Performance Regression or Improvement: pytorch_image_classification_benchmarks-resnet152-mean_inference_batch_latency_micro_secs:mean_inference_batch_latency_micro_secs

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Performance change found in the
test: pytorch_image_classification_benchmarks-resnet152-mean_inference_batch_latency_micro_secs for the metric: mean_inference_batch_latency_micro_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.
Test link -

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

Line 63 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=2

timestamp: Thu Feb 27 05:07:34 2025, metric_value: 6881032.25
timestamp: Wed Feb 26 04:57:15 2025, metric_value: 7385899.48
timestamp: Tue Feb 25 04:57:29 2025, metric_value: 7206908.92
timestamp: Mon Feb 24 04:56:34 2025, metric_value: 7312654.40 <---- Anomaly
timestamp: Sun Feb 23 04:59:23 2025, metric_value: 6898585.18
timestamp: Sat Feb 22 04:54:49 2025, metric_value: 6666102.06
timestamp: Fri Feb 21 05:00:08 2025, metric_value: 6868875.73
timestamp: Thu Feb 20 04:56:26 2025, metric_value: 6865516.33
timestamp: Wed Feb 19 05:01:25 2025, metric_value: 6821945.78
timestamp: Tue Feb 18 04:53:47 2025, metric_value: 6867330.15
timestamp: Mon Feb 17 04:58:22 2025, metric_value: 6801348.45
timestamp: Sun Feb 16 04:52:50 2025, metric_value: 7231609.13
timestamp: Sat Feb 15 04:53:41 2025, metric_value: 7272425.53
timestamp: Fri Feb 14 05:00:32 2025, metric_value: 7413785.43