Release Notes

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

Snap ML v1.15.6 (Apr. 23, 2024)

Bug fixes:

  • Fix concurrency issue during load testing in TIS.

Snap ML v1.15.5 (Apr. 5, 2024)

Features:

  • Support for importing and exporting multi-class boosting models.

Bug fixes:

  • Fix memory layout for GraphFeaturePreprocessor.

  • OneHotEncoder: use sparse_output rather than sparse.

Snap ML v1.15.4 (Feb. 7, 2024)

Bug fixes:

  • Support gRPC input buffers for pre-processing.

Snap ML v1.15.3 (Jan. 15, 2024)

Bug fixes:

  • Core dump using pre-processed data for prediction.

Snap ML v1.15.2 (Jan. 8, 2024)

Bug fixes:

  • Extended AnyDataset public API to get number of examples.

Snap ML v1.15.1 (Dec. 20, 2023)

Bug fixes:

  • Missing symbols in libsnapmlcore.so when using the Snap ML C++ API for preprocessing.

Snap ML v1.15.0 (Dec. 12, 2023)

New feature:

  • Support for accelerated pre-processing at C++ level.

Snap ML v1.14.6 (Apr. 11, 2024)

Bug fixes:

  • Fix issue with feature importances for some estimators.

Snap ML v1.14.5 (Dec. 12, 2023)

Bug fixes:

  • Raise exception when no features can be parsed from PMML file.

Snap ML v1.14.4 (Nov. 29, 2023)

Bug fixes:

  • PMML import: support for scikit-learn models trained in 32-bit precision.

Snap ML v1.14.3 (Nov. 29, 2023)

Bug fixes:

  • GenericTreeEnsemble exposes model schema after import.

Snap ML v1.14.2 (Oct. 19, 2023)

New features:

  • New GenericTreeEnsemble C++ API.

Bug fixes:

  • Fix bug when using std::vector input to snapml::DenseDataset.

  • Fix segfault for MBITreeEnsemble.

  • Fix hanging for SVM with RBF kernel on IBM Power™ architecture.

Snap ML v1.14.1 (Jul. 23, 2023)

New features:

  • Add Python 3.11 support and drop support for Python 3.7 (EOL).

  • Support for quantile loss in BoostingMachineRegressor.

  • Updates for Graph Preprocessor.

Bug fixes:

  • Ensure thread safety in C++ API.

Packaging:

  • In this release we switch to distributing manylinux_2_28 wheels for all Linux platforms.

Snap ML v1.14.0 (May 3, 2023)

Bug fixes:

  • Fix incorrect predictions with zdnn_tensors in certain cases.

Packaging:

  • Wheels for s390x are now built using manylinux_2_28.

Snap ML v1.13.2 (Jan. 15, 2024)

Bug fixes:

  • Fix issue with missing feature importances for decision trees and random forests.

Snap ML v1.13.1 (Oct. 24, 2023)

Bug fixes:

  • Fix hanging for SVM with RBF kernel on IBM Power™ architecture.

Snap ML v1.13.0 (Mar. 23, 2023)

New features:

  • C++ API (e.g., headers + shared library) is now packaged within the Snap ML wheel.

  • Compressed tree ensembles now support multi-class classification.

Snap ML v1.12.1 (Mar. 22, 2023)

Bug fixes:

  • Overflow issue for dense datasets.

Snap ML v1.12.0 (Jan. 31, 2023)

New features:

  • Further improved inference engine for tree ensembles on IBM z16™ AI accelerator.

  • Support for Poisson regression loss in boosting machines.

Bug fixes:

  • class:

    BatchedTreeEnsemble: handle case when some classes are not present in batch.

  • Various documentation improvements.

Snap ML v1.11.1 (Nov. 29, 2022)

Documentation:

  • Updated the documentation of the GraphFeaturePreprocessor class

Snap ML v1.11.0 (Nov. 22, 2022)

New features:

  • Added GraphFeaturePreprocessor that enables scalable and real-time feature extraction from graph-structured data.

Snap ML v1.10.2 (Nov. 18, 2022)

API changes:

  • When using the generic import_model function, if the IBM z16™ AI accelerator is detected, we set the number of CPU threads automatically based on the detected number of cores.

Bug fixes:

  • Supporting a larger variety of compression types applied to disjoint tree sets within the same ensemble.

