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Merge pull request #63 from matthewc2003/mlperf-inference-results-scc24
scc107 MLPerf submission
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open/scc107/code/bert-99/README.md

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TBD
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| Model | Scenario | Accuracy | Throughput | Latency (in ms) |
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|---------|------------|------------|--------------|-------------------|
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| bert-99 | offline | 90.8749 | 5.422 | - |
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This experiment is generated using the [MLCommons Collective Mind automation framework (CM)](https://github.com/mlcommons/cm4mlops).
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*Check [CM MLPerf docs](https://docs.mlcommons.org/inference) for more details.*
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## Host platform
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* OS version: Linux-6.8.0-1016-gcp-x86_64-with-glibc2.35
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* CPU version: x86_64
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* Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0]
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* MLCommons CM version: 3.2.7
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## CM Run Command
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See [CM installation guide](https://docs.mlcommons.org/inference/install/).
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```bash
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pip install -U cmind
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cm rm cache -f
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cm pull repo mlcommons@cm4mlops --checkout=77882c5c9b87459e1ae38c796f7a443506074cdd
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cm run script \
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= \
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```
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*Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf (CM scripts),
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you should simply reload mlcommons@cm4mlops without checkout and clean CM cache as follows:*
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```bash
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cm rm repo mlcommons@cm4mlops
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cm pull repo mlcommons@cm4mlops
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cm rm cache -f
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```
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## Results
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Platform: mlperf2-reference-cpu-pytorch_v2.5.0-default_config
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Model Precision: fp32
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### Accuracy Results
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`F1`: `90.87487`, Required accuracy for closed division `>= 89.96526`
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### Performance Results
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`Samples per second`: `5.42229`

open/scc107/measurements/mlperf2-reference-cpu-pytorch_v2.5.0-default_config/bert-99/offline/accuracy_console.out

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