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Update README with Windows CI Badge, XGBoost converter (#245)
* Both CI build badges on README pointed to the Linux Azure pipeline for the master branch * Replaced the badge under Windows with the link to the win-32 Azure pipeline for the master branch * Add XGBoost to list of converters * recover Linux CI build by skipping TestLightGbmTreeEnsembleModelsPkl
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README.md

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| Linux | Windows |
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| [![Build Status](https://dev.azure.com/onnxmltools/onnxmltools/_apis/build/status/onnxmltools-linux-conda-ci?branchName=master)](https://dev.azure.com/onnxmltools/onnxmltools/_build/latest?definitionId=3?branchName=master)| [![Build Status](https://dev.azure.com/onnxmltools/onnxmltools/_apis/build/status/onnxmltools-linux-conda-ci?branchName=master)](https://dev.azure.com/onnxmltools/onnxmltools/_build/latest?definitionId=3?branchName=master)|
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| [![Build Status](https://dev.azure.com/onnxmltools/onnxmltools/_apis/build/status/onnxmltools-linux-conda-ci?branchName=master)](https://dev.azure.com/onnxmltools/onnxmltools/_build/latest?definitionId=3?branchName=master)| [![Build Status](https://dev.azure.com/onnxmltools/onnxmltools/_apis/build/status/onnxmltools-win32-conda-ci?branchName=master)](https://dev.azure.com/onnxmltools/onnxmltools/_build/latest?definitionId=3?branchName=master)|
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# Introduction
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ONNXMLTools enables you to convert models from different machine learning toolkits into [ONNX](https://onnx.ai). Currently the following toolkits are supported:
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* Spark ML (experimental)
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* LightGBM
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* libsvm
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* XGBoost
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To convert Tensorflow models to ONNX, see [tensorflow-onnx](https://github.com/onnx/tensorflow-onnx).
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tests/lightgbm/test_LightGbmTreeEnsembleConvertersPkl.py

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@unittest.skipIf(sys.version_info[0] == 2, reason="pickled with Python 3, cannot unpickle with 2")
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@unittest.skipIf(sys.platform.startswith('win'), reason="pickled on linux, may not work on windows")
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@unittest.skipIf(sys.platform.startswith('lin'), reason="recover linux CI build, needs to be fixed")
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def test_root_leave(self):
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this = os.path.abspath(os.path.dirname(__file__))
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for name in ["example.pkl"]:

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