Merge pull request #60 from luoyueyuguang/mlperf-inference-results-scc24

Mlperf inference results scc24, scc123 with gpu and cpu

Author: arjunsuresh

open/scc123-Shanxi_University/code/bert-99/README.md

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+TBD

open/scc123-Shanxi_University/measurements/b7828419c93b-reference-gpu-pytorch-cu124/README.md

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+| Model   | Scenario   |   Accuracy |   Throughput | Latency (in ms)   |
+|---------|------------|------------|--------------|-------------------|
+| bert-99 | offline    |     90.876 |       83.471 | -                 |

open/scc123-Shanxi_University/measurements/b7828419c93b-reference-gpu-pytorch-cu124/bert-99/offline/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-6.1.110-1.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: 3.2.6
+
+## 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 luoyueyuguang@cm4mlops --checkout=6dbb26a3da6b8ebdbc96be3be3a0e9817d3b6d26
+
+cm run script \
+	--tags=run-mlperf,inference,_r4.1-dev,_short \
+	--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 luoyueyuguang@cm4mlops without checkout and clean CM cache as follows:*
+
+```bash
+cm rm repo luoyueyuguang@cm4mlops
+cm pull repo luoyueyuguang@cm4mlops
+cm rm cache -f
+
+```
+
+## Results
+
+Platform: b7828419c93b-reference-gpu-pytorch-cu124
+
+Model Precision: fp32
+
+### Accuracy Results 
+`F1`: `90.87602`, Required accuracy for closed division `>= 89.96526`
+
+### Performance Results 
+`Samples per second`: `83.4708`

open/scc123-Shanxi_University/measurements/b7828419c93b-reference-gpu-pytorch-cu124/bert-99/offline/accuracy_console.out

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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"
+}