108b860dc1
Dev containers[1] provide a self-contained development environment that
can be tailored for a project. GitHub Codespaces[2] provide a cloud
hosted environment to run these containers in. This makes it easy to
provision a consistent development environment with developer tooling
already installed and configured that provide the following benefits:
1. Developer onboarding is simplified.
1. Easy to get environment setup and running
2. Reference environment is available, if developer is having issues
with local environment
2. Developer tooling is provided and automatically configured.
1. Python / C++ build tooling
2. Python / C++ code formatters / linters
3. Easy to provision cloud hosted environment via GitHub Codespace.
4. Easy to create ephemeral development environments to test new changes
1. Can be used to provision environments to test changes
and Pull Requests
This can ease several pain points that developers on-boarding to the
project can encounter. One of the problems I have seen with developers
new to the project (I am one of these) is having the baseline
development environment (Python / C++) and recommended tools (e.g. VS
Code Python / C++ extensions, linters, and autoformatters) installed and
configured to efficiently get started in the repository. For all
developers, this makes it easy to leverage ephemeral cloud hosted
development environments via GitHub Codespaces.
**Notes:**
- Compiling the project can run into trouble if the codespace has < 32
GB of RAM
1) https://docs.github.com/en/codespaces/setting-up-your-project-for-codespaces/introduction-to-dev-containers
2) https://docs.github.com/en/codespaces/overview
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| .config | ||
| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| package/rpm | ||
| samples | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .flake8 | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| 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 | ||
| packages.config | ||
| pyproject.toml | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
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.