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cuML 0.7.0 (10 May 2019)

New Features

  • PR #405: Quasi-Newton GLM Solvers
  • PR #277: Add row- and column-wise weighted mean primitive
  • PR #424: Add a grid-sync struct for inter-block synchronization
  • PR #430: Add R-Squared Score to ml primitives
  • PR #463: Add matrix gather to ml primitives
  • PR #435: Expose cumlhandle in cython + developer guide
  • PR #455: Remove default-stream arguement across ml-prims and cuML
  • PR #375: cuml cpp shared library renamed to libcuml++.so
  • PR #444: Add supervised training to UMAP
  • PR #460: Random Forest & Decision Trees (Single-GPU, Classification)
  • PR #491: Add doxygen build target for ml-prims
  • PR #505: Add R-Squared Score to python interface
  • PR #507: Add coordinate descent for lasso and elastic-net
  • PR #511: Add a minmax ml-prim
  • PR #520: Add local build script to mimic gpuCI
  • PR #503: Add column-wise matrix sort primitive
  • PR #525: Add docs build script to cuML
  • PR #528: Remove current KMeans and replace it with a new single GPU implementation built using ML primitives

Improvements

  • PR #481: Refactoring Quasi-Newton to use cumlHandle
  • PR #467: Added validity check on cumlHandle_t
  • PR #461: Rewrote permute and added column major version
  • PR #440: README updates
  • PR #295: Improve build-time and the interface e.g., enable bool-OutType, for distance()
  • PR #390: Update docs version
  • PR #272: Add stream parameters to cublas and cusolver wrapper functions
  • PR #447: Added building and running mlprims tests to CI
  • PR #445: Lower dbscan memory usage by computing adjacency matrix directly
  • PR #431: Add support for fancy iterator input types to LinAlg::reduce_rows_by_key
  • PR #394: Introducing cumlHandle API to dbscan and add example
  • PR #500: Added CI check for black listed CUDA Runtime API calls
  • PR #475: exposing cumlHandle for dbscan from python-side
  • PR #395: Edited the CONTRIBUTING.md file
  • PR #512: generic copy method for copying buffers between device/host
  • PR #533: Add cudatoolkit conda dependency
  • PR #524: Use cmake find blas and find lapack to pass configure options to faiss
  • PR #527: Added notes on UMAP differences from reference implementation
  • PR #540: Use latest release version in update-version CI script
  • PR #552: Re-enable assert in kmeans tests with xfail as needed
  • PR #581: Add shared memory fast col major to row major function back with bound checks

Bug Fixes

  • PR #334: Fixed segfault in ML::cumlHandle_impl::destroyResources
  • PR #349: Developer guide clarifications for cumlHandle and cumlHandle_impl
  • PR #398: Fix CI scripts to allow nightlies to be uploaded
  • PR #399: Skip PCA tests to allow CI to run with driver 418
  • PR #422: Issue in the PCA tests was solved and CI can run with driver 418
  • PR #409: Add entry to gitmodules to ignore build artifacts
  • PR #412: Fix for svdQR function in ml-prims
  • PR #438: Code that depended on FAISS was building everytime.
  • PR #358: Fixed an issue when switching streams on MLCommon::device_buffer and MLCommon::host_buffer
  • PR #434: Fixing bug in CSR tests
  • PR #443: Remove defaults channel from ci scripts
  • PR #384: 64b index arithmetic updates to the kernels inside ml-prims
  • PR #459: Fix for runtime library path of pip package
  • PR #464: Fix for C++11 destructor warning in qn
  • PR #466: Add support for column-major in LinAlg::*Norm methods
  • PR #465: Fixing deadlock issue in GridSync due to consecutive sync calls
  • PR #468: Fix dbscan example build failure
  • PR #470: Fix resource leakage in Kalman filter python wrapper
  • PR #473: Fix gather ml-prim test for change in rng uniform API
  • PR #477: Fixes default stream initialization in cumlHandle
  • PR #480: Replaced qn_fit() declaration with #include of file containing definition to fix linker error
  • PR #495: Update cuDF and RMM versions in GPU ci test scripts
  • PR #499: DEVELOPER_GUIDE.md: fixed links and clarified ML::detail::streamSyncer example
  • PR #506: Re enable ml-prim tests in CI
  • PR #508: Fix for an error with default argument in LinAlg::meanSquaredError
  • PR #519: README.md Updates and adding BUILD.md back
  • PR #526: Fix the issue of wrong results when fit and transform of PCA are called separately
  • PR #531: Fixing missing arguments in updateDevice() for RF
  • PR #543: Exposing dbscan batch size through cython API and fixing broken batching
  • PR #551: Made use of ZLIB_LIBRARIES consistent between ml_test and ml_mg_test
  • PR #557: Modified CI script to run cuML tests before building mlprims and removed lapack flag
  • PR #578: Updated Readme.md to add lasso and elastic-net
  • PR #580: Fixing cython garbage collection bug in KNN
  • PR #577: Use find libz in prims cmake
  • PR #594: fixed cuda-memcheck mean_center test failures

