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Merge pull request #70 from woonyee28/mlperf-inference-results-scc24
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Results on system 4xH100
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arjunsuresh authored Oct 25, 2024
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TBD
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| Model | Scenario | Accuracy | Throughput | Latency (in ms) |
|---------------------|------------|-----------------------|--------------|-------------------|
| stable-diffusion-xl | offline | (15.70418, 233.56896) | 2.667 | - |
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{
"starting_weights_filename": "https://github.com/mlcommons/cm4mlops/blob/main/script/get-ml-model-stable-diffusion/_cm.json#L174",
"retraining": "no",
"input_data_types": "int32",
"weight_data_types": "int8",
"weight_transformations": "quantization, affine fusion"
}
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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-6.5.0-27-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: 2.4.0

## 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=114709c8f6dbefa9ce5f8a599d55b349b5464bca

cm run script \
--tags=run-mlperf,inference,_r4.1-dev,_short,_scc24-main \
--model=sdxl \
--implementation=nvidia \
--framework=tensorrt \
--category=datacenter \
--scenario=Offline \
--execution_mode=test \
--device=cuda \
--quiet \
--clean \
--batch-size=4 \
--target_qps=40
```
*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: 8297ae0eca20-nvidia-gpu-TensorRT-scc24-main

Model Precision: int8

### Accuracy Results
`CLIP_SCORE`: `15.70418`, Required accuracy for closed division `>= 31.68632` and `<= 31.81332`
`FID_SCORE`: `233.56896`, Required accuracy for closed division `>= 23.01086` and `<= 23.95008`

### Performance Results
`Samples per second`: `2.66695`
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