Results from R50 GH action on ubuntu-latest

Author: mlcommons-bot

open/MLCommons/measurements/gh_ubuntu-latest_x86-reference-cpu-tf_v2.20.0-default_config/README.md

@@ -1,3 +1,3 @@
 | Model    | Scenario   |   Accuracy |   Throughput | Latency (in ms)   |
 |----------|------------|------------|--------------|-------------------|
-| resnet50 | offline    |         76 |       20.771 | -                 |
+| resnet50 | offline    |         76 |       21.185 | -                 |

open/MLCommons/measurements/gh_ubuntu-latest_x86-reference-cpu-tf_v2.20.0-default_config/resnet50/offline/README.md

@@ -16,16 +16,16 @@ pip install -U mlcflow
 
 mlc rm cache -f
 
-mlc pull repo GATEOverflow@mlperf-automations --checkout=485fa219f8396805ae83de198dea312608ecdb4f
+mlc pull repo mlcommons@mlperf-automations --checkout=9b5a4d3d98da73f02d54b4f1aac84c109a6a6472
 
 
 ```
 *Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf,
- you should simply reload GATEOverflow@mlperf-automations without checkout and clean MLC cache as follows:*
+ you should simply reload mlcommons@mlperf-automations without checkout and clean MLC cache as follows:*
 
