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: Sun Jan 19 06:41:50 2025, metric_value: 151014.03
timestamp: Sat Jan 18 06:59:57 2025, metric_value: 153103.71
timestamp: Fri Jan 17 06:38:08 2025, metric_value: 156579.33
timestamp: Thu Jan 16 06:37:10 2025, metric_value: 145487.92 <---- Anomaly
timestamp: Wed Jan 15 06:41:40 2025, metric_value: 83105.55
timestamp: Tue Jan 14 06:39:24 2025, metric_value: 135890.03
timestamp: Mon Jan 13 06:34:22 2025, metric_value: 114564.80
timestamp: Sun Jan 12 06:42:25 2025, metric_value: 116487.21
timestamp: Sat Jan 11 06:49:59 2025, metric_value: 162788.62
timestamp: Fri Jan 10 06:39:12 2025, metric_value: 134560.38
timestamp: Thu Jan  9 06:35:49 2025, metric_value: 124447.99
timestamp: Wed Jan  8 06:50:47 2025, metric_value: 136275.64
timestamp: Tue Jan  7 06:55:36 2025, metric_value: 105295.79
timestamp: Mon Jan  6 06:33:51 2025, metric_value: 108228.74