scikit-cuda | Python interface to GPU-powered libraries | GPU library

by lebedov Python Version: 0.5.3 License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | scikit-cuda Summary

scikit-cuda is a Python library typically used in Hardware, GPU, Deep Learning, Numpy applications. scikit-cuda has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However scikit-cuda has a Non-SPDX License. You can install using 'pip install scikit-cuda' or download it from GitHub, PyPI.

Python interface to GPU-powered libraries

Support

Quality

Security

License

Reuse

Support

scikit-cuda has a medium active ecosystem.

It has 938 star(s) with 175 fork(s). There are 48 watchers for this library.

It had no major release in the last 12 months.

There are 51 open issues and 170 have been closed. On average issues are closed in 89 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of scikit-cuda is 0.5.3

Quality

scikit-cuda has 0 bugs and 0 code smells.

Security

scikit-cuda has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

scikit-cuda code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

scikit-cuda has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

scikit-cuda releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

It has 16434 lines of code, 1169 functions and 57 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed scikit-cuda and discovered the below as its top functions. This is intended to give you an instant insight into scikit-cuda implemented functionality, and help decide if they suit your requirements.

Wrapper for CUDA
Compute the eig value of a GPU
Vander for vander
Free libcula buffers
Wrapper function for the DVM
Compute the RDD of a GPU
R Solve a GPU
Permitian Hermitian decomposition
Transpose the matrix
Fit the PCA model
Test for symmetric test
Solve scipy
Inverse of the inverse function
Resolve the CUDA device
Perform triu on a matrix
Pin a tensorflow matrix
Calculate the diagonal of the matrix
Evaluate the GPU
Compute the eigenvalues of a matrix
Cholesky decomposition
Multiply two tensors
Multipline x and y z
Integrate tensorflow tensor
Computes the dot product of multiple GPUs
Compute fft of a given plan
Complex conjugate matrix

Get all kandi verified functions for this library.

scikit-cuda Key Features

No Key Features are available at this moment for scikit-cuda.

scikit-cuda Examples and Code Snippets

No Code Snippets are available at this moment for scikit-cuda.

Community Discussions

Trending Discussions on scikit-cuda

Colab Failed (How) to import files from git

QUESTION

Colab Failed (How) to import files from git

Asked 2020-Aug-29 at 16:15

I am new to using Colab and cannot find anything to make it work. Could anybody help me fix it or share a solution?

...

ANSWER

Answered 2020-Aug-29 at 08:14

There is subfolder named 'eucl_dist' under 'eucl_dist' again.

So, you have to access './eucl_dist/eucl_dist/gpu_dist'

Try this one.

Source https://stackoverflow.com/questions/63642350

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install scikit-cuda

You can install using 'pip install scikit-cuda' or download it from GitHub, PyPI.
You can use scikit-cuda like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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