Anupam

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Hi, I am using the sample script in this repository `resnet50_ddp_profiler.py` from https://github.com/pytorch/kineto/blob/main/tb_plugin/examples/resnet50_ddp_profiler.py Using ``` Python3.8 torch=2.0.1 torch-tb-profiler=0.4.3 # built from source ``` In tensorboard in the overview view the...

[FEA] Sparse PCA in C++ Layer

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In the middle of adding the PCA initialization for TSNE, we realized that there is no support yet for sparse inputs for PCA.

[FEA] PCA Initialization for TSNE

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Closes https://github.com/rapidsai/cuml/issues/3458 Add PCA embedding initialization to C++ layer and expose it in Python API. ```python from cuml.manifold import TSNE tsne = TSNE( ... init="pca" # ("random" or "pca") )...

Enable dice metric for UMAP and Pairwise Distances

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Closes https://github.com/rapidsai/cuml/issues/5129 Related to https://github.com/rapidsai/raft/pull/2359 1. Enables dice metric for UMAP 2. Enables dice metric for `cuml.metrics.pairwise_distances`

cupyx.scipy.sparse.linalg.eigsh not returning correct eigenvalues

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### Description The eigenvalues when using cupy eigsh are NaN and not the same as scipy eigsh. By inspecting the output in the _lanczos_fast algorithm that cupy uses, the output...

Documentation for future work: * The new lanczos solver in raft https://github.com/rapidsai/raft/pull/2416 can be integrated into the UMAP spectral initialization algorithm. * After the UMAP_::get_graph step the resulting COO matrix...

Lanczos Solver

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## Lanczos Solver for Sparse Eigen Decomposition We propose a new lanczos solver in raft that fixes the issues present in the previous solver `raft::sparse::solver::detail::computeSmallestEigenvectors`. Specifically we address the following...

Documenting for future work: Given a sparse matrix `A` convert it to a sparse laplacian matrix `L` where `D` is degree matrix. `L = D - A` Ideas on how...

Spectral Embedding

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Resolves #6912 # Usage ```python from sklearn import datasets from cuml.manifold import SpectralEmbedding import cupy as cp # (1500, 3) -> (1500, 2) n_samples = 1500 S_points, S_color = datasets.make_s_curve(n_samples,...