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arjunsuresh authored Oct 21, 2024
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TBD
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| Model | Scenario | Accuracy | Throughput | Latency (in ms) |
|---------|------------|------------|--------------|-------------------|
| bert-99 | offline | 90.876 | 85.447 | - |
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{
"starting_weights_filename": "https://armi.in/files/fp32/model.pytorch",
"retraining": "no",
"input_data_types": "fp32",
"weight_data_types": "fp32",
"weight_transformations": "none"
}
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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-6.1.112-1.el9.elrepo.x86_64-x86_64-with-glibc2.35
* CPU version: x86_64
* Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0]
* MLCommons CM version: 3.2.2

## 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 mlcommons@cm4mlops --checkout=bcde11676cc734f2398028a039fc1c94ae9fa81a

cm run script \
--tags=run-mlperf,inference,_r4.1-dev \
--model=bert-99 \
--implementation=reference \
--framework=pytorch \
--category=edge \
--scenario=Offline \
--execution_mode=valid \
--device=cuda \
--quiet
```
*Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf (CM scripts),
you should simply reload mlcommons@cm4mlops without checkout and clean CM cache as follows:*

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

```

## Results

Platform: 666dfb9811e4-reference-gpu-pytorch-cu124

Model Precision: fp32

### Accuracy Results
`F1`: `90.87602`, Required accuracy for closed division `>= 89.96526`

### Performance Results
`Samples per second`: `85.4467`
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