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 98605 queued and waiting for resources
srun: job 98605 has been allocated resources
Running benchmark on hydro05
Epoch [1/64], Step [100/600], Loss: 0.2761
Epoch [1/64], Step [200/600], Loss: 0.1135
Epoch [1/64], Step [300/600], Loss: 0.0752
Epoch [1/64], Step [400/600], Loss: 0.0658
Epoch [1/64], Step [500/600], Loss: 0.0945
Epoch [1/64], Step [600/600], Loss: 0.0354
Epoch [2/64], Step [100/600], Loss: 0.1101
Epoch [2/64], Step [200/600], Loss: 0.0260
Epoch [2/64], Step [300/600], Loss: 0.0535
Epoch [2/64], Step [400/600], Loss: 0.0383
Epoch [2/64], Step [500/600], Loss: 0.0388
Epoch [2/64], Step [600/600], Loss: 0.0709
Epoch [3/64], Step [100/600], Loss: 0.0566
Epoch [3/64], Step [200/600], Loss: 0.0295
Epoch [3/64], Step [300/600], Loss: 0.0580
Epoch [3/64], Step [400/600], Loss: 0.0149
Epoch [3/64], Step [500/600], Loss: 0.0149
Epoch [3/64], Step [600/600], Loss: 0.0285
Epoch [4/64], Step [100/600], Loss: 0.0278
Epoch [4/64], Step [200/600], Loss: 0.0949
Epoch [4/64], Step [300/600], Loss: 0.0171
Epoch [4/64], Step [400/600], Loss: 0.0395
Epoch [4/64], Step [500/600], Loss: 0.0064
Epoch [4/64], Step [600/600], Loss: 0.0829
Epoch [5/64], Step [100/600], Loss: 0.0133
Epoch [5/64], Step [200/600], Loss: 0.0276
Epoch [5/64], Step [300/600], Loss: 0.0142
Epoch [5/64], Step [400/600], Loss: 0.0259
Epoch [5/64], Step [500/600], Loss: 0.0025
Epoch [5/64], Step [600/600], Loss: 0.0151
Epoch [6/64], Step [100/600], Loss: 0.0192
Epoch [6/64], Step [200/600], Loss: 0.0338
Epoch [6/64], Step [300/600], Loss: 0.0557
Epoch [6/64], Step [400/600], Loss: 0.0206
Epoch [6/64], Step [500/600], Loss: 0.0364
Epoch [6/64], Step [600/600], Loss: 0.0070
Epoch [7/64], Step [100/600], Loss: 0.0210
Epoch [7/64], Step [200/600], Loss: 0.0209
Epoch [7/64], Step [300/600], Loss: 0.0299
Epoch [7/64], Step [400/600], Loss: 0.0182
Epoch [7/64], Step [500/600], Loss: 0.0115
Epoch [7/64], Step [600/600], Loss: 0.0029
Epoch [8/64], Step [100/600], Loss: 0.0241
Epoch [8/64], Step [200/600], Loss: 0.0575
Epoch [8/64], Step [300/600], Loss: 0.0239
Epoch [8/64], Step [400/600], Loss: 0.0037
Epoch [8/64], Step [500/600], Loss: 0.0129
Epoch [8/64], Step [600/600], Loss: 0.0293
Epoch [9/64], Step [100/600], Loss: 0.0042
Epoch [9/64], Step [200/600], Loss: 0.0112
Epoch [9/64], Step [300/600], Loss: 0.0056
Epoch [9/64], Step [400/600], Loss: 0.0057
Epoch [9/64], Step [500/600], Loss: 0.0055
Epoch [9/64], Step [600/600], Loss: 0.0222
Epoch [10/64], Step [100/600], Loss: 0.0044
Epoch [10/64], Step [200/600], Loss: 0.0090
Epoch [10/64], Step [300/600], Loss: 0.0132
Epoch [10/64], Step [400/600], Loss: 0.0033
Epoch [10/64], Step [500/600], Loss: 0.0041
Epoch [10/64], Step [600/600], Loss: 0.0043
Epoch [11/64], Step [100/600], Loss: 0.0038
Epoch [11/64], Step [200/600], Loss: 0.0008
Epoch [11/64], Step [300/600], Loss: 0.0055
Epoch [11/64], Step [400/600], Loss: 0.0275
Epoch [11/64], Step [500/600], Loss: 0.0022
Epoch [11/64], Step [600/600], Loss: 0.0229
Epoch [12/64], Step [100/600], Loss: 0.0026
Epoch [12/64], Step [200/600], Loss: 0.0121
Epoch [12/64], Step [300/600], Loss: 0.0003
Epoch [12/64], Step [400/600], Loss: 0.0018
Epoch [12/64], Step [500/600], Loss: 0.0018
Epoch [12/64], Step [600/600], Loss: 0.0135
Epoch [13/64], Step [100/600], Loss: 0.0045
Epoch [13/64], Step [200/600], Loss: 0.0009
Epoch [13/64], Step [300/600], Loss: 0.0040
