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 97055 queued and waiting for resources
srun: job 97055 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]. 1429.53 samples/sec. 179.080 ms/step.
Infer [16/40]. 1429.34 samples/sec. 179.103 ms/step.
Infer [24/40]. 1428.49 samples/sec. 179.210 ms/step.
Infer [32/40]. 1427.75 samples/sec. 179.304 ms/step.
Infer [40/40]. 1427.45 samples/sec. 179.340 ms/step.
Inference benchmark of vgg19_bn done. 1427.32 samples/sec, 179.34 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]. 10645.68 samples/sec. 24.047 ms/step.
Infer [16/40]. 10644.69 samples/sec. 24.050 ms/step.
Infer [24/40]. 10659.48 samples/sec. 24.016 ms/step.
Infer [32/40]. 10619.14 samples/sec. 24.107 ms/step.
Infer [40/40]. 10617.65 samples/sec. 24.111 ms/step.
Inference benchmark of resnet18 done. 10612.64 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]. 6506.23 samples/sec. 39.347 ms/step.
Infer [16/40]. 6471.82 samples/sec. 39.556 ms/step.
Infer [24/40]. 6457.45 samples/sec. 39.644 ms/step.
Infer [32/40]. 6460.25 samples/sec. 39.627 ms/step.
Infer [40/40]. 6462.85 samples/sec. 39.611 ms/step.
Inference benchmark of resnet34 done. 6461.34 samples/sec, 39.61 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]. 12371.58 samples/sec. 20.693 ms/step.
Infer [16/40]. 12417.44 samples/sec. 20.616 ms/step.
Infer [24/40]. 12452.44 samples/sec. 20.558 ms/step.
Infer [32/40]. 12433.82 samples/sec. 20.589 ms/step.
Infer [40/40]. 12393.57 samples/sec. 20.656 ms/step.
Inference benchmark of simplenetv1_5m_m1 done. 12388.45 samples/sec, 20.66 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": 12388.45,
        "infer_step_time": 20.656,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 5.75
    },
    {
        "model": "resnet18",
        "infer_samples_per_sec": 10612.64,
        "infer_step_time": 24.111,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 11.69
    },
    {
        "model": "resnet34",
        "infer_samples_per_sec": 6461.34,
        "infer_step_time": 39.611,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 21.8
    },
    {
        "model": "vgg19_bn",
        "infer_samples_per_sec": 1427.32,
        "infer_step_time": 179.34,
        "infer_batch_size": 256,
        "infer_img_size": 224,
        "param_count": 143.68
    }
]
Pytorch test completed in 62.579 secs

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

[workspace] $ /bin/sh -xe /tmp/jenkins14156656881992794935.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