9c0e5954cb
* Adding Output Shape Validation for ORT-CPU execution flow * Skipping validation check in-case output is not a tensor. Fixed conv_transpose test. Ignoring pad and reduction test * Comparison b/w signed and un-signed int. Removed const for a primitive variable * Commented the un-used test function signature * Removed exception instead logging warning. Because there are lots of ORT tests which are failing because of this validation * Fixed warning condition and test * Fixed test and addressed comment on the PR * Output shape verification will happen only for final output nodes of the model * Changed variable name from camel case to underscore style * Enable the tests as the validation failure will now logs warning instead of throwing an exception * Adding Output Shape Validation for ORT-CPU execution flow * Resolve merge conflict * Comparison b/w signed and un-signed int. Removed const for a primitive variable * Commented the un-used test function signature * Removed exception instead logging warning. Because there are lots of ORT tests which are failing because of this validation * Fixed warning condition and test * Fixed test and addressed comment on the PR * Output shape verification will happen only for final output nodes of the model * Changed variable name from camel case to underscore style * Enable the tests as the validation failure will now logs warning instead of throwing an exception * Remove duplicate function "GetLogger()" Remove duplicate function "GetLogger()" * Fixed typo in method name TestConvTransposeOpInitializer Fixed typo in method name "TestConvTransposeOpInitializer" Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com> |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator compatible with deep learning frameworks, PyTorch and TensorFlow/Keras, as well as classical machine learning libraries such as scikit-learn, and more.
ONNX Runtime uses the portable ONNX computation graph format, backed by execution providers optimized for operating systems, drivers and hardware.
Common use cases for ONNX Runtime:
- Improve inference performance for a wide variety of ML models
- Reduce time and cost of training large models
- Train in Python but deploy into a C#/C++/Java app
- Run with optimized performance on different hardware and operating systems
- Support models created in several different frameworks
ONNX Runtime inference APIs are stable and production-ready since the 1.0 release in October 2019 and can enable faster customer experiences and lower costs.
ONNX Runtime training feature was introduced in May 2020 in preview. This feature supports acceleration of PyTorch training on multi-node NVIDIA GPUs for transformer models. Additional updates for this feature are coming soon.
Get Started
- Install
- Inference
- Training
- Documentation
- Samples and Tutorials
- Build Instructions
- Frequently Asked Questions
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
Data/Telemetry
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.