Epoch [13/64], Step [400/600], Loss: 0.0025
Epoch [13/64], Step [500/600], Loss: 0.0379
Epoch [13/64], Step [600/600], Loss: 0.0027
Epoch [14/64], Step [100/600], Loss: 0.0016
Epoch [14/64], Step [200/600], Loss: 0.0016
Epoch [14/64], Step [300/600], Loss: 0.0006
Epoch [14/64], Step [400/600], Loss: 0.0296
Epoch [14/64], Step [500/600], Loss: 0.0097
Epoch [14/64], Step [600/600], Loss: 0.0029
Epoch [15/64], Step [100/600], Loss: 0.0025
Epoch [15/64], Step [200/600], Loss: 0.0017
Epoch [15/64], Step [300/600], Loss: 0.0014
Epoch [15/64], Step [400/600], Loss: 0.0070
Epoch [15/64], Step [500/600], Loss: 0.0007
Epoch [15/64], Step [600/600], Loss: 0.0026
Epoch [16/64], Step [100/600], Loss: 0.0005
Epoch [16/64], Step [200/600], Loss: 0.0031
Epoch [16/64], Step [300/600], Loss: 0.0014
Epoch [16/64], Step [400/600], Loss: 0.0015
Epoch [16/64], Step [500/600], Loss: 0.0147
Epoch [16/64], Step [600/600], Loss: 0.0007
Epoch [17/64], Step [100/600], Loss: 0.0028
Epoch [17/64], Step [200/600], Loss: 0.0007
Epoch [17/64], Step [300/600], Loss: 0.0006
Epoch [17/64], Step [400/600], Loss: 0.0007
Epoch [17/64], Step [500/600], Loss: 0.0053
Epoch [17/64], Step [600/600], Loss: 0.0062
Epoch [18/64], Step [100/600], Loss: 0.0016
Epoch [18/64], Step [200/600], Loss: 0.0016
Epoch [18/64], Step [300/600], Loss: 0.0012
Epoch [18/64], Step [400/600], Loss: 0.0149
Epoch [18/64], Step [500/600], Loss: 0.0027
Epoch [18/64], Step [600/600], Loss: 0.0008
Epoch [19/64], Step [100/600], Loss: 0.0001
Epoch [19/64], Step [200/600], Loss: 0.0005
Epoch [19/64], Step [300/600], Loss: 0.0071
Epoch [19/64], Step [400/600], Loss: 0.0001
Epoch [19/64], Step [500/600], Loss: 0.0077
Epoch [19/64], Step [600/600], Loss: 0.0086
Epoch [20/64], Step [100/600], Loss: 0.0028
Epoch [20/64], Step [200/600], Loss: 0.0012
Epoch [20/64], Step [300/600], Loss: 0.0004
Epoch [20/64], Step [400/600], Loss: 0.0006
Epoch [20/64], Step [500/600], Loss: 0.0029
Epoch [20/64], Step [600/600], Loss: 0.0067
Epoch [21/64], Step [100/600], Loss: 0.0006
Epoch [21/64], Step [200/600], Loss: 0.0004
Epoch [21/64], Step [300/600], Loss: 0.0007
Epoch [21/64], Step [400/600], Loss: 0.0006
Epoch [21/64], Step [500/600], Loss: 0.0045
Epoch [21/64], Step [600/600], Loss: 0.0012
Epoch [22/64], Step [100/600], Loss: 0.0098
Epoch [22/64], Step [200/600], Loss: 0.0001
Epoch [22/64], Step [300/600], Loss: 0.0037
Epoch [22/64], Step [400/600], Loss: 0.0012
Epoch [22/64], Step [500/600], Loss: 0.0007
Epoch [22/64], Step [600/600], Loss: 0.0003
Epoch [23/64], Step [100/600], Loss: 0.0014
Epoch [23/64], Step [200/600], Loss: 0.0001
Epoch [23/64], Step [300/600], Loss: 0.0001
Epoch [23/64], Step [400/600], Loss: 0.0036
Epoch [23/64], Step [500/600], Loss: 0.0017
Epoch [23/64], Step [600/600], Loss: 0.0002
Epoch [24/64], Step [100/600], Loss: 0.0004
Epoch [24/64], Step [200/600], Loss: 0.0048
Epoch [24/64], Step [300/600], Loss: 0.0030
Epoch [24/64], Step [400/600], Loss: 0.0020
Epoch [24/64], Step [500/600], Loss: 0.0010
Epoch [24/64], Step [600/600], Loss: 0.0026
Epoch [25/64], Step [100/600], Loss: 0.0028
Epoch [25/64], Step [200/600], Loss: 0.0019
Epoch [25/64], Step [300/600], Loss: 0.0010
Epoch [25/64], Step [400/600], Loss: 0.0003
Epoch [25/64], Step [500/600], Loss: 0.0043
Epoch [25/64], Step [600/600], Loss: 0.0006
Epoch [26/64], Step [100/600], Loss: 0.0003
Epoch [26/64], Step [200/600], Loss: 0.0001
Epoch [26/64], Step [300/600], Loss: 0.0000
Epoch [26/64], Step [400/600], Loss: 0.0018
