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Results from GH action on NVIDIA_RTX4090x2
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arjunsuresh committed Nov 8, 2024
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"host_processor_interconnect": "",
"host_processor_model_name": "Intel(R) Xeon(R) w7-2495X",
"host_processors_per_node": "1",
"host_storage_capacity": "7.0T",
"host_storage_capacity": "11T",
"host_storage_type": "SSD",
"hw_notes": "",
"number_of_nodes": "1",
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1 change: 1 addition & 0 deletions open/MLCommons/code/bert-99/README.md
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This experiment is generated using the [MLCommons Collective Mind automation framework (CM)](https://github.com/mlcommons/cm4mlops).

*Check [CM MLPerf docs](https://docs.mlcommons.org/inference) for more details.*

## Host platform

* OS version: Linux-6.2.0-39-generic-x86_64-with-glibc2.29
* CPU version: x86_64
* Python version: 3.8.10 (default, Sep 11 2024, 16:02:53)
[GCC 9.4.0]
* MLCommons CM version: 3.3.4

## CM Run Command

See [CM installation guide](https://docs.mlcommons.org/inference/install/).

```bash
pip install -U cmind

cm rm cache -f

cm pull repo gateoverflow@cm4mlops --checkout=2a5e2006ff3bff8affbfe950b9b40105b7db3a38

cm run script \
--tags=app,mlperf,inference,generic,_nvidia,_bert-99,_tensorrt,_cuda,_valid,_r4.1-dev_default,_offline \
--quiet=true \
--env.CM_QUIET=yes \
--env.CM_MLPERF_IMPLEMENTATION=nvidia \
--env.CM_MLPERF_MODEL=bert-99 \
--env.CM_MLPERF_RUN_STYLE=valid \
--env.CM_MLPERF_SKIP_SUBMISSION_GENERATION=False \
--env.CM_MLPERF_BACKEND=tensorrt \
--env.CM_MLPERF_SUBMISSION_SYSTEM_TYPE=datacenter,edge \
--env.CM_MLPERF_CLEAN_ALL=True \
--env.CM_MLPERF_DEVICE=cuda \
--env.CM_MLPERF_SUBMISSION_DIVISION=closed \
--env.CM_MLPERF_USE_DOCKER=True \
--env.CM_NVIDIA_GPU_NAME=rtx_4090 \
--env.CM_HW_NAME=RTX4090x2 \
--env.CM_RUN_MLPERF_SUBMISSION_PREPROCESSOR=yes \
--env.CM_MLPERF_INFERENCE_PULL_CODE_CHANGES=yes \
--env.CM_MLPERF_INFERENCE_PULL_SRC_CHANGES=yes \
--env.OUTPUT_BASE_DIR=/home/arjun/gh_action_results \
--env.CM_MLPERF_INFERENCE_SUBMISSION_DIR=/home/arjun/gh_action_submissions \
--env.CM_MLPERF_SUBMITTER=MLCommons \
--env.CM_USE_DATASET_FROM_HOST=yes \
--env.CM_MLPERF_LOADGEN_ALL_SCENARIOS=yes \
--env.CM_MLPERF_LOADGEN_COMPLIANCE=yes \
--env.CM_MLPERF_SUBMISSION_RUN=yes \
--env.CM_RUN_MLPERF_ACCURACY=on \
--env.CM_RUN_SUBMISSION_CHECKER=yes \
--env.CM_TAR_SUBMISSION_DIR=yes \
--env.CM_MLPERF_SUBMISSION_GENERATION_STYLE=full \
--env.CM_MLPERF_INFERENCE_VERSION=4.1-dev \
--env.CM_RUN_MLPERF_INFERENCE_APP_DEFAULTS=r4.1-dev_default \
--env.CM_MLPERF_LOADGEN_ALL_MODES=yes \
--env.CM_MLPERF_INFERENCE_SOURCE_VERSION=4.1.23 \
--env.CM_MLPERF_LAST_RELEASE=v4.1 \
--env.CM_TMP_CURRENT_PATH=/home/arjun/actions-runner/_work/cm4mlops/cm4mlops \
--env.CM_TMP_PIP_VERSION_STRING= \
--env.CM_MODEL=bert-99 \
--env.CM_MLPERF_CLEAN_SUBMISSION_DIR=yes \
--env.CM_RERUN=yes \
