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

[github-actions]

Performance change found in the
test: pytorch_image_classification_benchmarks-resnet152-GPU-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 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?from=now-90d&to=now&viewPanel=2

timestamp: Mon Dec 22 10:04:44 2025, metric_value: 191575.26
timestamp: Sun Dec 21 09:39:48 2025, metric_value: 204125.69
timestamp: Sat Dec 20 09:57:17 2025, metric_value: 198510.44 <---- Anomaly
timestamp: Fri Dec 19 10:33:47 2025, metric_value: 132726.05
timestamp: Thu Dec 18 10:07:15 2025, metric_value: 121803.73
timestamp: Wed Dec 17 09:54:09 2025, metric_value: 167888.23
timestamp: Mon Dec 15 10:09:35 2025, metric_value: 144108.35
timestamp: Sun Dec 14 10:27:23 2025, metric_value: 182123.93
timestamp: Fri Dec 12 09:51:24 2025, metric_value: 183383.95
timestamp: Thu Dec 11 09:49:56 2025, metric_value: 118511.65
timestamp: Wed Dec 10 09:44:14 2025, metric_value: 104418.46
timestamp: Tue Dec  9 09:24:34 2025, metric_value: 156529.60
timestamp: Mon Dec  8 09:24:37 2025, metric_value: 171624.29