ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Find a file
yf711 105f5f0f20
Avoid trt deprecated api warnings shown as errors during ORT-TRT build (#16035)
### Description
Avoid trt deprecated api warnings shown as errors when building
onnxruntime_test_all
This issue is only visible when installing trt via binaries, rather than
deb/rpm pkg (CI pipelines)


The change is similar to existing set_property for
onnxruntime_providers_tensorrt

89ea503024/cmake/onnxruntime_providers.cmake (L421)

### Motivation and Context

onnxruntime/test/unittest_main/[test_main.cc](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/test/unittest_main/test_main.cc#L32)
includes nvinfer.h, which includes deprecated trt apis and and generates
warnings.
When building onnxruntime_test_all, it will show warnings as errors and
block the build.

### Doubts
Although this issue is visible on trt tar binaries but not on trt
deb/rpm pkgs,
Their file size&hash are the same (creation time vary), regarding
headers/libs installing in different ways.
| tarBin | pkg |
| ------------------------------------------------------------ |
------------------------------------------------------------ |
| 997284784 Apr 26 15:15 libnvinfer_builder_resource.so.8.6.1 |
997284784 Apr 26 22:21 libnvinfer_builder_resource.so.8.6.1 |
| 235369632 Apr 26 15:14 libnvinfer.so.8.6.1 | 235369632 Apr 26 22:21
libnvinfer.so.8.6.1 |
2023-05-24 13:19:27 -07:00
.config
.devcontainer
.gdn
.github
.pipelines
.vscode
cgmanifests
cmake
csharp [Bug Fix] Incorrect comparison for FromBuffer in TrainingSession.cs (#16022) 2023-05-22 21:21:54 -07:00
dockerfiles
docs
include/onnxruntime/core
java
js
objectivec Add iOS Swift Package Manager support (#15297) 2023-04-20 16:18:35 +10:00
onnxruntime
orttraining
rust
samples
swift/OnnxRuntimeBindingsTests
tools
winml
.clang-format
.clang-tidy
.dockerignore
.gitattributes
.gitignore
.gitmodules
.lintrunner.toml
build.amd64.1411.bat
build.bat
build.sh
CITATION.cff
CODEOWNERS
CONTRIBUTING.md
lgtm.yml
LICENSE
NuGet.config
ort.wprp
ORT_icon_for_light_bg.png
Package.swift
packages.config
pyproject.toml
README.md
requirements-dev.txt
requirements-doc.txt
requirements-lintrunner.txt
requirements-training.txt
requirements.txt.in
SECURITY.md
setup.py
ThirdPartyNotices.txt
VERSION_NUMBER

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

Build Pipeline Status

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