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Merge pull request #78 from Catking14/mlperf-inference-results-scc24
MLPerf inference results scc24 NTHU
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TBD
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| Model | Scenario | Accuracy | Throughput | Latency (in ms) |
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|---------------------|------------|-----------------------|--------------|-------------------|
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| stable-diffusion-xl | offline | (16.40356, 235.76925) | 0.529 | - |
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{
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"starting_weights_filename": "https://github.com/mlcommons/inference/tree/master/text_to_image#download-model",
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"retraining": "no",
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"input_data_types": "fp32",
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"weight_data_types": "fp32",
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"weight_transformations": "no"
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}
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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-6.8.0-47-generic-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: 3.1.0
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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=9bdfff592f2d9607660fb20125ec620ebedc3758
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cm run script \
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--tags=run-mlperf,inference,_r4.1-dev,_short,_scc24-base \
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--model=sdxl \
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--implementation=reference \
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--framework=pytorch \
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--category=datacenter \
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--scenario=Offline \
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--execution_mode=test \
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--device=cuda \
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--quiet \
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--precision=float16 \
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--clean
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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: Kuai-Kuai_702-reference-gpu-pytorch-v2.5.1-scc24-base_cu124
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Model Precision: fp32
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### Accuracy Results
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`CLIP_SCORE`: `16.40356`, Required accuracy for closed division `>= 31.68632` and `<= 31.81332`
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`FID_SCORE`: `235.76925`, Required accuracy for closed division `>= 23.01086` and `<= 23.95008`
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### Performance Results
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`Samples per second`: `0.528528`

open/NTHU/measurements/Kuai-Kuai_702-reference-gpu-pytorch-v2.5.1-scc24-base_cu124/stable-diffusion-xl/offline/accuracy_console.out

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