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 96054 queued and waiting for resources
srun: job 96054 has been allocated resources
Running benchmark on hydro04
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]. 1383.87 samples/sec. 184.988 ms/step.
Infer [16/40]. 1382.64 samples/sec. 185.153 ms/step.
Infer [24/40]. 1381.43 samples/sec. 185.315 ms/step.
Infer [32/40]. 1380.47 samples/sec. 185.444 ms/step.
Infer [40/40]. 1379.86 samples/sec. 185.526 ms/step.
Inference benchmark of vgg19_bn done. 1379.75 samples/sec, 185.53 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]. 10683.80 samples/sec. 23.962 ms/step.
Infer [16/40]. 10680.40 samples/sec. 23.969 ms/step.
Infer [24/40]. 10673.61 samples/sec. 23.984 ms/step.
Infer [32/40]. 10653.46 samples/sec. 24.030 ms/step.
Infer [40/40]. 10651.42 samples/sec. 24.034 ms/step.
Inference benchmark of resnet18 done. 10647.62 samples/sec, 24.03 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]. 6520.67 samples/sec. 39.260 ms/step.
Infer [16/40]. 6491.80 samples/sec. 39.434 ms/step.
Infer [24/40]. 6484.88 samples/sec. 39.476 ms/step.
Infer [32/40]. 6489.46 samples/sec. 39.449 ms/step.
Infer [40/40]. 6487.27 samples/sec. 39.462 ms/step.
Inference benchmark of resnet34 done. 6485.77 samples/sec, 39.46 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]. 14035.67 samples/sec. 18.239 ms/step.
Infer [16/40]. 14021.65 samples/sec. 18.257 ms/step.
Infer [24/40]. 14018.27 samples/sec. 18.262 ms/step.
Infer [32/40]. 14022.80 samples/sec. 18.256 ms/step.
Infer [40/40]. 14013.96 samples/sec. 18.268 ms/step.
Inference benchmark of simplenetv1_5m_m1 done. 14007.76 samples/sec, 18.27 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": 14007.76,
"infer_step_time": 18.268,
"infer_batch_size": 256,
"infer_img_size": 224,
"param_count": 5.75
},
{
"model": "resnet18",
"infer_samples_per_sec": 10647.62,
"infer_step_time": 24.034,
"infer_batch_size": 256,
"infer_img_size": 224,
"param_count": 11.69
},
{
"model": "resnet34",
"infer_samples_per_sec": 6485.77,
"infer_step_time": 39.462,
"infer_batch_size": 256,
"infer_img_size": 224,
"param_count": 21.8
},
{
"model": "vgg19_bn",
"infer_samples_per_sec": 1379.75,
"infer_step_time": 185.526,
"infer_batch_size": 256,
"infer_img_size": 224,
"param_count": 143.68
}
]
Pytorch test completed in 126.380 secs
[SSH] completed
[SSH] exit-status: 0
[workspace] $ /bin/sh -xe /tmp/jenkins6720512242649496245.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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