Snap ML v1.10.1 (Nov. 3, 2022)

Bug fixes:

  • Fix segfault when scoring certain PMML files.

Snap ML v1.10.0 (Oct. 26, 2022)

New features:

  • Significantly improved inference engine for tree ensembles on IBM z16™ AI accelerator.

  • Automatic selection of zdnn_tensors vs. compress_trees based on hardware availability.

Snap ML v1.9.7 (Oct. 25, 2022)

Bug fixes:

  • Raise exception when importing PMML that contains no trees.

Snap ML v1.9.6 (Oct. 20, 2022)

Packaging changes:

  • Make numpy dependency conditional on Python version.

Snap ML v1.9.5 (Oct. 7, 2022)

Bug fixes:

  • Attribute used_features_ lists features in the same order that they appear in schema_ attribute.

Snap ML v1.9.4 (Sep. 24, 2022)

New features:

  • Populate schema_ attribute when importing PPML models via generic import API.

  • Python 3.10 support.

Bug fixes:

  • Remove NUMA-related warnings when the machine does not have any NUMA nodes configured.

  • Fix bug during pre-processing for compressed decision trees.

  • Fix various issue with caching and pickling tree ensemble models.

Snap ML v1.9.3 (Sep. 9, 2022)

Performance improvements:

  • Tree ensemble inference now leverages vector instructions on IBM Power™ systems.

Snap ML v1.9.2 (Aug. 31, 2022)

Bug fixes:

  • Fix issue with binary incompatibility between Linux/MacOS and Windows.

  • BoostingMachine: Fix overflow issue for heterogeneous ensembles on very large data.

  • MultiOutputCalibratedClassifier: support for RBF kernels.

  • BatchedTreeEnsemble: better handling of default SnapRandomForest.

  • BatchedTreeEnsemble: add base_score calculation.

  • BatchedTreeEnsemble: support calling partial_fit after fit.

  • ModelImport: improved error handling.

  • GeneralizedLinearModels: fix issue with RBFSampler transform function on s390x.

API changes:

  • Added generic import_model() function that can detect the ensemble and type task from the PMML file.

  • Added option remap_feature_indices to score imported models using only the features that are listed in the model file.

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 IBM 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 IBM z16™ systems only).

Snap ML v1.8.12 (Oct. 28, 2022)

Bug fixes:

  • BatchedTreeEnsemble: handle case when some classes are not present in batch.

Snap ML v1.8.11 (Oct. 18, 2022)

Packaging changes:

  • Make numpy dependency conditional on Python version.

Snap ML v1.8.10 (Sep. 15, 2022)

Features:

  • Python 3.10 support.

Bug fixes:

  • Do not print NUMA warnings on machines where no NUMA nodes are configured.

Packaging notes:

  • Linux/x86 wheels are now built with manylinux2014 platform tag (manylinux2010 reached EOL in 2020).

  • Runtime numpy dependency is now numpy>=1.21.3 since this is the oldest release that supports Python 3.7, 3.8, 3.9 and 3.10.

Snap ML v1.8.9 (Aug. 11, 2022)

Bug-fixes:

  • Fix overflow issue for heterogeneous BoostingMachines on very large data.

  • Support for RBF kernels in MultiOutputCalibratedClassifier.

Snap ML v1.8.8 (Jul. 20, 2022)

Bug-fixes:

  • Better handling of default SnapRandomForest inside BatchedTreeEnsemble.

Snap ML v1.8.7 (Jun. 20, 2022)

Bug-fixes:

  • Improved classes logic in BatchedTreeEnsemble.

Snap ML v1.8.6 (Jun. 16, 2022)

Bug-fixes:

  • Add base score computation to BatchedTreeEnsemble.

  • Fix issue with binary incompatibility between Linux/MacOS and Windows.

Snap ML v1.8.5 (Apr. 22, 2022)

Bug-fixes:

  • BatchedTreeEnsemble: call to fit is now equivalent to calling partial_fit on first batch.

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 v1.8.0 (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 v1.7.8 (Nov. 19, 2021)

Bug-fixes:

  • Support older machines that do not have AVX2 instructions.

Snap ML v1.7.7 (Jul. 21, 2021)

  • Added support for A100 GPUs

  • Fixed unit-tests that were failing on IBM 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 IBM 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