ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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Gary Miguel 93e239747f
Construct valid graphs for ONNX checker for IR version < 4. (#9665)
* Construct valid graphs for ONNX checker for IR version < 4.

Previously the constructed graph was not guaranteed to have its
initializers be a subset of its inputs, which is required for IR
version < 4. This resulted in spurious failures.

Fixes #9663
2021-11-12 09:13:28 +10:00
.gdn Update compliance tasks in python packaging pipeline and fix some compile warnings (#8471) 2021-07-30 17:16:37 -07:00
.github Update issue template to ask users to check known issues to avoid repetition. (#8288) 2021-07-02 15:36:14 -07:00
cgmanifests Update manylinux build scripts (#9701) 2021-11-09 11:55:49 -08:00
cmake fix the mkl dependency for eager mode (#9702) 2021-11-09 08:52:55 -08:00
csharp Enable building winml with --build_nuget (#9632) 2021-11-04 00:42:51 -07:00
dockerfiles Update dockerfile readme (#9241) 2021-10-01 17:28:26 -07:00
docs fixing pypi pipeline for release (#9716) 2021-11-10 17:33:51 -08:00
include/onnxruntime/core Construct valid graphs for ONNX checker for IR version < 4. (#9665) 2021-11-12 09:13:28 +10:00
java Support optional type in ORT (#8339) 2021-11-04 15:01:42 -07:00
js update ONNX Runtime Web CI to use same script for package versioning (#9698) 2021-11-10 12:52:34 -08:00
objectivec [Objective-C API] WIgnore clang documentation warnings from C/C++ header usage. (#9057) 2021-09-14 13:03:48 -07:00
onnxruntime Construct valid graphs for ONNX checker for IR version < 4. (#9665) 2021-11-12 09:13:28 +10:00
orttraining [python api] align api with other language bindings' treatment of explicit provider registrations. enforce use of providers param in python InferenceSession when execution providers other than default CPU are enabled. (#9712) 2021-11-10 12:17:53 -08:00
package/rpm Bumping up to 1.10 (#9006) 2021-09-22 16:34:28 -07:00
samples Add Python checks pipeline (#7032) 2021-08-09 10:37:05 -07:00
server fix boost download url (#7843) 2021-05-26 16:08:57 -07:00
tools fixing pypi pipeline for release (#9716) 2021-11-10 17:33:51 -08:00
winml Remove all warnings C4800: Implicit conversion from 'int32_t/int64_t' to bool. Possible information loss (#9535) 2021-11-08 10:12:27 -08:00
.clang-format
.clang-tidy
.dockerignore
.flake8 Add Python checks pipeline (#7032) 2021-08-09 10:37:05 -07:00
.gitattributes
.gitignore Add Xamarin support (#9436) 2021-10-27 20:07:07 +10:00
.gitmodules Remove optional-lite (#9424) 2021-10-22 16:45:45 -07:00
build.amd64.1411.bat
build.bat
build.sh
CODEOWNERS Update ORTTraiing frontend codeowner (#9427) 2021-10-18 23:56:21 -07:00
CONTRIBUTING.md fixed the link (#8757) 2021-08-18 11:45:42 -07:00
LICENSE
NuGet.config
ort.wprp
packages.config Update DirectML version to 1.5.1 and enable ARM/ARM64 builds with DML (#7511) 2021-04-30 00:49:30 -07:00
README.md Fix typo 2021-08-12 15:57:15 -07:00
requirements-dev.txt Add post-install command to build PyTorch CPP extensions from within onnxruntime package (#8027) 2021-06-28 18:11:58 -07:00
requirements-doc.txt Add auto doc gen for ORTModule API during CI build (#7046) 2021-03-22 10:20:33 -07:00
requirements-training.txt Add post-install command to build PyTorch CPP extensions from within onnxruntime package (#8027) 2021-06-28 18:11:58 -07:00
requirements.txt.in Chang how numpy version is handled. (#8130) 2021-06-23 14:08:37 -07:00
setup.py Strip AMD libraries bundled with Python package due to libonnxruntime_providers_rocm.so change (#9679) 2021-11-11 09:32:09 -08:00
ThirdPartyNotices.txt Clean up optional-lite references (#9534) 2021-10-25 21:05:45 -07:00
VERSION_NUMBER Bumping up to 1.10 (#9006) 2021-09-22 16:34:28 -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

General Information: onnxruntime.ai

Usage documention and tutorials: onnxruntime.ai/docs

Companion sample repositories:

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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.