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