Forward-merge branch-25.06 into branch-25.08 #2675
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…dsai#2626) This PR adds support for strided_matrix_view as an input to `matrix::sample_rows()`. For that, it also adds `raft::make_host_strided_matrix_view` and the support for `strided_matrix_view` as an input to `matrix::gather()`. Rel: rapidsai/cuvs#716 (comment) ## todo - [x] Update gather - [x] Update sample_rows - [x] Update gather test - [x] Update sample_rows test Authors: - tsuki (https://github.com/enp1s0) - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Tamas Bela Feher (https://github.com/tfeher) URL: rapidsai#2626
) This PR reverts shared-workflows branches to `branch-25.06` and ensures builds use CUDA 12.8. CUDA 12.9 will be used in the test matrix but not for builds in RAPIDS 25.06. See rapidsai/build-planning#173 (comment) for more information. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - Gil Forsyth (https://github.com/gforsyth) URL: rapidsai#2669
Python wrapper for the `device_resources_snmg` introduced in rapidsai#2647 Need this in for 25.06 (needed for; cuML python level usage for building knn graph in multi-gpu in UMAP) Authors: - Jinsol Park (https://github.com/jinsolp) Approvers: - Divye Gala (https://github.com/divyegala) URL: rapidsai#2666
Resolves rapidsai#2641 Authors: - Anupam (https://github.com/aamijar) Approvers: - Jinsol Park (https://github.com/jinsolp) - Corey J. Nolet (https://github.com/cjnolet) URL: rapidsai#2648
This updates raft-dask for changes made between dask 2025.2.0 and dask 2025.4.1, which was bumped in rapidsai/rapids-dask-dependency#95. This should fix (some of) the failures in rapidsai/cugraph#5076. Authors: - Tom Augspurger (https://github.com/TomAugspurger) Approvers: - Dante Gama Dessavre (https://github.com/dantegd) URL: rapidsai#2673
…apidsai#2671) The `raft::matrix::sample_rows` function can select rows both from host and device datasets. This PR changes how the case is handled when the dataset is accessible both from host and device. When the dataset pointer is accessible both on host and device, then in theory we can subsample it both with our CPU and GPU implementation. In practice, it is likely that the data is physically located in host memory, therefore it is preferred to process it on the CPU side. This has significance when the dataset is larger than GPU memory. In that case subsampling using the GPU kernels would lead to page trashing and low performance. In some HMM implementations this can also lead to application crash. This PR fixes the problem by preferring CPU side subsampling when the dataset is accessible on the CPU. Authors: - Tamas Bela Feher (https://github.com/tfeher) - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Artem M. Chirkin (https://github.com/achirkin) URL: rapidsai#2671
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