Auto-merge updates from auto-update branch

open/MLCommons/measurements/gh_ubuntu-latest-reference-cpu-tvm-onnx_v1.19.2-default_config/README.md

@@ -0,0 +1,3 @@
+| Model    | Scenario   |   Accuracy |   Throughput | Latency (in ms)   |
+|----------|------------|------------|--------------|-------------------|
+| resnet50 | offline    |         80 |        1.741 | -                 |

open/MLCommons/measurements/gh_ubuntu-latest-reference-cpu-tvm-onnx_v1.19.2-default_config/resnet50/offline/README.md

@@ -0,0 +1,44 @@
+*Check [CM MLPerf docs](https://docs.mlcommons.org/inference) for more details.*
+
+## Host platform
+
+* OS version: Linux-6.8.0-1020-azure-x86_64-with-glibc2.39
+* CPU version: x86_64
+* Python version: 3.9.21 (main, Dec 12 2024, 19:08:08) 
+[GCC 13.2.0]
+* MLC version: unknown
+
+## CM Run Command
+
+See [CM installation guide](https://docs.mlcommons.org/inference/install/).
+
+```bash
+pip install -U mlcflow
+
+mlc rm cache -f
+
+mlc pull repo sujik18@mlperf-automations --checkout=d49ffdad99d90e9df862cef6bc80d631dba2aca0
+
+
+```
+*Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf,
+ you should simply reload sujik18@mlperf-automations without checkout and clean MLC cache as follows:*
+
+```bash
+mlc rm repo sujik18@mlperf-automations
+mlc pull repo sujik18@mlperf-automations
+mlc rm cache -f
+
+```
+
+## Results
+
+Platform: gh_ubuntu-latest-reference-cpu-tvm-onnx_v1.19.2-default_config
+
+Model Precision: fp32
+
+### Accuracy Results 
+`acc`: `80.0`, Required accuracy for closed division `>= 75.6954`
+
+### Performance Results 
+`Samples per second`: `1.74129`

open/MLCommons/measurements/gh_ubuntu-latest-reference-cpu-tvm-onnx_v1.19.2-default_config/resnet50/offline/accuracy_console.out

@@ -0,0 +1,11 @@
+python3 python/main.py --profile resnet50-onnxruntime --model "/home/runner/MLC/repos/local/cache/get-tvm-model_167e4a91/resnet50_v1.onnx" --dataset-path /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_aa275843 --output "/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_dda6a2c1/test_results/gh_ubuntu-latest-reference-cpu-tvm-onnx-v1.19.2-default_config/resnet50/offline/accuracy" --backend tvm --scenario Offline --max-batchsize 1 --count 5 --threads 4 --user_conf /home/runner/MLC/repos/sujik18@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/69da754000874929a9db4383d9019958.conf --accuracy --use_preprocessed_dataset --cache_dir /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_aa275843 --dataset-list /home/runner/MLC/repos/local/cache/extract-file_0b9989cb/val.txt
+INFO:main:Namespace(dataset='imagenet', dataset_path='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_aa275843', dataset_list='/home/runner/MLC/repos/local/cache/extract-file_0b9989cb/val.txt', data_format=None, profile='resnet50-onnxruntime', scenario='Offline', max_batchsize=1, model='/home/runner/MLC/repos/local/cache/get-tvm-model_167e4a91/resnet50_v1.onnx', output='/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_dda6a2c1/test_results/gh_ubuntu-latest-reference-cpu-tvm-onnx-v1.19.2-default_config/resnet50/offline/accuracy', inputs=None, outputs=['ArgMax:0'], backend='tvm', device=None, model_name='resnet50', threads=4, qps=None, cache=0, cache_dir='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_aa275843', preprocessed_dir=None, use_preprocessed_dataset=True, accuracy=True, find_peak_performance=False, debug=False, user_conf='/home/runner/MLC/repos/sujik18@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/69da754000874929a9db4383d9019958.conf', audit_conf='audit.config', time=None, count=5, performance_sample_count=None, max_latency=None, samples_per_query=8)
+INFO:imagenet:Loading 5 preprocessed images using 4 threads
+INFO:imagenet:loaded 5 images, cache=0, already_preprocessed=True, took=0.0sec
+TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_167e4a91/model-tvm.so
+INFO:main:starting TestScenario.Offline
+TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_167e4a91/model-tvm.so
+TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_167e4a91/model-tvm.so
+TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_167e4a91/model-tvm.so
+TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_167e4a91/model-tvm.so
+TestScenario.Offline qps=1.74, mean=1.7544, time=2.880, acc=80.000%, queries=5, tiles=50.0:1.4731,80.0:1.7929,90.0:2.3219,95.0:2.5864,99.0:2.7979,99.9:2.8456

open/MLCommons/measurements/gh_ubuntu-latest-reference-cpu-tvm-onnx_v1.19.2-default_config/resnet50/offline/cpu_info.json

@@ -0,0 +1,26 @@
+{
+  "MLC_HOST_CPU_WRITE_PROTECT_SUPPORT": "yes",
+  "MLC_HOST_CPU_MICROCODE": "0xffffffff",
+  "MLC_HOST_CPU_FPU_SUPPORT": "yes",
+  "MLC_HOST_CPU_FPU_EXCEPTION_SUPPORT": "yes",
+  "MLC_HOST_CPU_BUGS": "sysret_ss_attrs null_seg spectre_v1 spectre_v2 spec_store_bypass srso",
+  "MLC_HOST_CPU_TLB_SIZE": "2560 4K pages",
+  "MLC_HOST_CPU_CFLUSH_SIZE": "64",
+  "MLC_HOST_CPU_ARCHITECTURE": "x86_64",
+  "MLC_HOST_CPU_TOTAL_CORES": "4",
+  "MLC_HOST_CPU_ON_LINE_CPUS_LIST": "0-3",
+  "MLC_HOST_CPU_VENDOR_ID": "AuthenticAMD",
+  "MLC_HOST_CPU_MODEL_NAME": "AMD EPYC 7763 64-Core Processor",
+  "MLC_HOST_CPU_FAMILY": "25",
+  "MLC_HOST_CPU_THREADS_PER_CORE": "2",
+  "MLC_HOST_CPU_PHYSICAL_CORES_PER_SOCKET": "2",
+  "MLC_HOST_CPU_SOCKETS": "1",
+  "MLC_HOST_CPU_L1D_CACHE_SIZE": "64 KiB (2 instances)",
+  "MLC_HOST_CPU_L1I_CACHE_SIZE": "64 KiB (2 instances)",
+  "MLC_HOST_CPU_L2_CACHE_SIZE": "1 MiB (2 instances)",
+  "MLC_HOST_CPU_L3_CACHE_SIZE": "32 MiB (1 instance)",
+  "MLC_HOST_CPU_NUMA_NODES": "1",
+  "MLC_HOST_CPU_TOTAL_LOGICAL_CORES": "4",
+  "MLC_HOST_MEMORY_CAPACITY": "16G",
+  "MLC_HOST_DISK_CAPACITY": "159G"
+}

open/MLCommons/measurements/gh_ubuntu-latest-reference-cpu-tvm-onnx_v1.19.2-default_config/resnet50/offline/gh_ubuntu-latest-reference-cpu-tvm-onnx_v1.19.2-default_config.json

@@ -0,0 +1,7 @@
+{
+  "starting_weights_filename": "https://zenodo.org/record/4735647/files/resnet50_v1.onnx",
+  "retraining": "no",
+  "input_data_types": "fp32",
+  "weight_data_types": "fp32",
+  "weight_transformations": "no"
+}