Performance Regression or Improvement: pytorch_image_classification_benchmarks-resnet152-GPU-mean_load_model_latency_milli_secs:mean_load_model_latency_milli_secs

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Performance change found in the
test: pytorch_image_classification_benchmarks-resnet152-GPU-mean_load_model_latency_milli_secs for the metric: mean_load_model_latency_milli_secs.

For more information on how to triage the alerts, please look at
Triage performance alert issues section of the README.

Test description: Pytorch image classification on 50k images of size 224 x 224 with resnet 152 with Tesla T4 GPU.
Test link -

beam/.test-infra/jenkins/job_InferenceBenchmarkTests_Python.groovy

Line 151 in 42d0a6e

test : 'apache_beam.testing.benchmarks.inference.pytorch_image_classification_benchmarks',

Test dashboard - http://metrics.beam.apache.org/d/ZpS8Uf44z/python-ml-runinference-benchmarks?orgId=1&viewPanel=7

timestamp: Tue Nov  5 07:03:18 2024, metric_value: 133606.48
timestamp: Mon Nov  4 06:43:16 2024, metric_value: 128937.33
timestamp: Sun Nov  3 06:44:58 2024, metric_value: 128399.02
timestamp: Sat Nov  2 06:35:42 2024, metric_value: 133067.77
timestamp: Thu Oct 31 06:28:48 2024, metric_value: 127739.04 <---- Anomaly
timestamp: Wed Oct 30 06:49:59 2024, metric_value: 124361.55
timestamp: Tue Oct 29 06:32:57 2024, metric_value: 123489.72
timestamp: Mon Oct 28 07:04:25 2024, metric_value: 117813.99
timestamp: Sun Oct 27 06:45:15 2024, metric_value: 115872.63
timestamp: Sat Oct 26 06:49:19 2024, metric_value: 120449.94
timestamp: Fri Oct 25 06:53:56 2024, metric_value: 120743.23
timestamp: Thu Oct 24 06:38:39 2024, metric_value: 121856.46
timestamp: Wed Oct 23 06:36:06 2024, metric_value: 119108.21
timestamp: Tue Oct 22 06:44:10 2024, metric_value: 126356.09
timestamp: Mon Oct 21 06:32:03 2024, metric_value: 120330.78