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Cuml Alternatives
Similar projects and alternatives to cuml
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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FLiPStackWeekly
FLaNK AI Weekly covering Apache NiFi, Apache Flink, Apache Kafka, Apache Spark, Apache Iceberg, Apache Ozone, Apache Pulsar, and more...
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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scikit-learn-intelex
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
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DeepDPM
"DeepDPM: Deep Clustering With An Unknown Number of Clusters" [Ronen, Finder, and Freifeld, CVPR 2022]
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raft
RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications. (by rapidsai)
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pdc-dp-means
"Revisiting DP-Means: Fast Scalable Algorithms via Parallelism and Delayed Cluster Creation" [Dinari and Freifeld, UAI 2022]
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better cuml alternative or higher similarity.
cuml reviews and mentions
Posts with mentions or reviews of cuml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-11-13.
- 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.
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A note from our sponsor - WorkOS
workos.com | 26 Apr 2024
Stats
Basic cuml repo stats
10
3,894
9.3
4 days ago
rapidsai/cuml is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of cuml is C++.
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