ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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Tianlei Wu 72186bbb71
[CUDA] Build nhwc ops by default (#22648)
### Description

* Build cuda nhwc ops by default.
* Deprecate `--enable_cuda_nhwc_ops` in build.py and add
`--disable_cuda_nhwc_ops` option

Note that it requires cuDNN 9.x. If you build with cuDNN 8, NHWC ops
will be disabled automatically.

### Motivation and Context

In general, NHWC is faster than NCHW for convolution in Nvidia GPUs with
Tensor Cores, and this could improve performance for vision models.

This is the first step to prefer NHWC for CUDA in 1.21 release. Next
step is to do some tests on popular vision models. If it help in most
models and devices, set `prefer_nhwc=1` as default cuda provider option.
2024-11-06 09:54:55 -08:00
.config Add an 1ES PT baseline file (#22587) 2024-10-25 09:18:30 -07:00
.devcontainer
.gdn
.github [CI] Set up proper permissions for linting workflow (#22696) 2024-11-01 18:14:52 -07:00
.pipelines [DML EP] Update DML to 1.15.4 (#22635) 2024-10-29 17:13:57 -07:00
.vscode Stop VSCode appending file associations to settings.json (#21944) 2024-08-31 19:04:12 -07:00
cgmanifests Remove nsync (#20413) 2024-10-21 15:32:14 -07:00
cmake [CUDA] Build nhwc ops by default (#22648) 2024-11-06 09:54:55 -08:00
csharp [C# MauiModelTester] Fix icon name in Info.plist (#21666) 2024-11-05 16:55:38 -08:00
dockerfiles [CUDA] Build nhwc ops by default (#22648) 2024-11-06 09:54:55 -08:00
docs [CUDA] Build nhwc ops by default (#22648) 2024-11-06 09:54:55 -08:00
include/onnxruntime/core [CoreML] ML Program more ops (2/N) (#22480) 2024-11-01 08:37:56 +08:00
java Build CUDA and DML together (#22602) 2024-10-31 15:51:13 -07:00
js [WebNN EP] Fix issues with MLTensor caching (#22701) 2024-11-06 09:17:11 -08:00
objectivec [CoreML ML Program] support acclerators selector (#22383) 2024-10-15 11:50:11 +08:00
onnxruntime [CUDA] Build nhwc ops by default (#22648) 2024-11-06 09:54:55 -08:00
orttraining enable serialize prepacked weights into data file (#22256) 2024-10-24 22:24:48 -07:00
rust
samples
tools [CUDA] Build nhwc ops by default (#22648) 2024-11-06 09:54:55 -08:00
winml Fix warnings (#21809) 2024-08-21 14:23:37 -07:00
.clang-format
.clang-tidy
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.gitignore
.gitmodules Revert "Upgrade emsdk from 3.1.59 to 3.1.62" (#21817) 2024-08-22 11:21:00 -07:00
.lintrunner.toml [js] change default formatter for JavaScript/TypeScript from clang-format to Prettier (#21728) 2024-08-14 16:51:22 -07:00
build.bat
build.sh
build_arm64x.bat
CITATION.cff
CODEOWNERS
CONTRIBUTING.md
lgtm.yml
LICENSE
NuGet.config Update C# test projects (#21631) 2024-09-05 08:21:23 +10:00
ort.wprp
ORT_icon_for_light_bg.png
packages.config [DML EP] Update DML to 1.15.4 (#22635) 2024-10-29 17:13:57 -07:00
pyproject.toml
README.md Update README.md with release roadmap info (#22486) 2024-10-18 11:00:43 -07:00
requirements-dev.txt
requirements-doc.txt
requirements-lintrunner.txt Update lintrunner requirements (#22185) 2024-09-23 18:27:16 -07:00
requirements-training.txt
requirements.txt
SECURITY.md
setup.py Update CMake to 3.31.0rc1 (#22433) 2024-10-16 11:50:13 -07:00
ThirdPartyNotices.txt Remove nsync (#20413) 2024-10-21 15:32:14 -07:00
VERSION_NUMBER bumps up version in main from 1.20 -> 1.21 (#22482) 2024-10-17 12:32:35 -07:00

ONNX Runtime is a cross-platform inference and training machine-learning accelerator.

ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →

ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →

Get Started & Resources

Builtin Pipeline Status

System Inference Training
Windows Build Status
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Linux Build Status
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Mac Build Status
Android Build Status
iOS Build Status
Web Build Status
Other Build Status

This project is tested with BrowserStack.

Third-party Pipeline Status

System Inference Training
Linux Build Status

Releases

The current release and past releases can be found here: https://github.com/microsoft/onnxruntime/releases.

For details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https://onnxruntime.ai/roadmap.

Data/Telemetry

Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.

Contributions and Feedback

We welcome contributions! Please see the contribution guidelines.

For feature requests or bug reports, please file a GitHub Issue.

For general discussion or questions, please use GitHub Discussions.

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

License

This project is licensed under the MIT License.