scikit-cuda VS cusim

Compare scikit-cuda vs cusim and see what are their differences.

cusim

Superfast CUDA implementation of Word2Vec and Latent Dirichlet Allocation (LDA) (by js1010)
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scikit-cuda cusim
1 1
968 40
- -
2.5 0.0
7 months ago about 3 years ago
Python Python
GNU General Public License v3.0 or later Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

scikit-cuda

Posts with mentions or reviews of scikit-cuda. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-22.

cusim

Posts with mentions or reviews of cusim. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing scikit-cuda and cusim you can also consider the following projects:

cupy - NumPy & SciPy for GPU

gensim - Topic Modelling for Humans

cuml - cuML - RAPIDS Machine Learning Library

pygraphistry - PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer

PyCUDA - CUDA integration for Python, plus shiny features

arrayfire-python - Python bindings for ArrayFire: A general purpose GPU library.

pyopencl - OpenCL integration for Python, plus shiny features

kernel_tuner - Kernel Tuner

tmu - Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.

jittor - Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.