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 97833 queued and waiting for resources
srun: job 97833 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]. 1429.28 samples/sec. 179.112 ms/step.
Infer [16/40]. 1429.88 samples/sec. 179.036 ms/step.
Infer [24/40]. 1430.40 samples/sec. 178.971 ms/step.
Infer [32/40]. 1429.90 samples/sec. 179.034 ms/step.
Infer [40/40]. 1429.23 samples/sec. 179.117 ms/step.
Inference benchmark of vgg19_bn done. 1428.99 samples/sec, 179.12 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]. 10658.28 samples/sec. 24.019 ms/step.
Infer [16/40]. 10654.05 samples/sec. 24.028 ms/step.
Infer [24/40]. 10647.87 samples/sec. 24.042 ms/step.
Infer [32/40]. 10623.44 samples/sec. 24.098 ms/step.
Infer [40/40]. 10619.42 samples/sec. 24.107 ms/step.
Inference benchmark of resnet18 done. 10615.49 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]. 6492.53 samples/sec. 39.430 ms/step.
Infer [16/40]. 6455.37 samples/sec. 39.657 ms/step.
Infer [24/40]. 6464.81 samples/sec. 39.599 ms/step.
Infer [32/40]. 6458.70 samples/sec. 39.636 ms/step.
Infer [40/40]. 6456.90 samples/sec. 39.648 ms/step.
Inference benchmark of resnet34 done. 6455.41 samples/sec, 39.65 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]. 12492.49 samples/sec. 20.492 ms/step.
Infer [16/40]. 12478.74 samples/sec. 20.515 ms/step.
Infer [24/40]. 12462.35 samples/sec. 20.542 ms/step.
Infer [32/40]. 12407.21 samples/sec. 20.633 ms/step.
Infer [40/40]. 12372.64 samples/sec. 20.691 ms/step.
Inference benchmark of simplenetv1_5m_m1 done. 12367.55 samples/sec, 20.69 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": 12367.55,
"infer_step_time": 20.691,
"infer_batch_size": 256,
"infer_img_size": 224,
"param_count": 5.75
},
{
"model": "resnet18",
"infer_samples_per_sec": 10615.49,
"infer_step_time": 24.107,
"infer_batch_size": 256,
"infer_img_size": 224,
"param_count": 11.69
},
{
"model": "resnet34",
"infer_samples_per_sec": 6455.41,
"infer_step_time": 39.648,
"infer_batch_size": 256,
"infer_img_size": 224,
"param_count": 21.8
},
{
"model": "vgg19_bn",
"infer_samples_per_sec": 1428.99,
"infer_step_time": 179.117,
"infer_batch_size": 256,
"infer_img_size": 224,
"param_count": 143.68
}
]
Pytorch test completed in 62.718 secs
[SSH] completed
[SSH] exit-status: 0
[workspace] $ /bin/sh -xe /tmp/jenkins9094339242012030452.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
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