cuML 0.15.0 (Date TBD)

New Features

PR #2581: Added model persistence via joblib in each section of estimator_intro.ipynb
PR #2554: Hashing Vectorizer and general vectorizer improvements
PR #2240: Making Dask models pickleable
PR #2267: CountVectorizer estimator
PR #2261: Exposing new FAISS metrics through Python API
PR #2287: Single-GPU TfidfTransformer implementation
PR #2289: QR SVD solver for MNMG PCA
PR #2312: column-major support for make_blobs
PR #2172: Initial support for auto-ARIMA
PR #2394: Adding cosine & correlation distance for KNN
PR #2392: PCA can accept sparse inputs, and sparse prim for computing covariance
PR #2465: Support pandas 1.0+
PR #2550: Single GPU Target Encoder
PR #2519: Precision recall curve using cupy
PR #2500: Replace UMAP functionality dependency on nvgraph with RAFT Spectral Clustering
PR #2502: cuML Implementation of sklearn.metrics.pairwise_distances
PR #2520: TfidfVectorizer estimator
PR #2211: MNMG KNN Classifier & Regressor
PR #2461: Add KNN Sparse Output Functionality
PR #2615: Incremental PCA
PR #2594: Confidence intervals for ARIMA forecasts
PR #2607: Add support for probability estimates in SVC
PR #2618: SVM class and sample weights
PR #2661: CUDA-11 support for single-gpu code
PR #2322: Sparse FIL forests with 8-byte nodes
PR #2675: Update conda recipes to support CUDA 11

Improvements

PR #2336: Eliminate rmm.device_array usage
PR #2262: Using fully shared PartDescriptor in MNMG decomposiition, linear models, and solvers
PR #2310: Pinning ucx-py to 0.14 to make 0.15 CI pass
PR #1945: enable clang tidy
PR #2339: umap performance improvements
PR #2308: Using fixture for Dask client to eliminate possiblity of not closing
PR #2345: make C++ logger level definition to be the same as python layer
PR #2329: Add short commit hash to conda package name
PR #2362: Implement binary/multi-classification log loss with cupy
PR #2363: Update threshold and make other changes for stress tests
PR #2371: Updating MBSGD tests to use larger batches
PR #2380: Pinning libcumlprims version to ease future updates
PR #2405: Remove references to deprecated RMM headers.
PR #2340: Import ARIMA in the root init file and fix the test_fit_function test
PR #2408: Install meta packages for dependencies
PR #2417: Move doc customization scripts to Jenkins
PR #2427: Moving MNMG decomposition to cuml
PR #2433: Add libcumlprims_mg to CMake
PR #2420: Add and set convert_dtype default to True in estimator fit methods
PR #2411: Refactor Mixin classes and use in classifier/regressor estimators
PR #2442: fix setting RAFT_DIR from the RAFT_PATH env var
PR #2469: Updating KNN c-api to document all arguments
PR #2453: Add CumlArray to API doc
PR #2440: Use Treelite Conda package
PR #2403: Support for input and output type consistency in logistic regression predict_proba
PR #2473: Add metrics.roc_auc_score to API docs. Additional readability and minor docs bug fixes
PR #2468: Add _n_features_in_ attribute to all single GPU estimators that implement fit
PR #2489: Removing explicit FAISS build and adding dependency on libfaiss conda package
PR #2480: Moving MNMG glm and solvers to cuml
PR #2490: Moving MNMG KMeans to cuml
PR #2483: Moving MNMG KNN to cuml
PR #2492: Adding additional assertions to mnmg nearest neighbors pytests
PR #2439: Update dask RF code to have print_detailed function
PR #2431: Match output of classifier predict with target dtype
PR #2237: Refactor RF cython code
PR #2513: Fixing LGTM Analysis Issues
PR #2099: Raise an error when float64 data is used with dask RF
PR #2522: Renaming a few arguments in KNeighbors* to be more readable
PR #2499: Provide access to cuml.DBSCAN core samples
PR #2526: Removing PCA TSQR as a solver due to scalability issues
PR #2536: Update conda upload versions for new supported CUDA/Python
PR #2538: Remove Protobuf dependency
PR #2553: Test pickle protocol 5 support
PR #2570: Accepting single df or array input in train_test_split
PR #2566: Remove deprecated cuDF from_gpu_matrix calls
PR #2583: findpackage.cmake.in template for cmake dependencies
PR #2577: Fully removing NVGraph dependency for CUDA 11 compatibility
PR #2575: Speed up TfidfTransformer
PR #2584: Removing dependency on sklearn's NotFittedError
PR #2591: Generate benchmark datsets using cuml.datasets
PR #2548: Fix limitation on number of rows usable with tSNE and refactor memory allocation
PR #2589: including cuda-11 build fixes into raft
PR #2599: Add Stratified train_test_split
PR #2487: Set classes_ attribute during classifier fit
PR #2605: Reduce memory usage in tSNE
PR #2611: Adding building doxygen docs to gpu ci
PR #2631: Enabling use of gtest conda package for build
PR #2623: Fixing kmeans score() API to be compatible with Scikit-learn
PR #2629: Add naive_bayes api docs
PR #2643: 'dense' and 'sparse' values of storage_type for FIL
PR #2666: Update MBSGD documentation to mention that the model is experimental
PR #2687: Update xgboost version to 1.2.0dev.rapidsai0.15
PR #2684: CUDA 11 conda development environment yml and faiss patch
PR #2648: Replace CNMeM with rmm::mr::pool_memory_resource.
PR #2686: Improve SVM tests
PR #2692: Changin LBFGS log level
PR #2705: Add sum operator and base operator overloader functions to cumlarray
PR #2701: Updating README + Adding ref to UMAP paper

