a451318820
If users use `trt_profile_min_shapes`, `trt_profile_max_shapes` and
`trt_profile_opt_shapes`, they need to provide all the dynamic shape
input with associated shape profiles.
In the case of the main graph is partitioned into TRT/CUDA subgraphs, if
the input of the subgraph is also dynamic shape, users need to provide
its shape profiles as well. User might not notice, so TRT EP will tell
them which input shape profiles need to be provided.
New warning message is :
```
Traceback (most recent call last):
File "/home/azureuser/disk2/debug/optional_inputs.py", line 218, in <module>
test_optional_input_dynamic(trt_profile=True, optional=True)
File "/home/azureuser/disk2/debug/optional_inputs.py", line 195, in test_optional_input_dynamic
session = ort.InferenceSession(
File "/home/azureuser/anaconda3/lib/python3.9/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line
419, in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
File "/home/azureuser/anaconda3/lib/python3.9/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line
471, in _create_inference_session
sess.initialize_session(providers, provider_options, disabled_optimizers)
onnxruntime.capi.onnxruntime_pybind11_state.EPFail: [ONNXRuntimeError] : 11 : EP_FAIL : User needs to provide all the
dynamic shape inputs with associated profiles if they want to explicitly set profiles through provider options.
Please note that main graph could be partitioned into TRT/CUDA/CPU subgraphs, in this case, user also needs to provide
shape profiles for the TRT subgraph's input if it's dynamic shape input.
Following input(s) has no associated shape profiles provided: x1
```
Please see this github issue:
https://github.com/microsoft/onnxruntime/issues/16600
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| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| rust | ||
| samples | ||
| swift/OnnxRuntimeBindingsTests | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .lintrunner.toml | ||
| 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
-
General Information: onnxruntime.ai
-
Usage documention and tutorials: onnxruntime.ai/docs
-
YouTube video tutorials: youtube.com/@ONNXRuntime
-
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
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.