dpmmpythonStreaming
cudf
dpmmpythonStreaming | cudf | |
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3 | 23 | |
14 | 7,291 | |
- | 1.8% | |
0.0 | 9.9 | |
over 1 year ago | 3 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
cudf
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A Polars exploration into Kedro
The interesting thing about Polars is that it does not try to be a drop-in replacement to pandas, like Dask, cuDF, or Modin, and instead has its own expressive API. Despite being a young project, it quickly got popular thanks to its easy installation process and its “lightning fast” performance.
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Why we dropped Docker for Python environments
Perhaps the largest for package size is the NVIDIA developed rapids toolkit https://rapids.ai/ . Even still adding things like pandas and some geospatial tools, you rapidly end up with an image well over a gigabyte, despite following cutting edge best practice with docker and python.
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Introducing TeaScript C++ Library
Yes sure, that is how OpenMP does; but on the other side: you seem to already do some basic type inference, and building an AST, no? Then you know as well the size and type of your vectors, and can execute actions in parallel if there is enough data to be worth parallelizing. Is there anyone who don't want their code to execute faster if it is possible? Those that do work in big data domain do use threads and vectorized instructions without user having to type in any directive; just import different library. Example, numpy or numpy with cuda backend, or similar GPU accelerated libraries like cudf.
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[D] Can we use Ray for distributed training on vertex ai ? Can someone provide me examples for the same ? Also which dataframe libraries you guys used for training machine learning models on huge datasets (100 gb+) (because pandas can't handle huge data).
Not the answer about Ray: you could use rapids.ai. I'm using it for for dataframe manipulation on GPU
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Story of my life
To put Data Analytics on GPU Steroids, Try RAPIDS cudf https://rapids.ai/
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Artificial Intelligence in Python
You can scope out https://rapids.ai/. Nvidia's AI toolkits. They have some handy notebooks to poke at to get you started.
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[D] [R] Large-scale clustering
try https://rapids.ai/
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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.).
- Integrating multiple point clouds?
- Buka | Sains Data GPU RAPIDS
What are some alternatives?
DeepDPM - "DeepDPM: Deep Clustering With An Unknown Number of Clusters" [Ronen, Finder, and Freifeld, CVPR 2022]
Numba - NumPy aware dynamic Python compiler using LLVM
DPMMSubClusters_GPU - DPMM Sub Clusters C++ on GPU Cross Platforms (Windows & Linux)
chia-plotter
Point-Processes - This repository contains the material (datasets, code, videos, spreadsheets) related to my book Stochastic Processes and Simulations - A Machine Learning Perspective.
wif500 - Try to find the WIF key and get a donation 200 btc
cuml - cuML - RAPIDS Machine Learning Library
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
DPMMSubClustersStreaming.jl - Code for our AISTATS '22 paper "Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data"
rmm - RAPIDS Memory Manager
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"
CUDA.jl - CUDA programming in Julia.