Bug Fixes

PR #2369: Update RF code to fix set_params memory leak
PR #2364: Fix for random projection
PR #2373: Use Treelite Pip package in GPU testing
PR #2376: Update documentation Links
PR #2407: fixed batch count in DBScan for integer overflow case
PR #2413: CumlArray and related methods updates to account for cuDF.Buffer contiguity update
PR #2424: --singlegpu flag fix on build.sh script
PR #2432: Using correct algo_name for UMAP in benchmark tests
PR #2445: Restore access to coef_ property of Lasso
PR #2441: Change p2p_enabled definition to work without ucx
PR #2447: Drop nvstrings
PR #2450: Update local build to use new gpuCI image
PR #2454: Mark RF memleak test as XFAIL, because we can't detect memleak reliably
PR #2455: Use correct field to store data type in LabelEncoder.fit_transform
PR #2475: Fix typo in build.sh
PR #2496: Fixing indentation for simulate_data in test_fil.py
PR #2494: Set QN regularization strength consistent with scikit-learn
PR #2486: Fix cupy input to kmeans init
PR #2497: Changes to accomodate cuDF unsigned categorical changes
PR #2209: Fix FIL benchmark for gpuarray-c input
PR #2507: Import treelite.sklearn
PR #2521: Fixing invalid smem calculation in KNeighborsCLassifier
PR #2515: Increase tolerance for LogisticRegression test
PR #2532: Updating doxygen in new MG headers
PR #2521: Fixing invalid smem calculation in KNeighborsCLassifier
PR #2515: Increase tolerance for LogisticRegression test
PR #2545: Fix documentation of n_iter_without_progress in tSNE Python bindings
PR #2543: Improve numerical stability of QN solver
PR #2544: Fix Barnes-Hut tSNE not using specified post_learning_rate
PR #2558: Disabled a long-running FIL test
PR #2540: Update default value for n_epochs in UMAP to match documentation & sklearn API
PR #2535: Fix issue with incorrect docker image being used in local build script
PR #2542: Fix small memory leak in TSNE
PR #2552: Fixed the length argument of updateDevice calls in RF test
PR #2565: Fix cell allocation code to avoid loops in quad-tree. Prevent NaNs causing infinite descent
PR #2563: Update scipy call for arima gradient test
PR #2569: Fix for cuDF update
PR #2508: Use keyword parameters in sklearn.datasets.make_* functions
PR #2587: Attributes for estimators relying on solvers
PR #2586: Fix SVC decision function data type
PR #2573: Considering managed memory as device type on checking for KMeans
PR #2574: Fixing include path in tsvd_mg.pyx
PR #2506: Fix usage of CumlArray attributes on cuml.common.base.Base
PR #2593: Fix inconsistency in train_test_split
PR #2609: Fix small doxygen issues
PR #2610: Remove cuDF tolist call
PR #2613: Removing thresholds from kmeans score tests (SG+MG)
PR #2616: Small test code fix for pandas dtype tests
PR #2617: Fix floating point precision error in tSNE
PR #2625: Update Estimator notebook to resolve errors
PR #2634: singlegpu build option fixes
PR #2641: [Breaking] Make max_depth in RF compatible with scikit-learn
PR #2650: Make max_depth behave consistently for max_depth > 14
PR #2651: AutoARIMA Python bug fix
PR #2654: Fix for vectorizer concatenations
PR #2655: Fix C++ RF predict function access of rows/samples array
PR #2649: Cleanup sphinx doc warnings for 0.15
PR #2668: Order conversion improvements to account for cupy behavior changes
PR #2669: Revert PR 2655 Revert "Fixes C++ RF predict function"
PR #2683: Fix incorrect "Bad CumlArray Use" error messages on test failures
PR #2695: Fix debug build issue due to incorrect host/device method setup
PR #2709: Fixing OneHotEncoder Overflow Error
PR #2710: Fix SVC doc statement about predic_proba

cuML 0.14.0 (03 Jun 2020)

New Features

PR #1994: Support for distributed OneHotEncoder
PR #1892: One hot encoder implementation with cupy
PR #1655: Adds python bindings for homogeneity score
PR #1704: Adds python bindings for completeness score
PR #1687: Adds python bindings for mutual info score
PR #1980: prim: added a new write-only unary op prim
PR #1867: C++: add logging interface support in cuML based spdlog
PR #1902: Multi class inference in FIL C++ and importing multi-class forests from treelite
PR #1906: UMAP MNMG
PR #2067: python: wrap logging interface in cython
PR #2083: Added dtype, order, and use_full_low_rank to MNMG make_regression
PR #2074: SG and MNMG make_classification
PR #2127: Added order to SG make_blobs, and switch from C++ to cupy based implementation
PR #2057: Weighted k-means
PR #2256: Add a make_arima generator
PR #2245: ElasticNet, Lasso and Coordinate Descent MNMG
PR #2242: Pandas input support with output as NumPy arrays by default
PR #2551: Add cuML RF multiclass prediction using FIL from python
PR #1728: Added notebook testing to gpuCI gpu build

