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Started by user Jeremy Enos
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 96829 queued and waiting for resources
srun: job 96829 has been allocated resources
Running benchmark on hydro01
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]. 1401.41 samples/sec. 182.673 ms/step.
Infer [16/40]. 1402.10 samples/sec. 182.584 ms/step.
Infer [24/40]. 1402.68 samples/sec. 182.508 ms/step.
Infer [32/40]. 1403.68 samples/sec. 182.378 ms/step.
Infer [40/40]. 1403.63 samples/sec. 182.385 ms/step.
Inference benchmark of vgg19_bn done. 1403.46 samples/sec, 182.38 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]. 10657.94 samples/sec. 24.020 ms/step.
Infer [16/40]. 10663.60 samples/sec. 24.007 ms/step.
Infer [24/40]. 10656.50 samples/sec. 24.023 ms/step.
Infer [32/40]. 10638.96 samples/sec. 24.063 ms/step.
Infer [40/40]. 10637.92 samples/sec. 24.065 ms/step.
Inference benchmark of resnet18 done. 10633.12 samples/sec, 24.07 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]. 6478.67 samples/sec. 39.514 ms/step.
Infer [16/40]. 6474.66 samples/sec. 39.539 ms/step.
Infer [24/40]. 6478.42 samples/sec. 39.516 ms/step.
Infer [32/40]. 6473.90 samples/sec. 39.543 ms/step.
Infer [40/40]. 6476.75 samples/sec. 39.526 ms/step.
Inference benchmark of resnet34 done. 6474.93 samples/sec, 39.53 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]. 12456.24 samples/sec. 20.552 ms/step.
Infer [16/40]. 12469.88 samples/sec. 20.529 ms/step.
Infer [24/40]. 12477.34 samples/sec. 20.517 ms/step.
Infer [32/40]. 12476.68 samples/sec. 20.518 ms/step.
Infer [40/40]. 12439.46 samples/sec. 20.580 ms/step.
Inference benchmark of simplenetv1_5m_m1 done. 12432.99 samples/sec, 20.58 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": 12432.99,
        "infer_step_time": 20.58,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 5.75
    },
    {
        "model": "resnet18",
        "infer_samples_per_sec": 10633.12,
        "infer_step_time": 24.065,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 11.69
    },
    {
        "model": "resnet34",
        "infer_samples_per_sec": 6474.93,
        "infer_step_time": 39.526,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 21.8
    },
    {
        "model": "vgg19_bn",
        "infer_samples_per_sec": 1403.46,
        "infer_step_time": 182.385,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 143.68
    }
]
Pytorch test completed in 176.370 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins16088803039225338053.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
Sending e-mails to: [email protected]
Finished: SUCCESS