cuML 0.6.1 (09 Apr 2019)

Bug Fixes

  • PR #462 Runtime library path fix for cuML pip package

cuML 0.6.0 (22 Mar 2019)

New Features

  • PR #249: Single GPU Stochastic Gradient Descent for linear regression, logistic regression, and linear svm with L1, L2, and elastic-net penalties.
  • PR #247: Added "proper" CUDA API to cuML
  • PR #235: NearestNeighbors MG Support
  • PR #261: UMAP Algorithm
  • PR #290: NearestNeighbors numpy MG Support
  • PR #303: Reusable spectral embedding / clustering
  • PR #325: Initial support for single process multi-GPU OLS and tSVD
  • PR #271: Initial support for hyperparameter optimization with dask for many models

Improvements

  • PR #144: Dockerfile update and docs for LinearRegression and Kalman Filter.
  • PR #168: Add /ci/gpu/build.sh file to cuML
  • PR #167: Integrating full-n-final ml-prims repo inside cuml
  • PR #198: (ml-prims) Removal of *MG calls + fixed a bug in permute method
  • PR #194: Added new ml-prims for supporting LASSO regression.
  • PR #114: Building faiss C++ api into libcuml
  • PR #64: Using FAISS C++ API in cuML and exposing bindings through cython
  • PR #208: Issue ml-common-3: Math.h: swap thrust::for_each with binaryOp,unaryOp
  • PR #224: Improve doc strings for readable rendering with readthedocs
  • PR #209: Simplify README.md, move build instructions to BUILD.md
  • PR #218: Fix RNG to use given seed and adjust RNG test tolerances.
  • PR #225: Support for generating random integers
  • PR #215: Refactored LinAlg::norm to Stats::rowNorm and added Stats::colNorm
  • PR #234: Support for custom output type and passing index value to main_op in *Reduction kernels
  • PR #230: Refactored the cuda_utils header
  • PR #236: Refactored cuml python package structure to be more sklearn like
  • PR #232: Added reduce_rows_by_key
  • PR #246: Support for 2 vectors in the matrix vector operator
  • PR #244: Fix for single GPU OLS and Ridge to support one column training data
  • PR #271: Added get_params and set_params functions for linear and ridge regression
  • PR #253: Fix for issue #250-reduce_rows_by_key failed memcheck for small nkeys
  • PR #269: LinearRegression, Ridge Python docs update and cleaning
  • PR #322: set_params updated
  • PR #237: Update build instructions
  • PR #275: Kmeans use of faster gpu_matrix
  • PR #288: Add n_neighbors to NearestNeighbors constructor
  • PR #302: Added FutureWarning for deprecation of current kmeans algorithm
  • PR #312: Last minute cleanup before release
  • PR #315: Documentation updating and enhancements
  • PR #330: Added ignored argument to pca.fit_transform to map to sklearn's implemenation
  • PR #342: Change default ABI to ON

Bug Fixes

  • PR #193: Fix AttributeError in PCA and TSVD
  • PR #211: Fixing inconsistent use of proper batch size calculation in DBSCAN
  • PR #202: Adding back ability for users to define their own BLAS
  • PR #201: Pass CMAKE CUDA path to faiss/configure script
  • PR #200 Avoid using numpy via cimport in KNN
  • PR #228: Bug fix: LinAlg::unaryOp with 0-length input
  • PR #279: Removing faiss-gpu references in README
  • PR #321: Fix release script typo
  • PR #327: Update conda requirements for version 0.6 requirements
  • PR #352: Correctly calculating numpy chunk sizing for kNN
  • PR #345: Run python import as part of package build to trigger compilation
  • PR #347: Lowering memory usage of kNN.
  • PR #355: Fixing issues with very large numpy inputs to SPMG OLS and tSVD.
  • PR #357: Removing FAISS requirement from README
  • PR #362: Fix for matVecOp crashing on large input sizes
  • PR #366: Index arithmetic issue fix with TxN_t class
  • PR #376: Disabled kmeans tests since they are currently too sensitive (see #71)
  • PR #380: Allow arbitrary data size on ingress for numba_utils.row_matrix
  • PR #385: Fix for long import cuml time in containers and fix for setup_pip

cuML 0.5.1 (05 Feb 2019)

