cuhnsw
cuml
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| cuhnsw | cuml | |
|---|---|---|
| 4 | 10 | |
| 112 | 3,894 | |
| - | 2.0% | |
| 0.0 | 9.3 | |
| about 3 years ago | 6 days ago | |
| Cuda | C++ | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cuhnsw
- [P] CUHNSW - CUDA Implementation of Hierarchical Navigable Small World (HNSW) Graph algorithm for Approximate Nearest Neighbor Search
- [P] CUHNSW - CUDA Implementation of Hierarchical Navigable Small World (HNSW) Graph Algorithm for Approximate Nearest Neighbor Search
- CUHNSW - CUDA implementation of HNSW for Approximate Nearest Neighbor
- CUHNSW - CUDA implementation of Hierarchical Navigable Small World (HNSW) graph algorithm for Approximate Nearest Neighbor (ANN)
cuml
- FLaNK Stack Weekly for 13 November 2023
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Is it possible to run Sklearn models on a GPU?
sklearn can't, bit take a look at cuML (https://github.com/rapidsai/cuml ). It uses the same API as sklearn but executes on GPU.
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[P] Looking for state of the art clustering algorithms
As a companion to the other comments, I'd like to mention that the RAPIDS library cuML provides GPU-accelerated versions of quite a few of the algorithms mentioned in this thread (HDBSCAN, UMAP, SVM, PCA, {Exact, Approximate} Nearest Neighbors, DBSCAN, KMeans, etc.).
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Is there a multi regression model that works on GPU?
CuML
- [D] What's your favorite unpopular/forgotten Machine Learning method?
- Machine Learning with PyTorch and Scikit-Learn – The *New* Python ML Book
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What are the advantages and disadvantages of using GPU for machine learning/ deep learning/ scientific computation over the conventional CPU software acceleration?
Did they implement the clustering algorithm themselves? cuML is a GPU-accelerated scikit-learn-like package that covers many of the common ML algorithms.
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Intel Extension for Scikit-Learn
https://github.com/rapidsai/cuml
> cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU equivalents. For details on performance, see the cuML Benchmarks Notebook.
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GPU Based Kernel-PCA
Cython code
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Python Machine Learning Guy getting started with CUDA. What should I be brushing up on?
Take a look at RAPIDS CUML https://github.com/rapidsai/cuml. It's useful for most common ML algorithms. Feel free to create Github issues for feature requests & bugs.
What are some alternatives?
Open3D - Open3D: A Modern Library for 3D Data Processing
scikit-learn - scikit-learn: machine learning in Python
lightseq - LightSeq: A High Performance Library for Sequence Processing and Generation
scikit-learn-intelex - Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
DOKSparse - sparse DOK tensors on GPU, pytorch
scikit-cuda - Python interface to GPU-powered libraries
FirstCollisionTimestepRarefiedGasSimulator - This simulator computes all possible intersections for a very small timestep for a particle model
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
cudf - cuDF - GPU DataFrame Library
evojax
numba-dpex - Data Parallel Extension for Numba