v1.6.5

The major updates of this release.

1. Add a new converter for H2O GBM MOJO
2. add tf2onnx wrapper with a onnxmltools converter style API.
3. Some bug fixing for the existing converters.

v1.6.0

Support opset 11

v1.5.1

Major updates:

1. Moving onnxconverter-common package from onnxmltools repo
2. Fix CI/nightly build
3. Fix ImageScaler bias for opset 10
4. Fix lightgbm.Booster
5. Fixed XGboost classifier converter output labels
6. Set default_batch_size to 'None'

v1.5.0

onnxmltools version 1.5.0 is now available! This version features ONNX Opset 10 support and code coverage.

How do I use the latest onnxmltools package?

pip install onnxmltools --upgrade
python -c "import onnxmltools"

This package includes converters for LightGBM, CoreML, Spark ML, LibSVM, XGBoost, and wrappers for conversion from scikit-learn and Keras.

Highlights since the last release

• Updating onnxmltools package version and requirements to 1.5.0 (#315)
• Opset 10 Updates
  • [Opset 10] Updates for thresholded relu (#308)
  • [Opset 10] Deprecate Upsample, create Resize op (#303)
  • [Opset 10] Pooling operator updates: AveragePool, MaxPool (#296)
  • Added apply_slice function to enable multiple versions of Slice (#291)
• Include code coverage / Improve CI Builds
  • Run code coverage on linux CI (#301)
  • Add support for Py3.7, onnx 1.5, onnxruntime 0.4 (#293)
• Fixing input to CoreML multiply for LeakyReLU (#297)
• Documentation update: Spark ML readme files (#289)

1.4 release

v1.4.1

Update __init__.py

v1.4.0 rc1 pre-release.

v1.4.0-rc1

upgrade package version for the release.

v1.3.2

with some new converters, xgboost, libsvm, and pyspark.
refactor onnxmltools structure by splitting keras and sklearn converters out.

The final release for onnxmltools 1.3

switch to the explicit padding if the input shape is available. (#198)

* switch to the explicit padding if the input shape is available.

* upgrade the version.

* fix for the nchw mode.

* output_padding could be None.

1.3.0-rc1

1. Multiple opset support: Enables user to generate model based on a specified ONNX opset
2. ONNX opset 8: Adds support for ONNX opset 8 in all converters
3. ONNX model optimization: Reduces redundant operators like transpose and identity in the converted model
4. Convert channel_last to channel_first: Enables converted model to run on WinML even if the original model is channel_last, since the WinML API only supports channel_first models
5. onnxruntime as backend test: Uses onnxruntime as the backend to test the converted model; improves code quality and compatibility between converters and the inference engine
6. Separate LightGBM from scikit-learn converter: Improves code organization

1.2.2

v1.2.2

upgrade the version for the final 1.2.2 release. (#132)