Epoch [26/64], Step [500/600], Loss: 0.0006
Epoch [26/64], Step [600/600], Loss: 0.0037
Epoch [27/64], Step [100/600], Loss: 0.0003
Epoch [27/64], Step [200/600], Loss: 0.0003
Epoch [27/64], Step [300/600], Loss: 0.0007
Epoch [27/64], Step [400/600], Loss: 0.0016
Epoch [27/64], Step [500/600], Loss: 0.0037
Epoch [27/64], Step [600/600], Loss: 0.0010
Epoch [28/64], Step [100/600], Loss: 0.0001
Epoch [28/64], Step [200/600], Loss: 0.0001
Epoch [28/64], Step [300/600], Loss: 0.0002
Epoch [28/64], Step [400/600], Loss: 0.0001
Epoch [28/64], Step [500/600], Loss: 0.0003
Epoch [28/64], Step [600/600], Loss: 0.0000
Epoch [29/64], Step [100/600], Loss: 0.0001
Epoch [29/64], Step [200/600], Loss: 0.0004
Epoch [29/64], Step [300/600], Loss: 0.0001
Epoch [29/64], Step [400/600], Loss: 0.0004
Epoch [29/64], Step [500/600], Loss: 0.0239
Epoch [29/64], Step [600/600], Loss: 0.0002
Epoch [30/64], Step [100/600], Loss: 0.0084
Epoch [30/64], Step [200/600], Loss: 0.0002
Epoch [30/64], Step [300/600], Loss: 0.0000
Epoch [30/64], Step [400/600], Loss: 0.0002
Epoch [30/64], Step [500/600], Loss: 0.0003
Epoch [30/64], Step [600/600], Loss: 0.0009
Epoch [31/64], Step [100/600], Loss: 0.0010
Epoch [31/64], Step [200/600], Loss: 0.0003
Epoch [31/64], Step [300/600], Loss: 0.0001
Epoch [31/64], Step [400/600], Loss: 0.0001
Epoch [31/64], Step [500/600], Loss: 0.0046
Epoch [31/64], Step [600/600], Loss: 0.0007
Epoch [32/64], Step [100/600], Loss: 0.0002
Epoch [32/64], Step [200/600], Loss: 0.0000
Epoch [32/64], Step [300/600], Loss: 0.0002
Epoch [32/64], Step [400/600], Loss: 0.0004
Epoch [32/64], Step [500/600], Loss: 0.0003
Epoch [32/64], Step [600/600], Loss: 0.0003
Epoch [33/64], Step [100/600], Loss: 0.0001
Epoch [33/64], Step [200/600], Loss: 0.0003
Epoch [33/64], Step [300/600], Loss: 0.0000
Epoch [33/64], Step [400/600], Loss: 0.0001
Epoch [33/64], Step [500/600], Loss: 0.0001
Epoch [33/64], Step [600/600], Loss: 0.0000
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: 0.0002
Epoch [34/64], Step [400/600], Loss: 0.0002
Epoch [34/64], Step [500/600], Loss: 0.0004
Epoch [34/64], Step [600/600], Loss: 0.0000
Epoch [35/64], Step [100/600], Loss: 0.0001
Epoch [35/64], Step [200/600], Loss: 0.0001
Epoch [35/64], Step [300/600], Loss: 0.0001
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: 0.0000
Epoch [36/64], Step [100/600], Loss: 0.0001
Epoch [36/64], Step [200/600], Loss: 0.0000
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: 0.0000
Epoch [37/64], Step [100/600], Loss: 0.0001
Epoch [37/64], Step [200/600], Loss: 0.0001
Epoch [37/64], Step [300/600], Loss: 0.0000
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: 0.0000
Epoch [38/64], Step [200/600], Loss: 0.0001
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: 0.0000
Epoch [38/64], Step [600/600], Loss: 0.0001
Epoch [39/64], Step [100/600], Loss: 0.0000
Epoch [39/64], Step [200/600], Loss: 0.0000
Epoch [39/64], Step [300/600], Loss: 0.0000
Epoch [39/64], Step [400/600], Loss: 0.0001
Epoch [39/64], Step [500/600], Loss: 0.0000
Epoch [39/64], Step [600/600], Loss: 0.0002
Epoch [40/64], Step [100/600], Loss: 0.0086
Epoch [40/64], Step [200/600], Loss: 0.0041
Epoch [40/64], Step [300/600], Loss: 0.0016
Epoch [40/64], Step [400/600], Loss: 0.0036
Epoch [40/64], Step [500/600], Loss: 0.0000
Epoch [40/64], Step [600/600], Loss: 0.0011
Epoch [41/64], Step [100/600], Loss: 0.0026
