Auto-merge updates from auto-update branch

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 |       21.101 | -                 |
+| resnet50 | offline    |         76 |       21.032 | -                 |

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=525d36468c61affa1c49bc324cdd741890d5fbf7
+mlc pull repo mlcommons@mlperf-automations --checkout=591a5c664394d3553e039d5252d17163c0d7a0b1
 
 
 ```
 *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`: `21.1009`
+`Samples per second`: `21.0316`

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_3e3ec4f3/resnet50_v1.pb" --dataset-path /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_ed5cbc80 --output "/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_68e6bf6a/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/320b2a92249f4d9eb3005a548dc1a72e.conf --accuracy --use_preprocessed_dataset --cache_dir /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_ed5cbc80 --dataset-list /home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_e2b2f609/val.txt
-INFO:main:Namespace(dataset='imagenet', dataset_path='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_ed5cbc80', dataset_list='/home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_e2b2f609/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_3e3ec4f3/resnet50_v1.pb', output='/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_68e6bf6a/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_ed5cbc80', 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/320b2a92249f4d9eb3005a548dc1a72e.conf', audit_conf='audit.config', time=None, count=500, performance_sample_count=None, max_latency=None, samples_per_query=8)
-2025-09-18 21:57:49.782430: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
-2025-09-18 21:57:49.826802: 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_fcecf9c8/resnet50_v1.pb" --dataset-path /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_077a6cd0 --output "/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_a884e825/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/f138a500d85042ac98a4f868bb2fc645.conf --accuracy --use_preprocessed_dataset --cache_dir /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_077a6cd0 --dataset-list /home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_9798347b/val.txt
+INFO:main:Namespace(dataset='imagenet', dataset_path='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_077a6cd0', dataset_list='/home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_9798347b/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_fcecf9c8/resnet50_v1.pb', output='/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_a884e825/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_077a6cd0', 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/f138a500d85042ac98a4f868bb2fc645.conf', audit_conf='audit.config', time=None, count=500, performance_sample_count=None, max_latency=None, samples_per_query=8)
+2025-09-21 08:05:51.594139: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
+2025-09-21 08:05:51.639104: 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.
-2025-09-18 21:57:50.986538: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
+2025-09-21 08:05:52.807021: 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_a3ded83c/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_c57762f4/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.
-2025-09-18 21:58:45.384220: 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)
+2025-09-21 08:06:52.086199: 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 00:00:1758232725.454856    3986 mlir_graph_optimization_pass.cc:437] MLIR V1 optimization pass is not enabled
+I0000 00:00:1758442012.171106    3984 mlir_graph_optimization_pass.cc:437] MLIR V1 optimization pass is not enabled
 INFO:main:starting TestScenario.Offline
-TestScenario.Offline qps=0.68, mean=14.9425, time=23.690, acc=76.000%, queries=16, tiles=50.0:15.0745,80.0:22.7829,90.0:23.4821,95.0:23.5503,99.0:23.6117,99.9:23.6255
+TestScenario.Offline qps=0.67, mean=15.0514, time=23.858, acc=76.000%, queries=16, tiles=50.0:15.1868,80.0:22.7970,90.0:23.6159,95.0:23.7344,99.0:23.7624,99.9:23.7687