Improvements

PR #1931: C++: enabled doxygen docs for all of the C++ codebase
PR #1944: Support for dask_cudf.core.Series in _extract_partitions
PR #1947: Cleaning up cmake
PR #1927: Use Cython's new_build_ext (if available)
PR #1946: Removed zlib dependency from cmake
PR #1988: C++: cpp bench refactor
PR #1873: Remove usage of nvstring and nvcat from LabelEncoder
PR #1968: Update SVC SVR with cuML Array
PR #1972: updates to our flow to use conda-forge's clang and clang-tools packages
PR #1974: Reduce ARIMA testing time
PR #1984: Enable Ninja build
PR #1985: C++ UMAP parametrizable tests
PR #2005: Adding missing algorithms to cuml benchmarks and notebook
PR #2016: Add capability to setup.py and build.sh to fully clean all cython build files and artifacts
PR #2044: A cuda-memcheck helper wrapper for devs
PR #2018: Using cuml.dask.part_utils.extract_partitions and removing similar, duplicated code
PR #2019: Enable doxygen build in our nightly doc build CI script
PR #1996: Cythonize in parallel
PR #2032: Reduce number of tests for MBSGD to improve CI running time
PR #2031: Encapsulating UCX-py interactions in singleton
PR #2029: Add C++ ARIMA log-likelihood benchmark
PR #2085: Convert TSNE to use CumlArray
PR #2051: Reduce the time required to run dask pca and dask tsvd tests
PR #1981: Using CumlArray in kNN and DistributedDataHandler in dask kNN
PR #2053: Introduce verbosity level in C++ layer instead of boolean verbose flag
PR #2047: Make internal streams non-blocking w.r.t. NULL stream
PR #2048: Random forest testing speedup
PR #2058: Use CumlArray in Random Projection
PR #2068: Updating knn class probabilities to use make_monotonic instead of binary search
PR #2062: Adding random state to UMAP mnmg tests
PR #2064: Speed-up K-Means test
PR #2015: Renaming .h to .cuh in solver, dbscan and svm
PR #2080: Improved import of sparse FIL forests from treelite
PR #2090: Upgrade C++ build to C++14 standard
PR #2089: CI: enabled cuda-memcheck on ml-prims unit-tests during nightly build
PR #2128: Update Dask RF code to reduce the time required for GPU predict to run
PR #2125: Build infrastructure to use RAFT
PR #2131: Update Dask RF fit to use DistributedDataHandler
PR #2055: Update the metrics notebook to use important cuML models
PR #2095: Improved import of src_prims/utils.h, making it less ambiguous
PR #2118: Updating SGD & mini-batch estimators to use CumlArray
PR #2120: Speeding up dask RandomForest tests
PR #1883: Use CumlArray in ARIMA
PR #877: Adding definition of done criteria to wiki
PR #2135: A few optimizations to UMAP fuzzy simplicial set
PR #1914: Change the meaning of ARIMA's intercept to match the literature
PR #2098: Renaming .h to .cuh in decision_tree, glm, pca
PR #2150: Remove deprecated RMM calls in RMM allocator adapter
PR #2146: Remove deprecated kalman filter
PR #2151: Add pytest duration and pytest timeout
PR #2156: Add Docker 19 support to local gpuci build
PR #2178: Reduce duplicated code in RF
PR #2124: Expand tutorial docs and sample notebook
PR #2175: Allow CPU-only and dataset params for benchmark sweeps
PR #2186: Refactor cython code to build OPG structs in common utils file
PR #2180: Add fully single GPU singlegpu python build
PR #2187: CMake improvements to manage conda environment dependencies
PR #2185: Add has_sklearn function and use it in datasets/classification.
PR #2193: Order-independent local shuffle in cuml.dask.make_regression
PR #2204: Update python layer to use the logger interface
PR #2184: Refoctor headers for holtwinters, rproj, tsvd, tsne, umap
PR #2199: Remove unncessary notebooks
PR #2195: Separating fit and transform calls in SG, MNMG PCA to save transform array memory consumption
PR #2201: Re-enabling UMAP repro tests
PR #2132: Add SVM C++ benchmarks
PR #2196: Updates to benchmarks. Moving notebook
PR #2208: Coordinate Descent, Lasso and ElasticNet CumlArray updates
PR #2210: Updating KNN tests to evaluate multiple index partitions
PR #2205: Use timeout to add 2 hour hard limit to dask tests
PR #2212: Improve DBScan batch count / memory estimation
PR #2213: Standardized include statements across all cpp source files, updated copyright on all modified files
PR #2214: Remove utils folder and refactor to common folder
PR #2220: Final refactoring of all src_prims header files following rules as specified in #1675
PR #2225: input_to_cuml_array keep order option, test updates and cleanup
PR #2244: Re-enable slow ARIMA tests as stress tests
PR #2231: Using OPG structs from cuml.common in decomposition algorithms
PR #2257: Update QN and LogisticRegression to use CumlArray
PR #2259: Add CumlArray support to Naive Bayes
PR #2252: Add benchmark for the Gram matrix prims
PR #2263: Faster serialization for Treelite objects with RF
PR #2264: Reduce build time for cuML by using make_blobs from libcuml++ interface
PR #2269: Add docs targets to build.sh and fix python cuml.common docs
PR #2271: Clarify doc for _unique default implementation in OneHotEncoder
PR #2272: Add docs build.sh script to repository
PR #2276: Ensure CumlArray provided dtype conforms
PR #2281: Rely on cuDF's Serializable in CumlArray
PR #2284: Reduce dataset size in SG RF notebook to reduce run time of sklearn
PR #2285: Increase the threshold for elastic_net test in dask/test_coordinate_descent
PR #2314: Update FIL default values, documentation and test
PR #2316: 0.14 release docs additions and fixes
PR #2320: Add prediction notes to RF docs
PR #2323: Change verbose levels and parameter name to match Scikit-learn API
PR #2324: Raise an error if n_bins > number of training samples in RF
PR #2335: Throw a warning if treelite cannot be imported and load_from_sklearn is used

Bug Fixes

PR #1939: Fix syntax error in cuml.common.array
PR #1941: Remove c++ cuda flag that was getting duplicated in CMake
PR #1971: python: Correctly honor --singlegpu option and CUML_BUILD_PATH env variable
PR #1969: Update libcumlprims to 0.14
PR #1973: Add missing mg files for setup.py --singlegpu flag
PR #1993: Set umap_transform_reproducibility tests to xfail
PR #2004: Refactoring the arguments to plant() call
PR #2017: Fixing memory issue in weak cc prim
PR #2028: Skipping UMAP knn reproducibility tests until we figure out why its failing in CUDA 10.2
PR #2024: Fixed cuda-memcheck errors with sample-without-replacement prim
PR #1540: prims: support for custom math-type used for computation inside adjusted rand index prim
PR #2077: dask-make blobs arguments to match sklearn
PR #2059: Make all Scipy imports conditional
PR #2078: Ignore negative cache indices in get_vecs
PR #2084: Fixed cuda-memcheck errors with COO unit-tests
PR #2087: Fixed cuda-memcheck errors with dispersion prim
PR #2096: Fixed syntax error with nightly build command for memcheck unit-tests
PR #2115: Fixed contingency matrix prim unit-tests for computing correct golden values
PR #2107: Fix PCA transform
PR #2109: input_to_cuml_array cuda_array_interface bugfix
PR #2117: cuDF array exception small fixes
PR #2139: CumlArray for adjusted_rand_score
PR #2140: Returning self in fit model functions
PR #2144: Remove GPU arch < 60 from CMake build
PR #2153: Added missing namespaces to some Decision Tree files
PR #2155: C++: fix doxygen build break
PR #2161: Replacing depreciated bruteForceKnn
PR #2162: Use stream in transpose prim
PR #2165: Fit function test correction
PR #2166: Fix handling of temp file in RF pickling
PR #2176: C++: fix for adjusted rand index when input array is all zeros
PR #2179: Fix clang tools version in libcuml recipe
PR #2183: Fix RAFT in nightly package
PR #2191: Fix placement of SVM parameter documentation and add examples
PR #2212: Fix DBScan results (no propagation of labels through border points)
PR #2215: Fix the printing of forest object
PR #2217: Fix opg_utils naming to fix singlegpu build
PR #2223: Fix bug in ARIMA C++ benchmark
PR #2224: Temporary fix for CI until new Dask version is released
PR #2228: Update to use reduce_ex in CumlArray to override cudf.Buffer
PR #2249: Fix bug in UMAP continuous target metrics
PR #2258: Fix doxygen build break
PR #2255: Set random_state for train_test_split function in dask RF
PR #2275: Fix RF fit memory leak
PR #2274: Fix parameter name verbose to verbosity in mnmg OneHotEncoder
PR #2277: Updated cub repo path and branch name
PR #2282: Fix memory leak in Dask RF concatenation
PR #2301: Scaling KNN dask tests sample size with n GPUs
PR #2293: Contiguity fixes for input_to_cuml_array and train_test_split
PR #2295: Fix convert_to_dtype copy even with same dtype
PR #2305: Fixed race condition in DBScan
PR #2354: Fix broken links in README
PR #2619: Explicitly skip raft test folder for pytest 6.0.0