 ```bash
-mlc rm repo GATEOverflow@mlperf-automations
-mlc pull repo GATEOverflow@mlperf-automations
+mlc rm repo mlcommons@mlperf-automations
+mlc pull repo mlcommons@mlperf-automations
 mlc rm cache -f
 
 ```
@@ -40,4 +40,4 @@ Model Precision: fp32
 `acc`: `76.0`, Required accuracy for closed division `>= 75.6954`
 
 ### Performance Results 
-`Samples per second`: `20.7707`
+`Samples per second`: `21.185`

open/MLCommons/measurements/gh_ubuntu-latest_x86-reference-cpu-tf_v2.20.0-default_config/resnet50/offline/accuracy_console.out

@@ -1,12 +1,12 @@
-python3 python/main.py --profile resnet50-tf --model "/home/runner/MLC/repos/local/cache/download-file_ml-model-resnet_d5689841/resnet50_v1.pb" --dataset-path /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_d8658594 --output "/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_9a370563/test_results/gh_ubuntu-latest x86-reference-cpu-tf-v2.20.0-default_config/resnet50/offline/accuracy" --scenario Offline --count 500 --threads 4 --user_conf /home/runner/MLC/repos/GATEOverflow@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/13155f7abdb04882824fb7a337fbc475.conf --accuracy --use_preprocessed_dataset --cache_dir /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_d8658594 --dataset-list /home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_e449eff6/val.txt
-INFO:main:Namespace(dataset='imagenet', dataset_path='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_d8658594', dataset_list='/home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_e449eff6/val.txt', data_format=None, profile='resnet50-tf', scenario='Offline', max_batchsize=32, model='/home/runner/MLC/repos/local/cache/download-file_ml-model-resnet_d5689841/resnet50_v1.pb', output='/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_9a370563/test_results/gh_ubuntu-latest x86-reference-cpu-tf-v2.20.0-default_config/resnet50/offline/accuracy', inputs=['input_tensor:0'], outputs=['ArgMax:0'], backend='tensorflow', device=None, model_name='resnet50', threads=4, qps=None, cache=0, cache_dir='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_d8658594', preprocessed_dir=None, use_preprocessed_dataset=True, accuracy=True, find_peak_performance=False, debug=False, user_conf='/home/runner/MLC/repos/GATEOverflow@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/13155f7abdb04882824fb7a337fbc475.conf', audit_conf='audit.config', time=None, count=500, performance_sample_count=None, max_latency=None, samples_per_query=8)
-2025-09-14 11:48:27.082848: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
-2025-09-14 11:48:27.128079: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+python3 python/main.py --profile resnet50-tf --model "/home/runner/MLC/repos/local/cache/download-file_ml-model-resnet_f43c2d73/resnet50_v1.pb" --dataset-path /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_0656bd0e --output "/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_bb96a6e3/test_results/gh_ubuntu-latest x86-reference-cpu-tf-v2.20.0-default_config/resnet50/offline/accuracy" --scenario Offline --count 500 --threads 4 --user_conf /home/runner/MLC/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/6a157bc406984d0a8fff33a626e0a931.conf --accuracy --use_preprocessed_dataset --cache_dir /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_0656bd0e --dataset-list /home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_b3583a31/val.txt
+INFO:main:Namespace(dataset='imagenet', dataset_path='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_0656bd0e', dataset_list='/home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_b3583a31/val.txt', data_format=None, profile='resnet50-tf', scenario='Offline', max_batchsize=32, model='/home/runner/MLC/repos/local/cache/download-file_ml-model-resnet_f43c2d73/resnet50_v1.pb', output='/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_bb96a6e3/test_results/gh_ubuntu-latest x86-reference-cpu-tf-v2.20.0-default_config/resnet50/offline/accuracy', inputs=['input_tensor:0'], outputs=['ArgMax:0'], backend='tensorflow', device=None, model_name='resnet50', threads=4, qps=None, cache=0, cache_dir='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_0656bd0e', preprocessed_dir=None, use_preprocessed_dataset=True, accuracy=True, find_peak_performance=False, debug=False, user_conf='/home/runner/MLC/repos/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/6a157bc406984d0a8fff33a626e0a931.conf', audit_conf='audit.config', time=None, count=500, performance_sample_count=None, max_latency=None, samples_per_query=8)
+2025-09-14 16:19:53.687262: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
+2025-09-14 16:19:53.731524: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
 To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-2025-09-14 11:48:28.303777: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
+2025-09-14 16:19:54.913829: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
 INFO:imagenet:Loading 500 preprocessed images using 4 threads
 INFO:imagenet:loaded 500 images, cache=0, already_preprocessed=True, took=0.0sec
-WARNING:tensorflow:From /home/runner/MLC/repos/local/cache/get-git-repo_inference-src_aec1600b/inference/vision/classification_and_detection/python/backend_tf.py:55: FastGFile.__init__ (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.
+WARNING:tensorflow:From /home/runner/MLC/repos/local/cache/get-git-repo_inference-src_77b77372/inference/vision/classification_and_detection/python/backend_tf.py:55: FastGFile.__init__ (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.
 Instructions for updating:
 Use tf.gfile.GFile.
 WARNING:tensorflow:From /opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/tensorflow/python/tools/strip_unused_lib.py:84: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
@@ -15,8 +15,8 @@ This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/mi
 WARNING:tensorflow:From /opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/tensorflow/python/tools/optimize_for_inference_lib.py:138: remove_training_nodes (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
 Instructions for updating:
 This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2.
-2025-09-14 11:49:24.861654: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)
+2025-09-14 16:20:50.402501: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)
 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
-I0000 00:00:1757850564.949305    4009 mlir_graph_optimization_pass.cc:437] MLIR V1 optimization pass is not enabled
+I0000 00:00:1757866850.487353    4015 mlir_graph_optimization_pass.cc:437] MLIR V1 optimization pass is not enabled
 INFO:main:starting TestScenario.Offline
-TestScenario.Offline qps=0.66, mean=15.2746, time=24.217, acc=76.000%, queries=16, tiles=50.0:15.2845,80.0:23.4807,90.0:23.8937,95.0:24.0677,99.0:24.0686,99.9:24.0688
+TestScenario.Offline qps=0.68, mean=14.9327, time=23.661, acc=76.000%, queries=16, tiles=50.0:15.0896,80.0:23.0130,90.0:23.4861,95.0:23.5513,99.0:23.5661,99.9:23.5695