ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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Jian Chen 06a84c8a0d
Enable DML on Windows and CUDA on Linux for Node.js binding (#19274)
This pull request includes modifications to the `c-api-cpu.yml` Azure
Pipelines configuration file. The changes mainly revolve around the
Node.js packaging stage and the handling of Node.js artifacts. The most
significant changes include renaming the Node.js packaging stage, adding
a new dependency to the stage, changing artifact names, adding a new
script to list Node.js artifacts, and updating the source folder for
copying NuGet binaries.

Changes in Node.js packaging:

*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L503-R508):
Renamed the Node.js packaging stage from `Nodejs_Packaging_CPU` to
`Nodejs_Packaging` and added `Windows_CI_GPU_DML_Dev` as a new
dependency to the stage.

Changes in handling of Node.js artifacts:

*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L568-R569):
Changed the artifact name from `drop-onnxruntime-nodejs-win-x64` to
`drop-onnxruntime-nodejs-win-x64-dml` in the task to download pipeline
artifacts for Windows x64.
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59R595-R598):
Added a new script to list Node.js artifacts from the directory
`$(Build.BinariesDirectory)/nodejs-artifacts/win32/x64/`.
*
[`tools/ci_build/github/azure-pipelines/templates/c-api-cpu.yml`](diffhunk://#diff-00815920cc190d10fdebceac0c3a4b8a59e408684ae38177dfe7f96cae276c59L635-R640):
Updated the source folder from
`$(Build.BinariesDirectory)\RelWithDebInfo\RelWithDebInfo\nuget-artifacts\onnxruntime-win-x64\lib`
to `$(Build.BinariesDirectory)\nodejs-artifacts\win32\x64` in the task
to copy NuGet binaries to the directory
`$(Build.SourcesDirectory)\js\node\bin\napi-v3\win32\x64`.

---------

Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com>
2024-02-05 14:33:58 -08:00
.config
.devcontainer
.gdn Update win-ci-pipeline.yml: enable xnnpack tests (#16244) 2023-06-14 19:12:42 -07:00
.github Bump gradle/gradle-build-action from 2 to 3 (#19297) 2024-02-05 09:41:57 -08:00
.pipelines Fix a build issue: /MP was not enabled correctly (#19190) 2024-01-29 12:45:38 -08:00
.vscode update .vscode/settings.json (#19084) 2024-01-10 19:26:01 -08:00
cgmanifests Add coremltools 7.1 as a dependency (#19389) 2024-02-03 09:42:21 +10:00
cmake phi2 conversion/optimization script (#19338) 2024-02-05 10:15:16 -08:00
csharp Add support for a collection of OrtValue as inputs and outputs to C# TrainingSession (#19048) 2024-01-25 21:55:36 -08:00
dockerfiles Update dockerfiles/Dockerfile.source to avoid installing onnx (#17975) 2023-10-20 09:24:21 -07:00
docs Update ScatterElements to Support Opset 13, 15, 18 (#19198) 2024-01-30 09:18:50 -08:00
include/onnxruntime/core [VitisAI] Refactor the VAIEP to use MSFT's standalone API (#19058) 2024-01-31 21:08:26 -08:00
java Change "#ifdef WIN32" to "#ifdef _WIN32" (#19254) 2024-01-24 14:35:44 -08:00
js [js/webgpu] Add LeakyRelu activation for fusedConv (#19369) 2024-02-02 09:06:38 -08:00
objectivec Objective-C API updates (#18738) 2023-12-07 16:47:46 -08:00
onnxruntime fix output shape inference packed gqa (#19374) 2024-02-05 13:53:39 -08:00
orttraining add ATen support for bicubic interpolation (#19380) 2024-02-05 13:11:37 -08:00
rust Fix rust compile issues and add GH action to run build validations and tests (#18346) 2023-11-09 04:26:02 -08:00
samples Removed all the deprecated python training code and related tests and utils (#18333) 2023-11-17 18:19:21 -08:00
tools Enable DML on Windows and CUDA on Linux for Node.js binding (#19274) 2024-02-05 14:33:58 -08:00
winml Update winml to use #cores - #soc cores by Default as the number of intraopthreads (#18384) 2023-11-28 09:26:48 -08:00
.clang-format Prevent GSL_SUPPRESS arguments from being modified by clang-format (#17242) 2023-08-22 18:26:53 -07:00
.clang-tidy
.dockerignore
.gitattributes
.gitignore Build onnxruntime.dll as arm64x (#18633) 2023-12-06 16:49:00 -08:00
.gitmodules update to emsdk-3.1.51 (#18844) 2024-01-12 16:04:33 -08:00
.lintrunner.toml FP16 optimizer automatically detect DeepSpeed compatibility (#18084) 2023-10-25 15:11:02 +08:00
build.bat try to find patch.exe in git default installation folder (#17106) 2023-08-10 21:48:13 -07:00
build.sh Upgrade old Python version in packaging pipeline (#16667) 2023-07-17 08:24:47 -07:00
build_arm64x.bat remove unnecessary environment variable (#19166) 2024-01-16 16:24:37 -08:00
CITATION.cff
CODEOWNERS
CONTRIBUTING.md
lgtm.yml
LICENSE
NuGet.config
ort.wprp ORT ETW dynamic logging that improves ORT diagnosability & performance (#18882) 2024-01-11 12:43:27 -08:00
ORT_icon_for_light_bg.png
packages.config Update DirectML nuget version to 1.13.1 (#19122) 2024-01-15 19:04:41 -08:00
pyproject.toml [ORTModule] ATen Efficient Attention and Triton Flash Attention (#17959) 2023-10-27 10:29:27 +08:00
README.md Update README.md (#18963) 2024-01-03 17:26:25 -08:00
requirements-dev.txt ONNX 1.15 integration (#17125) 2023-09-26 14:44:48 -07:00
requirements-doc.txt
requirements-lintrunner.txt Bump linter versions (#18341) 2023-11-08 13:04:40 -08:00
requirements-training.txt ONNX 1.15 integration (#17125) 2023-09-26 14:44:48 -07:00
requirements.txt.in
SECURITY.md
setup.py phi2 conversion/optimization script (#19338) 2024-02-05 10:15:16 -08:00
ThirdPartyNotices.txt Update ThirdPartyNotices.txt: Add Intel neural-speed (#19332) 2024-01-30 12:40:30 -08:00
VERSION_NUMBER [ORT 1.17.0 release] Bump up version to 1.18.0 (#19170) 2024-01-17 11:18:32 -08:00

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
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Third-party Pipeline Status

System Inference Training
Linux Build Status

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.