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### Description Improve docker commands to make docker image layer caching works. It can make docker building faster and more stable. So far, A100 pool's system disk is too small to use docker cache. We won't use pipeline cache for docker image and remove some legacy code. ### Motivation and Context There are often an exception of ``` 64.58 + curl https://nodejs.org/dist/v18.17.1/node-v18.17.1-linux-x64.tar.gz -sSL --retry 5 --retry-delay 30 --create-dirs -o /tmp/src/node-v18.17.1-linux-x64.tar.gz --fail 286.4 curl: (92) HTTP/2 stream 0 was not closed cleanly: INTERNAL_ERROR (err 2) ``` Because Onnxruntime pipeline have been sending too many requests to download Nodejs in docker building. Which is the major reason of pipeline failing now In fact, docker image layer caching never works. We can always see the scrips are still running ``` #9 [3/5] RUN cd /tmp/scripts && /tmp/scripts/install_centos.sh && /tmp/scripts/install_deps.sh && rm -rf /tmp/scripts #9 0.234 /bin/sh: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8) #9 0.235 /bin/sh: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8) #9 0.235 /tmp/scripts/install_centos.sh: line 1: !/bin/bash: No such file or directory #9 0.235 ++ '[' '!' -f /etc/yum.repos.d/microsoft-prod.repo ']' #9 0.236 +++ tr -dc 0-9. #9 0.236 +++ cut -d . -f1 #9 0.238 ++ os_major_version=8 .... #9 60.41 + curl https://nodejs.org/dist/v18.17.1/node-v18.17.1-linux-x64.tar.gz -sSL --retry 5 --retry-delay 30 --create-dirs -o /tmp/src/node-v18.17.1-linux-x64.tar.gz --fail #9 60.59 + return 0 ... ``` This PR is improving the docker command to make image layer caching work. Thus, CI won't send so many redundant request of downloading NodeJS. ``` #9 [2/5] ADD scripts /tmp/scripts #9 CACHED #10 [3/5] RUN cd /tmp/scripts && /tmp/scripts/install_centos.sh && /tmp/scripts/install_deps.sh && rm -rf /tmp/scripts #10 CACHED #11 [4/5] RUN adduser --uid 1000 onnxruntimedev #11 CACHED #12 [5/5] WORKDIR /home/onnxruntimedev #12 CACHED ``` ###Reference https://docs.docker.com/build/drivers/ --------- Co-authored-by: Yi Zhang <your@email.com> |
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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 |
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.