Bug Fixes

  • PR #189 Avoid using numpy via cimport to prevent ABI issues in Cython compilation

cuML 0.5.0 (28 Jan 2019)

New Features

  • PR #66: OLS Linear Regression
  • PR #44: Distance calculation ML primitives
  • PR #69: Ridge (L2 Regularized) Linear Regression
  • PR #103: Linear Kalman Filter
  • PR #117: Pip install support
  • PR #64: Device to device support from cuML device pointers into FAISS

Improvements

  • PR #56: Make OpenMP optional for building
  • PR #67: Github issue templates
  • PR #44: Refactored DBSCAN to use ML primitives
  • PR #91: Pytest cleanup and sklearn toyset datasets based pytests for kmeans and dbscan
  • PR #75: C++ example to use kmeans
  • PR #117: Use cmake extension to find any zlib installed in system
  • PR #94: Add cmake flag to set ABI compatibility
  • PR #139: Move thirdparty submodules to root and add symlinks to new locations
  • PR #151: Replace TravisCI testing and conda pkg builds with gpuCI
  • PR #164: Add numba kernel for faster column to row major transform
  • PR #114: Adding FAISS to cuml build

Bug Fixes

  • PR #48: CUDA 10 compilation warnings fix
  • PR #51: Fixes to Dockerfile and docs for new build system
  • PR #72: Fixes for GCC 7
  • PR #96: Fix for kmeans stack overflow with high number of clusters
  • PR #105: Fix for AttributeError in kmeans fit method
  • PR #113: Removed old glm python/cython files
  • PR #118: Fix for AttributeError in kmeans predict method
  • PR #125: Remove randomized solver option from PCA python bindings

cuML 0.4.0 (05 Dec 2018)

New Features

Improvements

  • PR #42: New build system: separation of libcuml.so and cuml python package
  • PR #43: Added changelog.md

Bug Fixes

cuML 0.3.0 (30 Nov 2018)

New Features

  • PR #33: Added ability to call cuML algorithms using numpy arrays

Improvements

  • PR #24: Fix references of python package from cuML to cuml and start using versioneer for better versioning
  • PR #40: Added support for refactored cuDF 0.3.0, updated Conda files
  • PR #33: Major python test cleaning, all tests pass with cuDF 0.2.0 and 0.3.0. Preparation for new build system
  • PR #34: Updated batch count calculation logic in DBSCAN
  • PR #35: Beginning of DBSCAN refactor to use cuML mlprims and general improvements

Bug Fixes

  • PR #30: Fixed batch size bug in DBSCAN that caused crash. Also fixed various locations for potential integer overflows
  • PR #28: Fix readthedocs build documentation
  • PR #29: Fix pytests for cuml name change from cuML
  • PR #33: Fixed memory bug that would cause segmentation faults due to numba releasing memory before it was used. Also fixed row major/column major bugs for different algorithms
  • PR #36: Fix kmeans gtest to use device data
  • PR #38: cuda_free bug removed that caused google tests to sometimes pass and sometimes fail randomly
  • PR #39: Updated cmake to correctly link with CUDA libraries, add CUDA runtime linking and include source files in compile target

cuML 0.2.0 (02 Nov 2018)

New Features

  • PR #11: Kmeans algorithm added
  • PR #7: FAISS KNN wrapper added
  • PR #21: Added Conda install support

Improvements

  • PR #15: Added compatibility with cuDF (from prior pyGDF)
  • PR #13: Added FAISS to Dockerfile
  • PR #21: Added TravisCI build system for CI and Conda builds

Bug Fixes

  • PR #4: Fixed explained variance bug in TSVD
  • PR #5: Notebook bug fixes and updated results

cuML 0.1.0

Initial release including PCA, TSVD, DBSCAN, ml-prims and cython wrappers

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