--env.CM_MLPERF_LOADGEN_EXTRA_OPTIONS= \
--env.CM_MLPERF_LOADGEN_MODE=performance \
--env.CM_MLPERF_LOADGEN_SCENARIO=Offline \
--env.CM_MLPERF_LOADGEN_SCENARIOS,=SingleStream,Offline,Server \
--env.CM_MLPERF_LOADGEN_MODES,=performance,accuracy \
--env.CM_OUTPUT_FOLDER_NAME=valid_results \
--env.CM_DOCKER_REUSE_EXISTING_CONTAINER=yes \
--env.CM_DOCKER_DETACHED_MODE=yes \
--env.CM_MLPERF_INFERENCE_RESULTS_DIR_=/home/arjun/gh_action_results/valid_results \
--env.CM_DOCKER_CONTAINER_ID=72ac8fc556bd \
--env.CM_MLPERF_LOADGEN_COMPLIANCE_TEST=TEST05 \
--add_deps_recursive.compiler.tags=gcc \
--add_deps_recursive.coco2014-original.tags=_full \
--add_deps_recursive.coco2014-preprocessed.tags=_full \
--add_deps_recursive.imagenet-original.tags=_full \
--add_deps_recursive.imagenet-preprocessed.tags=_full \
--add_deps_recursive.openimages-original.tags=_full \
--add_deps_recursive.openimages-preprocessed.tags=_full \
--add_deps_recursive.openorca-original.tags=_full \
--add_deps_recursive.openorca-preprocessed.tags=_full \
--add_deps_recursive.get-mlperf-inference-results-dir.tags=_version.r4_1-dev \
--add_deps_recursive.get-mlperf-inference-submission-dir.tags=_version.r4_1-dev \
--add_deps_recursive.mlperf-inference-nvidia-scratch-space.tags=_version.r4_1-dev \
--adr.compiler.tags=gcc \
--adr.coco2014-original.tags=_full \
--adr.coco2014-preprocessed.tags=_full \
--adr.imagenet-original.tags=_full \
--adr.imagenet-preprocessed.tags=_full \
--adr.openimages-original.tags=_full \
--adr.openimages-preprocessed.tags=_full \
--adr.openorca-original.tags=_full \
--adr.openorca-preprocessed.tags=_full \
--adr.get-mlperf-inference-results-dir.tags=_version.r4_1-dev \
--adr.get-mlperf-inference-submission-dir.tags=_version.r4_1-dev \
--adr.mlperf-inference-nvidia-scratch-space.tags=_version.r4_1-dev \
--v=False \
--print_env=False \
--print_deps=False \
--dump_version_info=True \
--env.OUTPUT_BASE_DIR=/cm-mount/home/arjun/gh_action_results \
--env.CM_MLPERF_INFERENCE_SUBMISSION_DIR=/cm-mount/home/arjun/gh_action_submissions \
--env.MLPERF_SCRATCH_PATH=/home/cmuser/CM/repos/local/cache/e066920512fd47b7
```
*Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf (CM scripts),
you should simply reload gateoverflow@cm4mlops without checkout and clean CM cache as follows:*

```bash
cm rm repo gateoverflow@cm4mlops
cm pull repo gateoverflow@cm4mlops
cm rm cache -f

```

## Results

Platform: RTX4090x2-nvidia_original-gpu-tensorrt-vdefault-default_config

Model Precision: int8

### Accuracy Results
`F1`: `90.15674`, Required accuracy for closed division `>= 89.96526`

### Performance Results
`Samples per second`: `8204.1`
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{
"starting_weights_filename": "https://zenodo.org/record/3750364/files/bert_large_v1_1_fake_quant.onnx",
"retraining": "no",
"input_data_types": "int32",
"weight_data_types": "int8",
"weight_transformations": "quantization, affine fusion"
}
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