Results from self hosted Github actions - NVIDIARTX4090

open/MLCommons/measurements/d024cdf0eec2-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/README.md

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+| Model               | Scenario   | Accuracy              |   Throughput | Latency (in ms)   |
+|---------------------|------------|-----------------------|--------------|-------------------|
+| stable-diffusion-xl | offline    | (15.18026, 235.62721) |        0.369 | -                 |

open/MLCommons/measurements/d024cdf0eec2-reference-gpu-pytorch_v2.5.0-scc24-base_cu124/stable-diffusion-xl/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-6.2.0-39-generic-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: 3.0.1
+
+## 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 gateoverflow@cm4mlops --checkout=c22bfc0a413f04eec707cbe860ba9cf818439c8a
+
+cm run script \
+	--tags=app,mlperf,inference,generic,_reference,_sdxl,_pytorch,_cuda,_test,_r4.1-dev_default,_float16,_offline \
+	--quiet=true \
+	--env.CM_MLPERF_MODEL_SDXL_DOWNLOAD_TO_HOST=yes \
+	--env.CM_QUIET=yes \
+	--env.CM_MLPERF_IMPLEMENTATION=reference \
+	--env.CM_MLPERF_MODEL=sdxl \
+	--env.CM_MLPERF_RUN_STYLE=test \
+	--env.CM_MLPERF_SKIP_SUBMISSION_GENERATION=False \
+	--env.CM_MLPERF_BACKEND=pytorch \
+	--env.CM_MLPERF_SUBMISSION_SYSTEM_TYPE=datacenter \
+	--env.CM_MLPERF_CLEAN_ALL=True \
+	--env.CM_MLPERF_DEVICE=cuda \
+	--env.CM_MLPERF_USE_DOCKER=True \
+	--env.CM_MLPERF_MODEL_PRECISION=float16 \
+	--env.OUTPUT_BASE_DIR=/home/arjun/scc_gh_action_results \
+	--env.CM_MLPERF_LOADGEN_SCENARIO=Offline \
+	--env.CM_MLPERF_INFERENCE_SUBMISSION_DIR=/home/arjun/scc_gh_action_submissions \
+	--env.CM_MLPERF_INFERENCE_VERSION=4.1-dev \
+	--env.CM_RUN_MLPERF_INFERENCE_APP_DEFAULTS=r4.1-dev_default \
+	--env.CM_MLPERF_SUBMISSION_GENERATION_STYLE=short \
+	--env.CM_MLPERF_SUT_NAME_RUN_CONFIG_SUFFIX4=scc24-base \
+	--env.CM_DOCKER_IMAGE_NAME=scc24-reference \
+	--env.CM_MLPERF_LOADGEN_ALL_MODES=yes \
+	--env.CM_MLPERF_LAST_RELEASE=v4.1 \
+	--env.CM_TMP_CURRENT_PATH=/home/arjun/actions-runner/_work/cm4mlops/cm4mlops \
+	--env.CM_TMP_PIP_VERSION_STRING= \
+	--env.CM_MODEL=sdxl \
+	--env.CM_MLPERF_LOADGEN_COMPLIANCE=no \
+	--env.CM_MLPERF_CLEAN_SUBMISSION_DIR=yes \
+	--env.CM_RERUN=yes \
+	--env.CM_MLPERF_LOADGEN_EXTRA_OPTIONS= \
+	--env.CM_MLPERF_LOADGEN_MODE=performance \
+	--env.CM_MLPERF_LOADGEN_SCENARIOS,=Offline \
+	--env.CM_MLPERF_LOADGEN_MODES,=performance,accuracy \
+	--env.CM_OUTPUT_FOLDER_NAME=test_results \
+	--add_deps_recursive.get-mlperf-inference-results-dir.tags=_version.r4_1-dev \
+	--add_deps_recursive.get-mlperf-inference-submission-dir.tags=_version.r4_1-dev \
+	--add_deps_recursive.mlperf-inference-nvidia-scratch-space.tags=_version.r4_1-dev \
+	--add_deps_recursive.submission-checker.tags=_short-run \
+	--add_deps_recursive.coco2014-preprocessed.tags=_size.50,_with-sample-ids \
+	--add_deps_recursive.coco2014-dataset.tags=_size.50,_with-sample-ids \
+	--add_deps_recursive.nvidia-preprocess-data.extra_cache_tags=scc24-base \
+	--v=False \
+	--print_env=False \
+	--print_deps=False \
+	--dump_version_info=True \
+	--env.OUTPUT_BASE_DIR=/home/arjun/scc_gh_action_results \
+	--env.CM_MLPERF_INFERENCE_SUBMISSION_DIR=/home/arjun/scc_gh_action_submissions \
+	--env.SDXL_CHECKPOINT_PATH=/home/cmuser/CM/repos/local/cache/6be1f30ecbde4c4e/stable_diffusion_fp16
+```
+*Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf (CM scripts),
+ you should simply reload gateoverflow@cm4mlops without checkout and clean CM cache as follows:*
+
+```bash
+cm rm repo gateoverflow@cm4mlops
+cm pull repo gateoverflow@cm4mlops
+cm rm cache -f
+
+```
+
+## Results
+
+Platform: d024cdf0eec2-reference-gpu-pytorch_v2.5.0-scc24-base_cu124
+
+Model Precision: fp32
+
+### Accuracy Results 
+`CLIP_SCORE`: `15.18026`, Required accuracy for closed division `>= 31.68632` and `<= 31.81332`
+`FID_SCORE`: `235.62721`, Required accuracy for closed division `>= 23.01086` and `<= 23.95008`
+
+### Performance Results 
+`Samples per second`: `0.36945`