Started by timer
Running as SYSTEM
Building in workspace /var/lib/jenkins/jobs/pytorch_infer/workspace
[SSH] script:
TARGETNODE=""""

module load anaconda3_gpu/4.13.0
module load cuda/11.7.0

cd pytorch_infer
rm -f infer_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 benchmark.py --model-list jenkins_list_short.txt --bench inference --channels-last --results-file infer_results_jenkins.csv"

# 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`

python transpose_results.py

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 benchmark.py --model-list jenkins_list_short.txt --bench inference --channels-last --results-file infer_results_jenkins.csv
srun: job 97349 queued and waiting for resources
srun: job 97349 has been allocated resources
Running benchmark on hydro03
Running bulk validation on these pretrained models: vgg19_bn, resnet18, resnet34, simplenetv1_5m_m1, 
Benchmarking in float32 precision. NHWC layout. torchscript disabled
Model vgg19_bn created, param count: 143678248
Running inference benchmark on vgg19_bn for 40 steps w/ input size (3, 224, 224) and batch size 256.
Infer [8/40]. 1428.30 samples/sec. 179.234 ms/step.
Infer [16/40]. 1428.36 samples/sec. 179.226 ms/step.
Infer [24/40]. 1428.27 samples/sec. 179.238 ms/step.
Infer [32/40]. 1428.39 samples/sec. 179.222 ms/step.
Infer [40/40]. 1428.19 samples/sec. 179.247 ms/step.
Inference benchmark of vgg19_bn done. 1428.02 samples/sec, 179.25 ms/step
Benchmarking in float32 precision. NHWC layout. torchscript disabled
Model resnet18 created, param count: 11689512
Running inference benchmark on resnet18 for 40 steps w/ input size (3, 224, 224) and batch size 256.
Infer [8/40]. 10649.48 samples/sec. 24.039 ms/step.
Infer [16/40]. 10652.46 samples/sec. 24.032 ms/step.
Infer [24/40]. 10647.66 samples/sec. 24.043 ms/step.
Infer [32/40]. 10612.26 samples/sec. 24.123 ms/step.
Infer [40/40]. 10618.35 samples/sec. 24.109 ms/step.
Inference benchmark of resnet18 done. 10614.47 samples/sec, 24.11 ms/step
Benchmarking in float32 precision. NHWC layout. torchscript disabled
Model resnet34 created, param count: 21797672
Running inference benchmark on resnet34 for 40 steps w/ input size (3, 224, 224) and batch size 256.
Infer [8/40]. 6512.60 samples/sec. 39.308 ms/step.
Infer [16/40]. 6475.53 samples/sec. 39.533 ms/step.
Infer [24/40]. 6467.75 samples/sec. 39.581 ms/step.
Infer [32/40]. 6478.80 samples/sec. 39.514 ms/step.
Infer [40/40]. 6467.68 samples/sec. 39.581 ms/step.
Inference benchmark of resnet34 done. 6466.09 samples/sec, 39.58 ms/step
Benchmarking in float32 precision. NHWC layout. torchscript disabled
Model simplenetv1_5m_m1 created, param count: 5752808
Running inference benchmark on simplenetv1_5m_m1 for 40 steps w/ input size (3, 224, 224) and batch size 256.
Infer [8/40]. 12465.26 samples/sec. 20.537 ms/step.
Infer [16/40]. 12429.23 samples/sec. 20.597 ms/step.
Infer [24/40]. 12410.91 samples/sec. 20.627 ms/step.
Infer [32/40]. 12391.75 samples/sec. 20.659 ms/step.
Infer [40/40]. 12363.49 samples/sec. 20.706 ms/step.
Inference benchmark of simplenetv1_5m_m1 done. 12358.38 samples/sec, 20.71 ms/step
args: Namespace(model_list='jenkins_list_short.txt', bench='inference', detail=False, results_file='infer_results_jenkins.csv', num_warm_iter=10, num_bench_iter=40, model='vgg19_bn', batch_size=256, img_size=None, input_size=None, use_train_size=False, num_classes=None, gp=None, channels_last=True, grad_checkpointing=False, amp=False, precision='float32', torchscript=False, fuser='', opt='sgd', opt_eps=None, opt_betas=None, momentum=0.9, weight_decay=0.0001, clip_grad=None, clip_mode='norm', smoothing=0.1, drop=0.0, drop_path=None, drop_block=None)
args: Namespace(model_list='jenkins_list_short.txt', bench='inference', detail=False, results_file='infer_results_jenkins.csv', num_warm_iter=10, num_bench_iter=40, model='resnet18', batch_size=256, img_size=None, input_size=None, use_train_size=False, num_classes=None, gp=None, channels_last=True, grad_checkpointing=False, amp=False, precision='float32', torchscript=False, fuser='', opt='sgd', opt_eps=None, opt_betas=None, momentum=0.9, weight_decay=0.0001, clip_grad=None, clip_mode='norm', smoothing=0.1, drop=0.0, drop_path=None, drop_block=None)
args: Namespace(model_list='jenkins_list_short.txt', bench='inference', detail=False, results_file='infer_results_jenkins.csv', num_warm_iter=10, num_bench_iter=40, model='resnet34', batch_size=256, img_size=None, input_size=None, use_train_size=False, num_classes=None, gp=None, channels_last=True, grad_checkpointing=False, amp=False, precision='float32', torchscript=False, fuser='', opt='sgd', opt_eps=None, opt_betas=None, momentum=0.9, weight_decay=0.0001, clip_grad=None, clip_mode='norm', smoothing=0.1, drop=0.0, drop_path=None, drop_block=None)
args: Namespace(model_list='jenkins_list_short.txt', bench='inference', detail=False, results_file='infer_results_jenkins.csv', num_warm_iter=10, num_bench_iter=40, model='simplenetv1_5m_m1', batch_size=256, img_size=None, input_size=None, use_train_size=False, num_classes=None, gp=None, channels_last=True, grad_checkpointing=False, amp=False, precision='float32', torchscript=False, fuser='', opt='sgd', opt_eps=None, opt_betas=None, momentum=0.9, weight_decay=0.0001, clip_grad=None, clip_mode='norm', smoothing=0.1, drop=0.0, drop_path=None, drop_block=None)
--result
[
    {
        "model": "simplenetv1_5m_m1",
        "infer_samples_per_sec": 12358.38,
        "infer_step_time": 20.706,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 5.75
    },
    {
        "model": "resnet18",
        "infer_samples_per_sec": 10614.47,
        "infer_step_time": 24.109,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 11.69
    },
    {
        "model": "resnet34",
        "infer_samples_per_sec": 6466.09,
        "infer_step_time": 39.581,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 21.8
    },
    {
        "model": "vgg19_bn",
        "infer_samples_per_sec": 1428.02,
        "infer_step_time": 179.247,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 143.68
    }
]
Pytorch test completed in 63.122 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins8193175101099129657.sh
+ scp 'HYDRO_REMOTE:~svchydrojenkins/pytorch_infer/time.txt' /var/lib/jenkins/jobs/pytorch_infer/workspace
+ scp 'HYDRO_REMOTE:~svchydrojenkins/pytorch_infer/infer_results_jenkins.csv' /var/lib/jenkins/jobs/pytorch_infer/workspace
Recording plot data
Saving plot series data from: /var/lib/jenkins/jobs/pytorch_infer/workspace/time.txt
Finished: SUCCESS