CML_AMP_Intelligent-QA-Chatbot-with-NiFi-Pinecone-and-Llama2
cuml
| CML_AMP_Intelligent-QA-Chatbot-with-NiFi-Pinecone-and-Llama2 | cuml | |
|---|---|---|
| 3 | 10 | |
| 8 | 3,971 | |
| - | 1.7% | |
| 8.4 | 9.3 | |
| about 1 month ago | 6 days ago | |
| Python | C++ | |
| Apache License 2.0 | Apache License 2.0 |
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CML_AMP_Intelligent-QA-Chatbot-with-NiFi-Pinecone-and-Llama2
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?
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.
scikit-learn - scikit-learn: machine learning in Python
FLaNK-Halifax - Community over Code, Apache NiFi, Apache Kafka, Apache Flink, Python, GTFS, Transit, Open Source, Open Data
scikit-learn-intelex - Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
pgmq - A lightweight message queue. Like AWS SQS and RSMQ but on Postgres.
scikit-cuda - Python interface to GPU-powered libraries
CoC2023 - Community over Code, Apache NiFi, Apache Kafka, Apache Flink, Python, GTFS, Transit, Open Source, Open Data
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
nifi - Apache NiFi
cudf - cuDF - GPU DataFrame Library
efficient-kan - An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
evojax