cuML 0.13.0 (31 Mar 2020)

New Features

PR #1777: Python bindings for entropy
PR #1742: Mean squared error implementation with cupy
PR #1817: Confusion matrix implementation with cupy (SNSG and MNMG)
PR #1766: Mean absolute error implementation with cupy
PR #1766: Mean squared log error implementation with cupy
PR #1635: cuML Array shim and configurable output added to cluster methods
PR #1586: Seasonal ARIMA
PR #1683: cuml.dask make_regression
PR #1689: Add framework for cuML Dask serializers
PR #1709: Add decision_function() and predict_proba() for LogisticRegression
PR #1714: Add print_env.sh file to gather important environment details
PR #1750: LinearRegression CumlArray for configurable output
PR #1814: ROC AUC score implementation with cupy
PR #1767: Single GPU decomposition models configurable output
PR #1646: Using FIL to predict in MNMG RF
PR #1778: Make cuML Handle picklable
PR #1738: cuml.dask refactor beginning and dask array input option for OLS, Ridge and KMeans
PR #1874: Add predict_proba function to RF classifier
PR #1815: Adding KNN parameter to UMAP
PR #1978: Adding predict_proba function to dask RF

Improvements

PR #1644: Add predict_proba() for FIL binary classifier
PR #1620: Pickling tests now automatically finds all model classes inheriting from cuml.Base
PR #1637: Update to newer treelite version with XGBoost 1.0 compatibility
PR #1632: Fix MBSGD models inheritance, they now inherits from cuml.Base
PR #1628: Remove submodules from cuML
PR #1755: Expose the build_treelite function for python
PR #1649: Add the fil_sparse_format variable option to RF API
PR #1647: storage_type=AUTO uses SPARSE for large models
PR #1668: Update the warning statement thrown in RF when the seed is set but n_streams is not 1
PR #1662: use of direct cusparse calls for coo2csr, instead of depending on nvgraph
PR #1747: C++: dbscan performance improvements and cleanup
PR #1697: Making trustworthiness batchable and using proper workspace
PR #1721: Improving UMAP pytests
PR #1717: Call rmm_cupy_allocator for CuPy allocations
PR #1718: Import using_allocator from cupy.cuda
PR #1723: Update RF Classifier to throw an exception for multi-class pickling
PR #1726: Decorator to allocate CuPy arrays with RMM
PR #1719: UMAP random seed reproducibility
PR #1748: Test serializing CumlArray objects
PR #1776: Refactoring pca/tsvd distributed
PR #1762: Update CuPy requirement to 7
PR #1768: C++: Different input and output types for add and subtract prims
PR #1790: Add support for multiple seeding in k-means++
PR #1805: Adding new Dask cuda serializers to naive bayes + a trivial perf update
PR #1812: C++: bench: UMAP benchmark cases added
PR #1795: Add capability to build CumlArray from bytearray/memoryview objects
PR #1824: C++: improving the performance of UMAP algo
PR #1816: Add ARIMA notebook
PR #1856: Update docs for 0.13
PR #1827: Add HPO demo Notebook
PR #1825: --nvtx option in build.sh
PR #1847: Update XGBoost version for CI
PR #1837: Simplify cuML Array construction
PR #1848: Rely on subclassing for cuML Array serialization
PR #1866: Minimizing client memory pressure on Naive Bayes
PR #1788: Removing complexity bottleneck in S-ARIMA
PR #1873: Remove usage of nvstring and nvcat from LabelEncoder
PR #1891: Additional improvements to naive bayes tree reduction

