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Results from R50 GH action on ubuntu-latest
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| Model | Scenario | Accuracy | Throughput | Latency (in ms) |
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|----------|------------|------------|--------------|-------------------|
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| resnet50 | offline | 76 | 20.951 | - |
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| resnet50 | offline | 76 | 19.712 | - |

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

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mlc rm cache -f
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mlc pull repo mlcommons@mlperf-automations --checkout=5bf50be4373fb0090d23434194a8b19a619546b9
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mlc pull repo mlcommons@mlperf-automations --checkout=c11bfd0cfdb2546949e2ac2498f258b900763156
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```
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`acc`: `76.0`, Required accuracy for closed division `>= 75.6954`
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### Performance Results
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`Samples per second`: `20.9513`
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`Samples per second`: `19.7117`
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python3 python/main.py --profile resnet50-tf --model "/home/runner/MLC/repos/local/cache/download-file_ml-model-resnet_e9cc4238/resnet50_v1.pb" --dataset-path /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_6d9b3263 --output "/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_fc6af5a2/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/8e831d2d2ff74466b63e628a765ed1e2.conf --accuracy --use_preprocessed_dataset --cache_dir /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_6d9b3263 --dataset-list /home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_01aab75a/val.txt
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INFO:main:Namespace(dataset='imagenet', dataset_path='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_6d9b3263', dataset_list='/home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_01aab75a/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_e9cc4238/resnet50_v1.pb', output='/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_fc6af5a2/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_6d9b3263', 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/8e831d2d2ff74466b63e628a765ed1e2.conf', audit_conf='audit.config', time=None, count=500, performance_sample_count=None, max_latency=None, samples_per_query=8)
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2025-09-14 18:17:57.277675: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
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2025-09-14 18:17:57.328598: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
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python3 python/main.py --profile resnet50-tf --model "/home/runner/MLC/repos/local/cache/download-file_ml-model-resnet_2bd81d58/resnet50_v1.pb" --dataset-path /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_9f8a2e0b --output "/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_9515623d/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/642cc58645684c14996a5cb732e54b60.conf --accuracy --use_preprocessed_dataset --cache_dir /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_9f8a2e0b --dataset-list /home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_d5f2d018/val.txt
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INFO:main:Namespace(dataset='imagenet', dataset_path='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_9f8a2e0b', dataset_list='/home/runner/MLC/repos/local/cache/extract-file_imagenet-aux-da_d5f2d018/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_2bd81d58/resnet50_v1.pb', output='/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_9515623d/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_9f8a2e0b', 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/642cc58645684c14996a5cb732e54b60.conf', audit_conf='audit.config', time=None, count=500, performance_sample_count=None, max_latency=None, samples_per_query=8)
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2025-09-14 18:38:08.301147: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
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2025-09-14 18:38:08.348714: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
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To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
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2025-09-14 18:17:58.587142: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
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2025-09-14 18:38:09.695355: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
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INFO:imagenet:Loading 500 preprocessed images using 4 threads
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INFO:imagenet:loaded 500 images, cache=0, already_preprocessed=True, took=0.0sec
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WARNING:tensorflow:From /home/runner/MLC/repos/local/cache/get-git-repo_inference-src_e0b3e64f/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.
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WARNING:tensorflow:From /home/runner/MLC/repos/local/cache/get-git-repo_inference-src_bac1dd97/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.
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Instructions for updating:
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Use tf.gfile.GFile.
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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.
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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.
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Instructions for updating:
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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.
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2025-09-14 18:18:53.021238: 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)
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2025-09-14 18:39:05.521385: 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)
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WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
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I0000 00:00:1757873933.092190 4008 mlir_graph_optimization_pass.cc:437] MLIR V1 optimization pass is not enabled
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I0000 00:00:1757875145.647691 4010 mlir_graph_optimization_pass.cc:437] MLIR V1 optimization pass is not enabled
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INFO:main:starting TestScenario.Offline
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TestScenario.Offline qps=0.67, mean=14.9658, time=23.774, acc=76.000%, queries=16, tiles=50.0:15.2893,80.0:22.5874,90.0:23.4731,95.0:23.6128,99.0:23.6713,99.9:23.6845
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TestScenario.Offline qps=0.67, mean=15.0804, time=23.842, acc=76.000%, queries=16, tiles=50.0:15.2228,80.0:22.8847,90.0:23.6296,95.0:23.7003,99.0:23.7369,99.9:23.7451

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