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### Description * Add a few arguments CUDA_VERSION, CUDNN_VERSION, OS, GIT_COMMIT, GIT_BRANCH and ONNXRUNTIME_VERSION to the Dockerfile.cuda to allow for more flexibility in the build process. * Update README.md to include the new arguments and their usage. * Output labels to image so that it is easy to inspect the image. Available CUDA versions for ubuntu 24.04 can be found [here](https://hub.docker.com/r/nvidia/cuda/tags), and available CUDNN versions can be found [here](https://pypi.org/project/nvidia-cudnn-cu12/#history). Example command line to build docker image: ``` docker build -t onnxruntime-cuda --build-arg CUDA_VERSION=12.6.1 \ --build-arg CUDNN_VERSION=9.5.0.50 \ --build-arg GIT_BRANCH=$(git rev-parse --abbrev-ref HEAD) \ --build-arg GIT_COMMIT=$(git rev-parse HEAD) \ --build-arg ONNXRUNTIME_VERSION=$(cat ../VERSION_NUMBER) \ -f Dockerfile.cuda .. ``` Example labels from `docker inspect onnxruntime-cuda`: ``` "Labels": { "CUDA_VERSION": "12.6.1", "CUDNN_VERSION": "9.5.0.50", "maintainer": "Changming Sun <chasun@microsoft.com>", "onnxruntime_git_branch": "main", "onnxruntime_git_commit": "bc84958dcef5c6017ae58085f55b669efd74f4a5", "onnxruntime_version": "1.20.0", "org.opencontainers.image.ref.name": "ubuntu", "org.opencontainers.image.version": "24.04" } ``` ### Motivation and Context https://github.com/microsoft/onnxruntime/pull/22339 has hard-coded the cuda and cudnn versions. User might want to choose specified cuda and cudnn version during building docker image. |
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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
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General Information: onnxruntime.ai
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Usage documentation and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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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 |
This project is tested with BrowserStack.
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.