Bug Fixes

PR #1835 : Fix calling default RF Classification always
PT #1904: replace cub sort
PR #1833: Fix depth issue in shallow RF regression estimators
PR #1770: Warn that KalmanFilter is deprecated
PR #1775: Allow CumlArray to work with inputs that have no 'strides' in array interface
PR #1594: Train-test split is now reproducible
PR #1590: Fix destination directory structure for run-clang-format.py
PR #1611: Fixing pickling errors for KNN classifier and regressor
PR #1617: Fixing pickling issues for SVC and SVR
PR #1634: Fix title in KNN docs
PR #1627: Adding a check for multi-class data in RF classification
PR #1654: Skip treelite patch if its already been applied
PR #1661: Fix nvstring variable name
PR #1673: Using struct for caching dlsym state in communicator
PR #1659: TSNE - introduce 'convert_dtype' and refactor class attr 'Y' to 'embedding_'
PR #1672: Solver 'svd' in Linear and Ridge Regressors when n_cols=1
PR #1670: Lasso & ElasticNet - cuml Handle added
PR #1671: Update for accessing cuDF Series pointer
PR #1652: Support XGBoost 1.0+ models in FIL
PR #1702: Fix LightGBM-FIL validation test
PR #1701: test_score kmeans test passing with newer cupy version
PR #1706: Remove multi-class bug from QuasiNewton
PR #1699: Limit CuPy to <7.2 temporarily
PR #1708: Correctly deallocate cuML handles in Cython
PR #1730: Fixes to KF for test stability (mainly in CUDA 10.2)
PR #1729: Fixing naive bayes UCX serialization problem in fit()
PR #1749: bug fix rf classifier/regressor on seg fault in bench
PR #1751: Updated RF documentation
PR #1765: Update the checks for using RF GPU predict
PR #1787: C++: unit-tests to check for RF accuracy. As well as a bug fix to improve RF accuracy
PR #1793: Updated fil pyx to solve memory leakage issue
PR #1810: Quickfix - chunkage in dask make_regression
PR #1842: DistributedDataHandler not properly setting 'multiple'
PR #1849: Critical fix in ARIMA initial estimate
PR #1851: Fix for cuDF behavior change for multidimensional arrays
PR #1852: Remove Thrust warnings
PR #1868: Turning off IPC caching until it is fixed in UCX-py/UCX
PR #1876: UMAP exponential decay parameters fix
PR #1887: Fix hasattr for missing attributes on base models
PR #1877: Remove resetting index in shuffling in train_test_split
PR #1893: Updating UCX in comms to match current UCX-py
PR #1888: Small train_test_split test fix
PR #1899: Fix dask extract_partitions(), remove transformation as instance variable in PCA and TSVD and match sklearn APIs
PR #1920: Temporarily raising threshold for UMAP reproducibility tests
PR #1918: Create memleak fixture to skip memleak tests in CI for now
PR #1926: Update batch matrix test margins
PR #1925: Fix failing dask tests
PR #1936: Update DaskRF regression test to xfail
PR #1932: Isolating cause of make_blobs failure
PR #1951: Dask Random forest regression CPU predict bug fix
PR #1948: Adjust BatchedMargin margin and disable tests temporarily
PR #1950: Fix UMAP test failure

cuML 0.12.0 (04 Feb 2020)

New Features

PR #1483: prims: Fused L2 distance and nearest-neighbor prim
PR #1494: bench: ml-prims benchmark
PR #1514: bench: Fused L2 NN prim benchmark
PR #1411: Cython side of MNMG OLS
PR #1520: Cython side of MNMG Ridge Regression
PR #1516: Suppor Vector Regression (epsilon-SVR)

Improvements

PR #1638: Update cuml/docs/README.md
PR #1468: C++: updates to clang format flow to make it more usable among devs
PR #1473: C++: lazy initialization of "costly" resources inside cumlHandle
PR #1443: Added a new overloaded GEMM primitive
PR #1489: Enabling deep trees using Gather tree builder
PR #1463: Update FAISS submodule to 1.6.1
PR #1488: Add codeowners
PR #1432: Row-major (C-style) GPU arrays for benchmarks
PR #1490: Use dask master instead of conda package for testing
PR #1375: Naive Bayes & Distributed Naive Bayes
PR #1377: Add GPU array support for FIL benchmarking
PR #1493: kmeans: add tiling support for 1-NN computation and use fusedL2-1NN prim for L2 distance metric
PR #1532: Update CuPy to >= 6.6 and allow 7.0
PR #1528: Re-enabling KNN using dynamic library loading for UCX in communicator
PR #1545: Add conda environment version updates to ci script
PR #1541: Updates for libcudf++ Python refactor
PR #1555: FIL-SKL, an SKLearn-based benchmark for FIL
PR #1537: Improve pickling and scoring suppport for many models to support hyperopt
PR #1551: Change custom kernel to cupy for col/row order transform
PR #1533: C++: interface header file separation for SVM
PR #1560: Helper function to allocate all new CuPy arrays with RMM memory management
PR #1570: Relax nccl in conda recipes to >=2.4 (matching CI)
PR #1578: Add missing function information to the cuML documenataion
PR #1584: Add has_scipy utility function for runtime check
PR #1583: API docs updates for 0.12
PR #1591: Updated FIL documentation

Bug Fixes

PR #1470: Documentation: add make_regression, fix ARIMA section
PR #1482: Updated the code to remove sklearn from the mbsgd stress test
PR #1491: Update dev environments for 0.12
PR #1512: Updating setup_cpu() in SpeedupComparisonRunner
PR #1498: Add build.sh to code owners
PR #1505: cmake: added correct dependencies for prims-bench build
PR #1534: Removed TODO comment in create_ucp_listeners()
PR #1548: Fixing umap extra unary op in knn graph
PR #1547: Fixing MNMG kmeans score. Fixing UMAP pickling before fit(). Fixing UMAP test failures.
PR #1557: Increasing threshold for kmeans score
PR #1562: Increasing threshold even higher
PR #1564: Fixed a typo in function cumlMPICommunicator_impl::syncStream
PR #1569: Remove Scikit-learn exception and depedenncy in SVM
PR #1575: Add missing dtype parameter in call to strides to order for CuPy 6.6 code path
PR #1574: Updated the init file to include SVM
PR #1589: Fixing the default value for RF and updating mnmg predict to accept cudf
PR #1601: Fixed wrong datatype used in knn voting kernel

cuML 0.11.0 (11 Dec 2019)

New Features

PR #1295: Cython side of MNMG PCA
PR #1218: prims: histogram prim
PR #1129: C++: Separate include folder for C++ API distribution
PR #1282: OPG KNN MNMG Code (disabled for 0.11)
PR #1242: Initial implementation of FIL sparse forests
PR #1194: Initial ARIMA time-series modeling support.
PR #1286: Importing treelite models as FIL sparse forests
PR #1285: Fea minimum impurity decrease RF param
PR #1301: Add make_regression to generate regression datasets
PR #1322: RF pickling using treelite, protobuf and FIL
PR #1332: Add option to cuml.dask make_blobs to produce dask array
PR #1307: Add RF regression benchmark
PR #1327: Update the code to build treelite with protobuf
PR #1289: Add Python benchmarking support for FIL
PR #1371: Cython side of MNMG tSVD
PR #1386: Expose SVC decision function value

