- PR #405: Quasi-Newton GLM Solvers
- 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 #463: Added matrix gather to ml primitives
- PR #435: Exposing 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 #467: Added validity check on cumlHandle_t
- 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
- 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 #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
- 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
- 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
- PR #189 Avoid using numpy via cimport to prevent ABI issues in Cython compilation
- 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
- 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
- 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
- PR #42: New build system: separation of libcuml.so and cuml python package
- PR #43: Added changelog.md
- PR #33: Added ability to call cuML algorithms using numpy arrays
- 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
- 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
- PR #11: Kmeans algorithm added
- PR #7: FAISS KNN wrapper added
- PR #21: Added Conda install support
- 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
- PR #4: Fixed explained variance bug in TSVD
- PR #5: Notebook bug fixes and updated results
Initial release including PCA, TSVD, DBSCAN, ml-prims and cython wrappers