600], Loss: 0.0000
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: 0.0000
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: 0.0001
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: 0.0001
Epoch [52/64], Step [500/600], Loss: 0.0001
Epoch [52/64], Step [600/600], Loss: 0.0072
Epoch [53/64], Step [100/600], Loss: 0.0029
Epoch [53/64], Step [200/600], Loss: 0.0006
Epoch [53/64], Step [300/600], Loss: 0.0001
Epoch [53/64], Step [400/600], Loss: 0.0227
Epoch [53/64], Step [500/600], Loss: 0.0152
Epoch [53/64], Step [600/600], Loss: 0.0009
Epoch [54/64], Step [100/600], Loss: 0.0004
Epoch [54/64], Step [200/600], Loss: 0.0002
Epoch [54/64], Step [300/600], Loss: 0.0001
Epoch [54/64], Step [400/600], Loss: 0.0027
Epoch [54/64], Step [500/600], Loss: 0.0011
Epoch [54/64], Step [600/600], Loss: 0.0000
Epoch [55/64], Step [100/600], Loss: 0.0003
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: 0.0002
Epoch [55/64], Step [500/600], Loss: 0.0007
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: 0.0005
Epoch [56/64], Step [300/600], Loss: 0.0000
Epoch [56/64], Step [400/600], Loss: 0.0004
Epoch [56/64], Step [500/600], Loss: 0.0004
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: 0.0001
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: 0.0002
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: 0.0001
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: 0.0001
Epoch [59/64], Step [400/600], Loss: 0.0000
Epoch [59/64], Step [500/600], Loss: 0.0001
Epoch [59/64], Step [600/600], Loss: 0.0000
Epoch [60/64], Step [100/600], Loss: 0.0001
Epoch [60/64], Step [200/600], Loss: 0.0001
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: 0.0001
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: 0.0000
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: 0.0001
Epoch [63/64], Step [400/600], Loss: 0.0000
Epoch [63/64], Step [500/600], Loss: 0.0001
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: 0.0000
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 439.730 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins2011768228673171109.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