Improvements

PR #1170: Use git to clone subprojects instead of git submodules
PR #1239: Updated the treelite version
PR #1225: setup.py clone dependencies like cmake and correct include paths
PR #1224: Refactored FIL to prepare for sparse trees
PR #1249: Include libcuml.so C API in installed targets
PR #1259: Conda dev environment updates and use libcumlprims current version in CI
PR #1277: Change dependency order in cmake for better printing at compile time
PR #1264: Add -s flag to GPU CI pytest for better error printing
PR #1271: Updated the Ridge regression documentation
PR #1283: Updated the cuMl docs to include MBSGD and adjusted_rand_score
PR #1300: Lowercase parameter versions for FIL algorithms
PR #1312: Update CuPy to version 6.5 and use conda-forge channel
PR #1336: Import SciKit-Learn models into FIL
PR #1314: Added options needed for ASVDb output (CUDA ver, etc.), added option to select algos
PR #1335: Options to print available algorithms and datasets in the Python benchmark
PR #1338: Remove BUILD_ABI references in CI scripts
PR #1340: Updated unit tests to uses larger dataset
PR #1351: Build treelite temporarily for GPU CI testing of FIL Scikit-learn model importing
PR #1367: --test-split benchmark parameter for train-test split
PR #1360: Improved tests for importing SciKit-Learn models into FIL
PR #1368: Add --num-rows benchmark command line argument
PR #1351: Build treelite temporarily for GPU CI testing of FIL Scikit-learn model importing
PR #1366: Modify train_test_split to use CuPy and accept device arrays
PR #1258: Documenting new MPI communicator for multi-node multi-GPU testing
PR #1345: Removing deprecated should_downcast argument
PR #1362: device_buffer in UMAP + Sparse prims
PR #1376: AUTO value for FIL algorithm
PR #1408: Updated pickle tests to delete the pre-pickled model to prevent pointer leakage
PR #1357: Run benchmarks multiple times for CI
PR #1382: ARIMA optimization: move functions to C++ side
PR #1392: Updated RF code to reduce duplication of the code
PR #1444: UCX listener running in its own isolated thread
PR #1445: Improved performance of FIL sparse trees
PR #1431: Updated API docs
PR #1441: Remove unused CUDA conda labels
PR #1439: Match sklearn 0.22 default n_estimators for RF and fix test errors
PR #1461: Add kneighbors to API docs

Bug Fixes

PR #1281: Making rng.h threadsafe
PR #1212: Fix cmake git cloning always running configure in subprojects
PR #1261: Fix comms build errors due to cuml++ include folder changes
PR #1267: Update build.sh for recent change of building comms in main CMakeLists
PR #1278: Removed incorrect overloaded instance of eigJacobi
PR #1302: Updates for numba 0.46
PR #1313: Updated the RF tests to set the seed and n_streams
PR #1319: Using machineName arg passed in instead of default for ASV reporting
PR #1326: Fix illegal memory access in make_regression (bounds issue)
PR #1330: Fix C++ unit test utils for better handling of differences near zero
PR #1342: Fix to prevent memory leakage in Lasso and ElasticNet
PR #1337: Fix k-means init from preset cluster centers
PR #1354: Fix SVM gamma=scale implementation
PR #1344: Change other solver based methods to create solver object in init
PR #1373: Fixing a few small bugs in make_blobs and adding asserts to pytests
PR #1361: Improve SMO error handling
PR #1384: Lower expectations on batched matrix tests to prevent CI failures
PR #1380: Fix memory leaks in ARIMA
PR #1391: Lower expectations on batched matrix tests even more
PR #1394: Warning added in svd for cuda version 10.1
PR #1407: Resolved RF predict issues and updated RF docstring
PR #1401: Patch for lbfgs solver for logistic regression with no l1 penalty
PR #1416: train_test_split numba and rmm device_array output bugfix
PR #1419: UMAP pickle tests are using wrong n_neighbors value for trustworthiness
PR #1438: KNN Classifier to properly return Dataframe with Dataframe input
PR #1425: Deprecate seed and use random_state similar to Scikit-learn in train_test_split
PR #1458: Add joblib as an explicit requirement
PR #1474: Defer knn mnmg to 0.12 nightly builds and disable ucx-py dependency

cuML 0.10.0 (16 Oct 2019)

New Features

PR #1148: C++ benchmark tool for c++/CUDA code inside cuML
PR #1071: Selective eigen solver of cuSolver
PR #1073: Updating RF wrappers to use FIL for GPU accelerated prediction
PR #1104: CUDA 10.1 support
PR #1113: prims: new batched make-symmetric-matrix primitive
PR #1112: prims: new batched-gemv primitive
PR #855: Added benchmark tools
PR #1149 Add YYMMDD to version tag for nightly conda packages
PR #892: General Gram matrices prim
PR #912: Support Vector Machine
PR #1274: Updated the RF score function to use GPU predict

Improvements

PR #961: High Peformance RF; HIST algo
PR #1028: Dockerfile updates after dir restructure. Conda env yaml to add statsmodels as a dependency
PR #1047: Consistent OPG interface for kmeans, based on internal libcumlprims update
PR #763: Add examples to train_test_split documentation
PR #1093: Unified inference kernels for different FIL algorithms
PR #1076: Paying off some UMAP / Spectral tech debt.
PR #1086: Ensure RegressorMixin scorer uses device arrays
PR #1110: Adding tests to use default values of parameters of the models
PR #1108: input_to_host_array function in input_utils for input processing to host arrays
PR #1114: K-means: Exposing useful params, removing unused params, proxying params in Dask
PR #1138: Implementing ANY_RANK semantics on irecv
PR #1142: prims: expose separate InType and OutType for unaryOp and binaryOp
PR #1115: Moving dask_make_blobs to cuml.dask.datasets. Adding conversion to dask.DataFrame
PR #1136: CUDA 10.1 CI updates
PR #1135: K-means: add boundary cases for kmeans||, support finer control with convergence
PR #1163: Some more correctness improvements. Better verbose printing
PR #1165: Adding except + in all remaining cython
PR #1186: Using LocalCUDACluster Pytest fixture
PR #1173: Docs: Barnes Hut TSNE documentation
PR #1176: Use new RMM API based on Cython
PR #1219: Adding custom bench_func and verbose logging to cuml.benchmark
PR #1247: Improved MNMG RF error checking

