Cuml

New Features

- PR 405: Quasi-Newton GLM Solvers

New Features

- PR 277: Added row- and column-wise weighted mean primitive
- PR 424: Added a grid-sync struct for inter-block synchronization
- PR 430: Adding R-Squared Score to ml primitives
- PR 375: cuml cpp shared library renamed to libcuml++.so

Improvements

- 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 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

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

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

Bug Fixes

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

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

New Features

Improvements

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

Bug Fixes

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