dpmmpythonStreaming
cuml
dpmmpythonStreaming | cuml | |
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3 | 10 | |
14 | 3,903 | |
- | 1.1% | |
0.0 | 9.3 | |
over 1 year ago | 5 days ago | |
Python | C++ | |
MIT License | Apache License 2.0 |
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dpmmpythonStreaming
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[P] Looking for state of the art clustering algorithms
Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
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[R] Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
Code (Python wrapper): https://github.com/BGU-CS-VIL/dpmmpythonStreaming
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[R] DeepDPM: Deep Clustering With an Unknown Number of Clusters
We have yet to publish a streaming-data solution, but it is definitely a direction we are considering. That said, there is nothing refraining you from keep training the network. However, one should carefully design how you update the input (training) data. There is also this solution that seems interesting: https://github.com/BGU-CS-VIL/dpmmpythonStreaming
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?
DeepDPM - "DeepDPM: Deep Clustering With An Unknown Number of Clusters" [Ronen, Finder, and Freifeld, CVPR 2022]
scikit-learn - scikit-learn: machine learning in Python
DPMMSubClusters_GPU - DPMM Sub Clusters C++ on GPU Cross Platforms (Windows & Linux)
scikit-learn-intelex - Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
Point-Processes - This repository contains the material (datasets, code, videos, spreadsheets) related to my book Stochastic Processes and Simulations - A Machine Learning Perspective.
scikit-cuda - Python interface to GPU-powered libraries
DPMMSubClustersStreaming.jl - Code for our AISTATS '22 paper "Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data"
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
VersatileHDPMixtureModels.jl - Code for our UAI '20 paper "Scalable and Flexible Clustering of Grouped Data via Parallel and Distributed Sampling in Versatile Hierarchical Dirichlet Processes"
cudf - cuDF - GPU DataFrame Library
dpmmpython - Python wrapper for the DPMMSubCluster Julia package for inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)
evojax