Bug Fixes

PR #1231: RF respect number of cuda streams from cuml handle
PR #1230: Rf bugfix memleak in regression
PR #1208: compile dbscan bug
PR #1016: Use correct libcumlprims version in GPU CI
PR #1040: Update version of numba in development conda yaml files
PR #1043: Updates to accomodate cuDF python code reorganization
PR #1044: Remove nvidia driver installation from ci/cpu/build.sh
PR #991: Barnes Hut TSNE Memory Issue Fixes
PR #1075: Pinning Dask version for consistent CI results
PR #990: Barnes Hut TSNE Memory Issue Fixes
PR #1066: Using proper set of workers to destroy nccl comms
PR #1072: Remove pip requirements and setup
PR #1074: Fix flake8 CI style check
PR #1087: Accuracy improvement for sqrt/log in RF max_feature
PR #1088: Change straggling numba python allocations to use RMM
PR #1106: Pinning Distributed version to match Dask for consistent CI results
PR #1116: TSNE CUDA 10.1 Bug Fixes
PR #1132: DBSCAN Batching Bug Fix
PR #1162: DASK RF random seed bug fix
PR #1164: Fix check_dtype arg handling for input_to_dev_array
PR #1171: SVM prediction bug fix
PR #1177: Update dask and distributed to 2.5
PR #1204: Fix SVM crash on Turing
PR #1199: Replaced sprintf() with snprintf() in THROW()
PR #1205: Update dask-cuda in yml envs
PR #1211: Fixing Dask k-means transform bug and adding test
PR #1236: Improve fix for SMO solvers potential crash on Turing
PR #1251: Disable compiler optimization for CUDA 10.1 for distance prims
PR #1260: Small bugfix for major conversion in input_utils
PR #1276: Fix float64 prediction crash in test_random_forest

cuML 0.9.0 (21 Aug 2019)

New Features

PR #894: Convert RF to treelite format
PR #826: Jones transformation of params for ARIMA models timeSeries ml-prim
PR #697: Silhouette Score metric ml-prim
PR #674: KL Divergence metric ml-prim
PR #787: homogeneity, completeness and v-measure metrics ml-prim
PR #711: Mutual Information metric ml-prim
PR #724: Entropy metric ml-prim
PR #766: Expose score method based on inertia for KMeans
PR #823: prims: cluster dispersion metric
PR #816: Added inverse_transform() for LabelEncoder
PR #789: prims: sampling without replacement
PR #813: prims: Col major istance prim
PR #635: Random Forest & Decision Tree Regression (Single-GPU)
PR #819: Forest Inferencing Library (FIL)
PR #829: C++: enable nvtx ranges
PR #835: Holt-Winters algorithm
PR #837: treelite for decision forest exchange format
PR #871: Wrapper for FIL
PR #870: make_blobs python function
PR #881: wrappers for accuracy_score and adjusted_rand_score functions
PR #840: Dask RF classification and regression
PR #870: make_blobs python function
PR #879: import of treelite models to FIL
PR #892: General Gram matrices prim
PR #883: Adding MNMG Kmeans
PR #930: Dask RF
PR #882: TSNE - T-Distributed Stochastic Neighbourhood Embedding
PR #624: Internals API & Graph Based Dimensionality Reductions Callback
PR #926: Wrapper for FIL
PR #994: Adding MPI comm impl for testing / benchmarking MNMG CUDA
PR #960: Enable using libcumlprims for MG algorithms/prims

Improvements

PR #822: build: build.sh update to club all make targets together
PR #807: Added development conda yml files
PR #840: Require cmake >= 3.14
PR #832: Stateless Decision Tree and Random Forest API
PR #857: Small modifications to comms for utilizing IB w/ Dask
PR #851: Random forest Stateless API wrappers
PR #865: High Performance RF
PR #895: Pretty prints arguments!
PR #920: Add an empty marker kernel for tracing purposes
PR #915: syncStream added to cumlCommunicator
PR #922: Random Forest support in FIL
PR #911: Update headers to credit CannyLabs BH TSNE implementation
PR #918: Streamline CUDA_REL environment variable
PR #924: kmeans: updated APIs to be stateless, refactored code for mnmg support
PR #950: global_bias support in FIL
PR #773: Significant improvements to input checking of all classes and common input API for Python
PR #957: Adding docs to RF & KMeans MNMG. Small fixes for release
PR #965: Making dask-ml a hard dependency
PR #976: Update api.rst for new 0.9 classes
PR #973: Use cudaDeviceGetAttribute instead of relying on cudaDeviceProp object being passed
PR #978: Update README for 0.9
PR #1009: Fix references to notebooks-contrib
PR #1015: Ability to control the number of internal streams in cumlHandle_impl via cumlHandle
PR #1175: Add more modules to docs ToC

Bug Fixes

PR #923: Fix misshapen level/trend/season HoltWinters output
PR #831: Update conda package dependencies to cudf 0.9
PR #772: Add missing cython headers to SGD and CD
PR #849: PCA no attribute trans_input_ transform bug fix
PR #869: Removing incorrect information from KNN Docs
PR #885: libclang installation fix for GPUCI
PR #896: Fix typo in comms build instructions
PR #921: Fix build scripts using incorrect cudf version
PR #928: TSNE Stability Adjustments
PR #934: Cache cudaDeviceProp in cumlHandle for perf reasons
PR #932: Change default param value for RF classifier
PR #949: Fix dtype conversion tests for unsupported cudf dtypes
PR #908: Fix local build generated file ownerships
PR #983: Change RF max_depth default to 16
PR #987: Change default values for knn
PR #988: Switch to exact tsne
PR #991: Cleanup python code in cuml.dask.cluster
PR #996: ucx_initialized being properly set in CommsContext
PR #1007: Throws a well defined error when mutigpu is not enabled
PR #1018: Hint location of nccl in build.sh for CI
PR #1022: Using random_state to make K-Means MNMG tests deterministic
PR #1034: Fix typos and formatting issues in RF docs
PR #1052: Fix the rows_sample dtype to float

cuML 0.8.0 (27 June 2019)

