[GitHub] [beam] github-actions[bot] commented on issue #27077: Performance Regression or Improvement: Pytorch image classification on 50k images of size 224 x 224 with resnet 152 with Tesla T4 GPU:mean_load_model_latency_milli_secs

["github-actions[bot] (via GitHub)" <gi...@apache.org>]

github-actions[bot] commented on issue #27077:
URL: https://github.com/apache/beam/issues/27077#issuecomment-1736581900

   
     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](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/testing/analyzers/README.md#triage-performance-alert-issues).
   
   
   `Test description:` Pytorch image classification on 50k images of size 224 x 224 with resnet 152 with Tesla T4 GPU. Test link - https://github.com/apache/beam/blob/42d0a6e3564d8b9c5d912428a6de18fb22a13ac1/.test-infra/jenkins/job_InferenceBenchmarkTests_Python.groovy#L151 Test dashboard - http://metrics.beam.apache.org/d/ZpS8Uf44z/python-ml-runinference-benchmarks?orgId=1&viewPanel=7
   
   ```
   
   timestamp: Tue Sep 26 20:39:13 2023, metric_value: 230483.59 <---- Anomaly
   timestamp: Sun Sep 24 20:16:17 2023, metric_value: 171467.95
   timestamp: Sat Sep 23 20:34:54 2023, metric_value: 170864.55
   timestamp: Fri Sep 22 20:31:38 2023, metric_value: 176961.21
   timestamp: Thu Sep 21 20:42:19 2023, metric_value: 180771.67
   timestamp: Tue Sep 19 20:22:42 2023, metric_value: 198311.58
   timestamp: Mon Sep 18 20:32:00 2023, metric_value: 177070.07
   timestamp: Sun Sep 17 20:17:01 2023, metric_value: 180274.53
   timestamp: Sat Sep 16 20:16:20 2023, metric_value: 176510.54
   timestamp: Thu Sep 14 20:21:44 2023, metric_value: 182756.80
   timestamp: Wed Sep 13 20:24:00 2023, metric_value: 182646.01
   
   ```


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