Release Notes

The latest stable version of Snap ML is available at https://pypi.org/project/snapml/.

Snap ML v1.7.8 (November 19, 2021)

Bug-fixes:

  • Support older machines that do not have AVX2 instructions.

Snap ML v1.7.7 (July 21, 2021)

  • Added support for A100 GPUs

  • Fixed unit-tests that were failing on POWER systems when using multiple GPUs

Snap ML v1.7.6 (June 18, 2021)

  • Relaxed numpy dependency to be >= 1.18.5

Snap ML v1.7.5 (June 17, 2021)

  • Relaxed numpy dependency to be >= 1.19.0

  • Added support for reading ONNX files generated on Z systems

Snap ML v1.7.4 (June 11, 2021)

  • New and improved inference engine for tree-based ensembles

  • Removed predict_proba from DecisionTreeRegressor and RandomForestRegressor

  • Relaxed numpy dependency to be >= 1.19.2

Snap ML v1.7.3 (May 26, 2021)

  • Pinned numpy dependency to 1.19.2

Snap ML v1.7.2 (May 26, 2021)

  • Simplified the pre-trained model import API for Boosting Machines

  • Fixed support for string labels at training/inference time

  • Stop the train routine if the input dataset is empty by raising a ValueError

  • Fixed issues related to the Windows build

  • Fixed bug in single-record inference when fit_intercept=True (linear models)

  • Unified code inference path for tree ensembles

  • Added exception handling for OpenMP code

Snap ML v1.7.1 (May 17, 2021)

  • Added multi-class classification support (Decision Trees and Random Forests)

  • Fixed issue related to class weights and Logistic Regression

  • Fixed issue with pickled boosting machine models

Snap ML v1.7.0 (February 22, 2021)

  • Added Windows, MacOS, Linux/x86, Linux/PPC support

  • Accelerated inference engine for tree ensembles

  • Added support for importing pre-trained tree ensembles from PMML, XGBoost, LightGBM and ONNX

  • Added a new ML algorithm: heterogeneous boosting machine model (for more details: https://proceedings.neurips.cc/paper/2020/file/7fd3b80fb1884e2927df46a7139bb8bf-Paper.pdf)

  • Integrated Snap ML into Lale

  • Added non-linear kernel support for linear models

  • Added predict_proba to LogisticRegression in the multi-class case

  • Added support for arbitrary class labels support for linear models

  • Added feature importance for tree-based models

  • Added support for cross_entropy loss for boosting machines

  • Various bug fixes

Version 1.7.0 included already all the following Machine Learning models and solvers:

  • Linear Regression: multi-threaded CPU, GPU, multi-GPU

  • Logistic Regression: multi-threaded CPU, GPU, multi-GPU

  • Support Vector Machine: multi-threaded CPU, GPU, multi-GPU

  • Decision Tree: multi-threaded CPU, GPU

  • Random Forest: multi-threaded CPU, GPU, multi-GPU

  • Boosting Machine: multi-threaded CPU, GPU