New Features

PR #652: Adjusted Rand Index metric ml-prim
PR #679: Class label manipulation ml-prim
PR #636: Rand Index metric ml-prim
PR #515: Added Random Projection feature
PR #504: Contingency matrix ml-prim
PR #644: Add train_test_split utility for cuDF dataframes
PR #612: Allow Cuda Array Interface, Numba inputs and input code refactor
PR #641: C: Separate C-wrapper library build to generate libcuml.so
PR #631: Add nvcategory based ordinal label encoder
PR #681: Add MBSGDClassifier and MBSGDRegressor classes around SGD
PR #705: Quasi Newton solver and LogisticRegression Python classes
PR #670: Add test skipping functionality to build.sh
PR #678: Random Forest Python class
PR #684: prims: make_blobs primitive
PR #673: prims: reduce cols by key primitive
PR #812: Add cuML Communications API & consolidate Dask cuML

Improvements

PR #597: C++ cuML and ml-prims folder refactor
PR #590: QN Recover from numeric errors
PR #482: Introduce cumlHandle for pca and tsvd
PR #573: Remove use of unnecessary cuDF column and series copies
PR #601: Cython PEP8 cleanup and CI integration
PR #596: Introduce cumlHandle for ols and ridge
PR #579: Introduce cumlHandle for cd and sgd, and propagate C++ errors in cython level for cd and sgd
PR #604: Adding cumlHandle to kNN, spectral methods, and UMAP
PR #616: Enable clang-format for enforcing coding style
PR #618: CI: Enable copyright header checks
PR #622: Updated to use 0.8 dependencies
PR #626: Added build.sh script, updated CI scripts and documentation
PR #633: build: Auto-detection of GPU_ARCHS during cmake
PR #650: Moving brute force kNN to prims. Creating stateless kNN API.
PR #662: C++: Bulk clang-format updates
PR #671: Added pickle pytests and correct pickling of Base class
PR #675: atomicMin/Max(float, double) with integer atomics and bit flipping
PR #677: build: 'deep-clean' to build.sh to clean faiss build as well
PR #683: Use stateless c++ API in KNN so that it can be pickled properly
PR #686: Use stateless c++ API in UMAP so that it can be pickled properly
PR #695: prims: Refactor pairwise distance
PR #707: Added stress test and updated documentation for RF
PR #701: Added emacs temporary file patterns to .gitignore
PR #606: C++: Added tests for host_buffer and improved device_buffer and host_buffer implementation
PR #726: Updated RF docs and stress test
PR #730: Update README and RF docs for 0.8
PR #744: Random projections generating binomial on device. Fixing tests.
PR #741: Update API docs for 0.8
PR #754: Pickling of UMAP/KNN
PR #753: Made PCA and TSVD picklable
PR #746: LogisticRegression and QN API docstrings
PR #820: Updating DEVELOPER GUIDE threading guidelines

Bug Fixes

PR #584: Added missing virtual destructor to deviceAllocator and hostAllocator
PR #620: C++: Removed old unit-test files in ml-prims
PR #627: C++: Fixed dbscan crash issue filed in 613
PR #640: Remove setuptools from conda run dependency
PR #646: Update link in contributing.md
PR #649: Bug fix to LinAlg::reduce_rows_by_key prim filed in issue #648
PR #666: fixes to gitutils.py to resolve both string decode and handling of uncommitted files
PR #676: Fix template parameters in bernoulli() implementation.
PR #685: Make CuPy optional to avoid nccl conda package conflicts
PR #687: prims: updated tolerance for reduce_cols_by_key unit-tests
PR #689: Removing extra prints from NearestNeighbors cython
PR #718: Bug fix for DBSCAN and increasing batch size of sgd
PR #719: Adding additional checks for dtype of the data
PR #736: Bug fix for RF wrapper and .cu print function
PR #547: Fixed issue if C++ compiler is specified via CXX during configure.
PR #759: Configure Sphinx to render params correctly
PR #762: Apply threshold to remove flakiness of UMAP tests.
PR #768: Fixing memory bug from stateless refactor
PR #782: Nearest neighbors checking properly whether memory should be freed
PR #783: UMAP was using wrong size for knn computation
PR #776: Hotfix for self.variables in RF
PR #777: Fix numpy input bug
PR #784: Fix jit of shuffle_idx python function
PR #790: Fix rows_sample input type for RF
PR #793: Fix for dtype conversion utility for numba arrays without cupy installed
PR #806: Add a seed for sklearn model in RF test file
PR #843: Rf quantile fix

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 #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 #516: Added Trustworthiness score feature
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 #407: Test files to run stress, correctness and unit tests for cuml algos
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
PR #592: More efficient matrix copy/reverse methods
PR #721: Added pickle tests for DBSCAN and Random Projections

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
PR #572: Pulling DBSCAN components into reusable primitives

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
PR #630: Fixing a missing kneighbors in nearest neighbors python proxy

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

Files

CHANGELOG.md

Latest commit

History

CHANGELOG.md

File metadata and controls

cuML 0.15.0 (Date TBD)

New Features

Improvements

Bug Fixes

cuML 0.14.0 (03 Jun 2020)

New Features

Improvements

Bug Fixes

cuML 0.13.0 (31 Mar 2020)

New Features

Improvements

Bug Fixes

cuML 0.12.0 (04 Feb 2020)

New Features

Improvements

Bug Fixes

cuML 0.11.0 (11 Dec 2019)

New Features

Improvements

Bug Fixes

cuML 0.10.0 (16 Oct 2019)

New Features

Improvements

Bug Fixes

cuML 0.9.0 (21 Aug 2019)

New Features

Improvements

Bug Fixes

cuML 0.8.0 (27 June 2019)

New Features

Improvements

Bug Fixes

cuML 0.7.0 (10 May 2019)

New Features

Improvements

Bug Fixes

cuML 0.6.1 (09 Apr 2019)

Bug Fixes

cuML 0.6.0 (22 Mar 2019)

New Features

Improvements

Bug Fixes

cuML 0.5.1 (05 Feb 2019)

Bug Fixes

cuML 0.5.0 (28 Jan 2019)

New Features

Improvements

Bug Fixes

cuML 0.4.0 (05 Dec 2018)

New Features

Improvements

Bug Fixes

cuML 0.3.0 (30 Nov 2018)

New Features

Improvements

Bug Fixes

cuML 0.2.0 (02 Nov 2018)

New Features

Improvements

Bug Fixes

cuML 0.1.0