pyopencl
scikit-cuda
| pyopencl | scikit-cuda | |
|---|---|---|
| 2 | 1 | |
| 1,029 | 970 | |
| - | - | |
| 8.1 | 2.5 | |
| 6 days ago | 7 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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.
pyopencl
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An example for OpenCL 3.0?
Please note that OpenCL consists of two parts: host API and a separate language which is used to write kernels (code which is going to be offloaded to devices). OpenCL specification describes host APIs as C-style APIs and that is what implementors has to provide. However, there are number of various libraries which provides bindings for other languages: - C++ - Python - Go - Rust
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Doubts on pyopencl
I thought the project could be dead, but then I looked into the latest commits to the repository, and it is certainly not dead as a project.
scikit-cuda
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GPU Based Kernel-PCA
I found this lovely repo -> https://github.com/lebedov/scikit-cuda
What are some alternatives?
PyCUDA - CUDA integration for Python, plus shiny features
cupy - NumPy & SciPy for GPU
python-performance - Repository for the book Fast Python - published by Manning
cuml - cuML - RAPIDS Machine Learning Library
arrayfire-python - Python bindings for ArrayFire: A general purpose GPU library.
inventory-hunter - ⚡️ Get notified as soon as your next CPU, GPU, or game console is in stock
kernel_tuner - Kernel Tuner
plotoptix - Data visualisation and ray tracing in Python based on OptiX 7.7 framework.
cusim - Superfast CUDA implementation of Word2Vec and Latent Dirichlet Allocation (LDA)
LSQR-CUDA - This is a LSQR-CUDA implementation written by Lawrence Ayers under the supervision of Stefan Guthe of the GRIS institute at the Technische Universität Darmstadt. The LSQR library was authored Chris Paige and Michael Saunders.
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.