281f199754
### Description Properly cleans up all temporary resources created while running benchmarks. Details: - Dump all temporary artifacts (TRT engines, TRT profiles, inference profiles, fp16 models) into a temp directory in `/tmp/`. Each model/EP combination has its own temp directory that is deleted after validation and benchmarking. - Allow running both validation and benchmarking in one invocation of the benchmark.py script. This is necessary to allow the benchmarking step to reuse artifacts (e.g., TRT engines) created during validation. Before this PR, we ran validation on all model/EP combinations before running benchmarks on all combinations again. This required us to keep all temporary artifacts for all model/EP combinations throughout the entire run (expensive). - Create individual functions for validation and benchmarking (split-up large function that did it all) ### Motivation and Context The EP Perf pipeline failed to run because the script generated too much output and the VM ran out of disk space. |
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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
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 | |||
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| 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.