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

[AnandInguva]

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 - https://github.com/apache/beam/blob/42d0a6e3564d8b9c5d912428a6de18fb22a13ac1/.test-infra/jenkins/job_InferenceBenchmarkTests_Python.groovy#L63
Test dashboard - http://metrics.beam.apache.org/d/ZpS8Uf44z/python-ml-runinference-benchmarks?orgId=1&viewPanel=2

timestamp: Sun Oct  8 18:43:04 2023, metric_value: 6501936.25
timestamp: Sat Oct  7 18:43:10 2023, metric_value: 6344657.03
timestamp: Sat Oct  7 13:24:25 2023, metric_value: 6401279.27 <---- Anomaly
timestamp: Wed Oct  4 20:11:00 2023, metric_value: 6585933.45
timestamp: Thu Sep 28 18:46:29 2023, metric_value: 6451933.21
timestamp: Wed Sep 27 18:50:01 2023, metric_value: 6630534.66
timestamp: Tue Sep 26 18:55:08 2023, metric_value: 6343025.40
timestamp: Mon Sep 25 18:48:10 2023, metric_value: 6639268.07
timestamp: Sun Sep 24 18:45:32 2023, metric_value: 6666197.60
timestamp: Sat Sep 23 18:48:47 2023, metric_value: 6563930.16
timestamp: Fri Sep 22 18:50:09 2023, metric_value: 6441102.41
timestamp: Thu Sep 21 18:54:32 2023, metric_value: 6316440.33
timestamp: Wed Sep 20 19:40:13 2023, metric_value: 6591127.22