92b6e10d37
### Description
Python Package Pipeline failed since there is exception raised in
test_smooth_quant (from #16288):
```
File "/home/cloudtest/.local/lib/python3.8/site-packages/onnxruntime/quantization/quantize.py", line 384, in quantize_static
importlib.import_module("neural_compressor.adaptor.ox_utils.smooth_quant")
File "/home/cloudtest/.local/lib/python3.8/site-packages/neural_compressor/__init__.py", line 24, in <module>
from .contrib import *
File "/home/cloudtest/.local/lib/python3.8/site-packages/neural_compressor/contrib/__init__.py", line 19, in <module>
from .strategy import *
File "/home/cloudtest/.local/lib/python3.8/site-packages/neural_compressor/contrib/strategy/__init__.py", line 26, in <module>
__import__(basename(f)[:-3], globals(), locals(), level=1)
File "/home/cloudtest/.local/lib/python3.8/site-packages/neural_compressor/contrib/strategy/sigopt.py", line 22, in <module>
from neural_compressor.strategy.strategy import strategy_registry, TuneStrategy
File "/home/cloudtest/.local/lib/python3.8/site-packages/neural_compressor/strategy/__init__.py", line 20, in <module>
from .strategy import STRATEGIES
File "/home/cloudtest/.local/lib/python3.8/site-packages/neural_compressor/strategy/strategy.py", line 41, in <module>
from ..algorithm import AlgorithmScheduler, ALGORITHMS
File "/home/cloudtest/.local/lib/python3.8/site-packages/neural_compressor/algorithm/__init__.py", line 20, in <module>
from .algorithm import ALGORITHMS, Algorithm, AlgorithmScheduler, algorithm_registry
File "/home/cloudtest/.local/lib/python3.8/site-packages/neural_compressor/algorithm/algorithm.py", line 21, in <module>
from neural_compressor.utils.create_obj_from_config import get_algorithm
File "/home/cloudtest/.local/lib/python3.8/site-packages/neural_compressor/utils/create_obj_from_config.py", line 20, in <module>
from neural_compressor.metric import METRICS
File "/home/cloudtest/.local/lib/python3.8/site-packages/neural_compressor/metric/__init__.py", line 30, in <module>
__import__(basename(f)[:-3], globals(), locals(), level=1)
File "/home/cloudtest/.local/lib/python3.8/site-packages/neural_compressor/metric/coco_tools.py", line 54, in <module>
from pycocotools import coco
File "/usr/local/lib/python3.8/dist-packages/pycocotools/coco.py", line 52, in <module>
from . import mask as maskUtils
File "/usr/local/lib/python3.8/dist-packages/pycocotools/mask.py", line 3, in <module>
import pycocotools._mask as _mask
File "pycocotools/_mask.pyx", line 1, in init pycocotools._mask
ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject
```
The cause is pycocotools package uses "oldest-supported-numpy", which
might cause older version numpy in build pycocotools:
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| .config | ||
| .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
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Usage documention and tutorials: onnxruntime.ai/docs
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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.