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Merge pull request #5 from mlcommons/dev
Major update with CM automation for the latest ABTF model, Cognata dataset and loadgen
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# Byte-compiled / optimized / DLL files | ||
__pycache__/ | ||
*.py[cod] | ||
*$py.class | ||
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downloads/ | ||
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parts/ | ||
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# PyInstaller | ||
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# before PyInstaller builds the exe, so as to inject date/other infos into it. | ||
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# PyBuilder | ||
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# IPython | ||
profile_default/ | ||
ipython_config.py | ||
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# pyenv | ||
# For a library or package, you might want to ignore these files since the code is | ||
# intended to run in multiple environments; otherwise, check them in: | ||
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# pipenv | ||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. | ||
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# install all needed dependencies. | ||
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# poetry | ||
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. | ||
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control | ||
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# pdm | ||
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. | ||
#pdm.lock | ||
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it | ||
# in version control. | ||
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env.bak/ | ||
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Copyright (c) 2024 MLCommons |
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# Collective Mind interface and automation for ABTF | ||
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This repository contains [CM scripts (cross-platform automation recipes)](https://github.com/mlcommons/ck) | ||
to make it easier to prepare and benchmark different versions of ABTF models | ||
(public or private) with MLPerf loadgen across different software and hardware. | ||
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* Run and benchmark reference ABTF model via CM (CPU and CUDA): [README](docs/test-abtf-model/README.md) | ||
* Knowledge base: [README](docs/test-abtf-model/README-kb.md) |
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alias: mlcommons@cm4abtf | ||
uid: 566d31eda11948a9 | ||
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git: true | ||
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deps: | ||
- alias: mlcommons@cm4mlops | ||
uid: 9e97bb72b0474657 | ||
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- alias: mlcommons@ck | ||
uid: a4705959af8e447a | ||
conflict: True | ||
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[ [Back to the main page](README.md) ] | ||
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## Prepare workflow to benchmark ABTF model on CUDA-based device | ||
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### Prerequisites | ||
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* We expect that you already have CUDA driver installed | ||
* Tested with PyTorch 2.2.2 and CUDA 11.8 and 12.1 | ||
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### Detect or install CUDA toolkit and libraries | ||
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```bash | ||
cmr "get cuda _toolkit _cudnn" | ||
cmr "get cuda-devices" | ||
``` | ||
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### Build MLPerf loadgen | ||
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```bash | ||
cmr "get mlperf inference loadgen _copy" --version=main | ||
``` | ||
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### Install or detect PyTorch and PyTorchVision | ||
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#### CUDA 11.8 | ||
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```bash | ||
cmr "get generic-python-lib _torch_cuda" --extra-index-url=https://download.pytorch.org/whl/cu118 --force-install | ||
cmr "get generic-python-lib _torchvision_cuda" --extra-index-url=https://download.pytorch.org/whl/cu118 --force-install | ||
``` | ||
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#### CUDA 12.1 | ||
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```bash | ||
cmr "get generic-python-lib _torch_cuda" --extra-index-url=https://download.pytorch.org/whl/cu121 --force-install | ||
cmr "get generic-python-lib _torchvision_cuda" --extra-index-url=https://download.pytorch.org/whl/cu121 --force-install | ||
``` | ||
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## Test Model with a test image | ||
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```bash | ||
cmr "test abtf ssd-resnet50 cognata pytorch _cuda" --input=0000008766.png --output=0000008766_prediction_test.jpg --config=baseline_8MP_ss_scales --num-classes=13 | ||
``` | ||
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## Benchmark model with MLPerf loadgen | ||
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```bash | ||
cmr "generic loadgen python _pytorch _cuda _custom _cmc" --samples=5 --modelsamplepath=0000008766.png.cuda.pickle --modelpath=baseline_8mp_ss_scales_ep15.pth --modelcfg.num_classes=13 --modelcfg.config=baseline_8MP_ss_scales | ||
``` | ||
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## Benchmarking other models | ||
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Other ways to download public or private model code and weights: | ||
```bash | ||
cmr "get ml-model abtf-ssd-pytorch _skip_weights" --adr.abtf-ml-model-code-git-repo.env.CM_ABTF_MODEL_CODE_GIT_URL=https://github.com/mlcommons/abtf-ssd-pytorch | ||
cmr "get ml-model abtf-ssd-pytorch _skip_weights" --model_code_git_url=https://github.com/mlcommons/abtf-ssd-pytorch --model_code_git_branch=cognata-cm | ||
cmr "get ml-model abtf-ssd-pytorch _skip_weights _skip_code" | ||
``` | ||
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Other ways to run local (private) model: | ||
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You can first copy ABTF model code from GitHub to your local directory `my-model-code`. | ||
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``` | ||
cmr "generic loadgen python _pytorch _cuda _custom _cmc" --samples=5 --modelsamplepath=0000008766.png.cpu.pickle \ | ||
--modelpath=baseline_8mp_ss_scales_ep15.pth \ | ||
--modelcfg.num_classes=13 \ | ||
--modelcodepath="my-model-code" \ | ||
--modelcfg.config=baseline_8MP_ss_scales | ||
``` | ||
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## Feedback | ||
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Join MLCommons discord or get in touch with developer: gfursin@cknowledge.org | ||
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# Issues | ||
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## Weird case on Windows | ||
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### 20240410: Grigori | ||
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If I download baseline_8mp_ss_scales_ep15.pth to the ROOT directory with the virtual environment, | ||
pip stops working since it considers this file as a broken package ... | ||
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# Misc commands | ||
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Register local ABTF model in CM cache to be the default | ||
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```bash | ||
cmr "get ml-model abtf-ssd-pytorch _local.baseline_8mp_ss_scales_ep15.pth" | ||
``` |
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