Auto update

open/Community/measurements/default-reference-cpu-tvm-onnx_v1.19.2-default_config/README.md

@@ -1,3 +1,3 @@
 | Model    | Scenario   |   Accuracy |   Throughput | Latency (in ms)   |
 |----------|------------|------------|--------------|-------------------|
-| resnet50 | offline    |         80 |          1.7 | -                 |
+| resnet50 | offline    |         80 |        1.737 | -                 |

open/Community/measurements/default-reference-cpu-tvm-onnx_v1.19.2-default_config/resnet50/offline/README.md

@@ -17,16 +17,16 @@ pip install -U mlcflow
 
 mlc rm cache -f
 
-mlc pull repo GATEOverflow@mlperf-automations --checkout=4622ce7112da201e6dead55ac4c402dc54aa0da5
+mlc pull repo mlcommons@mlperf-automations --checkout=f84d23738b1edab748d9b9e4004b995bc143886c
 
 
 ```
 *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
 
 ```
@@ -41,4 +41,4 @@ Model Precision: fp32
 `acc`: `80.0`, Required accuracy for closed division `>= 75.6954`
 
 ### Performance Results 
-`Samples per second`: `1.6996`
+`Samples per second`: `1.73704`

open/Community/measurements/default-reference-cpu-tvm-onnx_v1.19.2-default_config/resnet50/offline/accuracy_console.out

@@ -1,11 +1,11 @@
-python3 python/main.py --profile resnet50-onnxruntime --model "/home/runner/MLC/repos/local/cache/get-tvm-model_9fa9c04a/resnet50_v1.onnx" --dataset-path /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_9626ca44 --output "/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_698f46f1/test_results/default-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/GATEOverflow@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/4cbec86190594ff18d2ad34b91a7ca4b.conf --accuracy --use_preprocessed_dataset --cache_dir /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_9626ca44 --dataset-list /home/runner/MLC/repos/local/cache/extract-file_b033c29b/val.txt
-INFO:main:Namespace(dataset='imagenet', dataset_path='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_9626ca44', dataset_list='/home/runner/MLC/repos/local/cache/extract-file_b033c29b/val.txt', data_format=None, profile='resnet50-onnxruntime', scenario='Offline', max_batchsize=1, model='/home/runner/MLC/repos/local/cache/get-tvm-model_9fa9c04a/resnet50_v1.onnx', output='/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_698f46f1/test_results/default-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_9626ca44', 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/4cbec86190594ff18d2ad34b91a7ca4b.conf', audit_conf='audit.config', time=None, count=5, performance_sample_count=None, max_latency=None, samples_per_query=8)
+python3 python/main.py --profile resnet50-onnxruntime --model "/home/runner/MLC/repos/local/cache/get-tvm-model_829bf2f3/resnet50_v1.onnx" --dataset-path /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_24a37a05 --output "/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_43e11165/test_results/default-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/mlcommons@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/9352645028624658bc76cae37124d8e5.conf --accuracy --use_preprocessed_dataset --cache_dir /home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_24a37a05 --dataset-list /home/runner/MLC/repos/local/cache/extract-file_78774a62/val.txt
+INFO:main:Namespace(dataset='imagenet', dataset_path='/home/runner/MLC/repos/local/cache/get-preprocessed-dataset-imagenet_24a37a05', dataset_list='/home/runner/MLC/repos/local/cache/extract-file_78774a62/val.txt', data_format=None, profile='resnet50-onnxruntime', scenario='Offline', max_batchsize=1, model='/home/runner/MLC/repos/local/cache/get-tvm-model_829bf2f3/resnet50_v1.onnx', output='/home/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_43e11165/test_results/default-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_24a37a05', 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/9352645028624658bc76cae37124d8e5.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_9fa9c04a/model-tvm.so
+TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_829bf2f3/model-tvm.so
 INFO:main:starting TestScenario.Offline
-TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_9fa9c04a/model-tvm.so
-TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_9fa9c04a/model-tvm.so
-TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_9fa9c04a/model-tvm.so
-TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_9fa9c04a/model-tvm.so
-TestScenario.Offline qps=1.72, mean=1.7674, time=2.899, acc=80.000%, queries=5, tiles=50.0:1.4999,80.0:1.7954,90.0:2.3313,95.0:2.5993,99.0:2.8137,99.9:2.8619
+TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_829bf2f3/model-tvm.so
+TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_829bf2f3/model-tvm.so
+TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_829bf2f3/model-tvm.so
+TVM: loading model /home/runner/MLC/repos/local/cache/get-tvm-model_829bf2f3/model-tvm.so
+TestScenario.Offline qps=1.74, mean=1.7556, time=2.872, acc=80.000%, queries=5, tiles=50.0:1.4972,80.0:1.7776,90.0:2.3093,95.0:2.5752,99.0:2.7879,99.9:2.8357