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: 0.0015
Epoch [41/64], Step [500/600], Loss: 0.0002
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: 0.0001
Epoch [42/64], Step [400/600], Loss: 0.0002
Epoch [42/64], Step [500/600], Loss: 0.0006
Epoch [42/64], Step [600/600], Loss: 0.0000
Epoch [43/64], Step [100/600], Loss: 0.0004
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: 0.0000
Epoch [43/64], Step [600/600], Loss: 0.0001
Epoch [44/64], Step [100/600], Loss: 0.0000
Epoch [44/64], Step [200/600], Loss: 0.0002
Epoch [44/64], Step [300/600], Loss: 0.0003
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: 0.0001
Epoch [45/64], Step [100/600], Loss: 0.0000
Epoch [45/64], Step [200/600], Loss: 0.0000
Epoch [45/64], Step [300/600], Loss: 0.0001
Epoch [45/64], Step [400/600], Loss: 0.0003
Epoch [45/64], Step [500/600], Loss: 0.0002
Epoch [45/64], Step [600/600], Loss: 0.0001
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: 0.0001
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: 0.0001
Epoch [47/64], Step [100/600], Loss: 0.0001
Epoch [47/64], Step [200/600], Loss: 0.0002
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: 0.0000
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: 0.0001
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: 0.0000
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: 0.0000
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: 0.0000
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: 0.0000
Epoch [50/64], Step [400/600], Loss: 0.0000
Epoch [50/64], Step [500/600], Loss: 0.0000
Epoch [50/64], Step [600/600], Loss: 0.0000
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: 0.0417
Epoch [51/64], Step [500/600], Loss: 0.0005
Epoch [51/64], Step [600/600], Loss: 0.0085
Epoch [52/64], Step [100/600], Loss: 0.0001
Epoch [52/64], Step [200/600], Loss: 0.0004
Epoch [52/64], Step [300/600], Loss: 0.0003
Epoch [52/64], Step [400/600], Loss: 0.0004
Epoch [52/64], Step [500/600], Loss: 0.0001
Epoch [52/64], Step [600/600], Loss: 0.0002
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: 0.0002
Epoch [53/64], Step [400/600], Loss: 0.0002
Epoch [53/64], Step [500/600], Loss: 0.0008
Epoch [53/64], Step [600/600], Loss: 0.0030
Epoch [54/64], Step [100/600], Loss: 0.0060
Epoch [54/64], Step [200/600], Loss: 0.0000
Epoch [54/64], Step [300/600], Loss: 0.0001
Epoch [54/64], Step [400/600], Loss: 0.0002
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: 0.0000
Epoch [55/64], Step [300/600], Loss: 0.0001
Epoch [55/64], Step [400/600], Loss: 0.0000
Epoch [55/64], Step [500/600], Loss: 0.0000
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: 0.0000
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: 0.0001
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: 0.0000
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: 0.0001
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: 0.0001
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: 0.0000
Epoch [60/64], Step [500/600], Loss: 0.0000
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: 0.0000
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: 0.0000
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: 0.0000
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: 0.0001
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 371.899 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins10541114391838761694.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