Release Notes

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

Snap ML v1.9.1 (May 31, 2022)

New features:

  • New export_model method for RandomForest[Classifier/Regressor] and BoostingMachine[Classifier/Regressor].

Bug fixes:

  • Support importing ensembles from PMML that were trained using sample weights.

  • Fix reference counting for PyNone.

  • Improved memory management for inference engine on z16 AI accelerator.

API changes:

  • Expose import_model method in BoostingMachine[Classifier/Regressor].

  • Expose optimize_trees method in RandomForest[Classifier/Regressor] and BoostingMachine[Classifier/Regressor].

Snap ML v1.9.0 (Apr. 1, 2022)

New features:

  • New matrix-based algorithms for tree-ensemble inference using zDNN library (available for Z16 systems only).

Snap ML v1.8.4 (Feb. 24, 2022)

Bug-fixes:

  • Fix bug with string labels in BoostingMachine.

  • Fix bug with overflow in RBFSampler.

  • Fix bug related to compressed ensembles of variable depth.

  • Fix bug related to number of features-based optimization in compressed ensemble.

New features:

  • ExtraTrees support in inference engine.

  • New features for knowledge distillation.

Perf. improvements:

  • Training performance improvement for all tree-based models.

Snap ML v1.8.3 (Dec. 10, 2021)

API changes:

  • Added option to enable/disable optimized inference for MultiOutputCalibratedClassifier

Bug-fixes:

  • MultiOutputCalibratedClassifier now returns self

Snap ML v1.8.2 (Dec. 7, 2021)

Bug fixes:

  • Fix segfault for cross entropy loss and early stopping

  • Fix issue with class weights and BoostingMachineClassifier

Snap ML v1.8.1 (Dec. 2, 2021)

New Features:

  • Support for older machines that do not have AVX2 instructions.

  • New MultiOutputCalibratedClassifier estimator.

  • SVM: support for squared hinge loss and shrinkage.

  • Support np.memmap as input for GLMs.

API Changes:

  • Added fit function to BatchedTreeEnsemble classes.

Dependency Changes:

  • Compile against numpy==1.19.3, to support numpy>=1.18.5 at runtime.

Bug-fixes:

  • Correct class label predictions when importing RF/XGB models.

  • Fix issue when deepcopying estimators that were not yet fitted.

  • Fix documentation in BoostingMachineClassifier.

Snap ML v1.7.8 (Nov. 19, 2021)

Bug-fixes:

  • Support older machines that do not have AVX2 instructions.

Snap ML v1.8.0 (Nov. 11, 2021)

New Features:

  • Python 3.9 support (Python 3.6 is no longer supported).

  • Accelerated scoring of random forest models trained in scikit-learn via PMML or ONNX import.

  • Faster tree ensemble inference.

  • Support for multiclass classification in BoostingMachineClassifier.

  • Feature importance for boosting machines.

  • New estimators to support batched training of tree ensembles on very large datasets.

API Changes:

  • Setter functions are provided for all estimators to change parameters for training and inference.

  • Deprecated setting n_jobs at inference time as argument to predict.

  • Expose intercept attribute for GLMs.

  • Reorganization of Booster parameters.

Bug-fixes:

  • Enforce user-specific n_jobs for multiclass SVM.

  • Fixed PY_SSIZE_T_CLEAN warnings for newer versions of Python.

  • Fixed bug when serializing compressed trees in heterogeneous ensemble.

  • Fixed race condition for exact regression trees.

  • Fixed segfault when calling decision_function for multiclass SVM.

  • Fixed memory issue for boosting machines with subsample<1.

Snap ML v1.7.7 (Jul. 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 (Jun. 18, 2021)

  • Relaxed numpy dependency to be >= 1.18.5

Snap ML v1.7.5 (Jun. 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 (Jun. 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 (Feb. 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