gputools | GPU accelerated image/volume processing in Python | GPU library

by maweigert Python Version: v0.2.2 License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | gputools Summary

gputools is a Python library typically used in Hardware, GPU, Deep Learning applications. gputools has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install gputools' or download it from GitHub, PyPI.

This package aims to provide GPU accelerated implementations of common volume processing algorithms to the python ecosystem, such as. via OpenCL and the excellent pyopencl bindings.

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Quality

Security

License

Reuse

Support

gputools has a low active ecosystem.

It has 76 star(s) with 17 fork(s). There are 9 watchers for this library.

It had no major release in the last 12 months.

There are 7 open issues and 5 have been closed. On average issues are closed in 130 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of gputools is v0.2.2

Quality

gputools has 0 bugs and 0 code smells.

Security

gputools has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

gputools code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gputools is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

gputools releases are available to install and integrate.

Deployable package is available in PyPI.

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

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Wrap OCLImage class method
Return the absolute path to the given path
Check if dtype is supported
Run a kernel
Convolve a 2D image
Get value from config file
Compute the fft of an array
Convolve a 3D image
Gaussian filter
Convolve a 2d array
Rotate a 3d array
Apply affine transformation to a 3d array
Integral integral function
Convolve the given image and h
Calculate perlin2d
Run NLM3
Wrap an OCL array
SSimulate the image
R Generate geometric transform
Compute similarity between two images
Scale image
Compute TV2 image using OpenCLF2
Fftshift an array or ndarray
Get a reduction kernel
Apply uniform filter
Compute the fft of a NumPy array
Calculate perlin 3 perlin3 volume
Apply affine to a 3d array

Get all kandi verified functions for this library.

gputools Key Features

No Key Features are available at this moment for gputools.

gputools Examples and Code Snippets

No Code Snippets are available at this moment for gputools.

Community Discussions

Trending Discussions on gputools

foreach doparallel on GPU

Calling CUDA API functions from Rcpp package causes segfault

Crash ERROR_CGDataProvider_BufferIsNotReadable + 12, CGDataProviderRetainBytePtr + 216

How to build Tensorflow 1.4 with CUDNN 5.0?

QUESTION

foreach doparallel on GPU

Asked 2018-Jun-21 at 08:21

I have this code for writing my results in parallel. I am using foreach and doParallel libraries in R.

...

ANSWER

Answered 2018-Jun-21 at 08:21

Parallelization with foreach or similar tools works because you have multiple CPUs (or a CPU with multiple cores), which can process multiple tasks at once. A GPU also has multiple cores, but these are already used to process a single task in parallel. So if you want to parallelize further, you will need multiple GPUs.

However, keep in mind that GPUs are faster than CPUs only for certain types of applications. Matrix operations with large matrices being a prime example! See the performance section here for a recent comparison of one particular example. So it might make sense for you to consider if the GPU is the right tool for you.

In addition: File IO will always go via the CPU.

Source https://stackoverflow.com/questions/50961484

QUESTION

Calling CUDA API functions from Rcpp package causes segfault

Asked 2018-May-04 at 13:17

I am currently trying to build an R-Package which works with CUDA. While the traditional method of creating the package would work, much like the gputools package, I wanted to try Rcpp for the package as it seems more clean and convenient concerning return values.

The package installation works well so far, but the issue is that the first call of a CUDA API function (like cudaMalloc() for example) crashes my RStudio.

I created a minimal example to illustrate my case.

It is as simple as

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ANSWER

Answered 2018-May-04 at 13:17

Thank you @RalfStubner, the mistake generating the error above was indeed just the declaration of a return type that was never returned.

So instead of

Source https://stackoverflow.com/questions/49856810

QUESTION

Crash ERROR_CGDataProvider_BufferIsNotReadable + 12, CGDataProviderRetainBytePtr + 216

Asked 2018-Mar-15 at 06:23

I have a crash once in every 1000 to 2000 sessions in our app and I can't figure out what to do about it.

Is that an Apple iOS crash where I cant do anything about it?

I am using SDWebImage pod in my project, maybe it is that?

here is the crash log:

...

ANSWER

Answered 2018-Jan-31 at 19:29

@chris-garrett pointed me in the right direction and I was able to greatly mitigate the issue by fixing memory leaks.

Users who are now getting this error are people who spend at least 30 minutes on the app, which is pretty uncommon. Also, the fact that it affects mostly big screens like iPhone 7/6s/8 Plus, iPhone X, where more memory is needed to render images would corroborate that theory

What you should do to solve this, is to check the memory while debugging. If it increases gradually when repeating the same actions, there's certainly something for you to fix.

There are many ways to find leaks in your app, I'm not a big fan of Instruments, but you can find many tutorials online if needed. Another option is to use memory graph. I wrote previously an article on how to use memory graph here.

Source https://stackoverflow.com/questions/48217875

QUESTION

How to build Tensorflow 1.4 with CUDNN 5.0?

Asked 2017-Nov-09 at 17:04

I'm trying to install Tensorflow 1.4 from sources with CUDA 8.0 and CUDNN 5.0.5, on Centos 7. It's indicated in the documentation that it should work with CUDNN 3 and higher. I'm working in a virtual env with Python 3.4.5, using Bazel 0.7.0, with GCC 4.9. During the configuration, I've set CUDNN version to 5.0.5 and the library has been found.

Unfortunately, it doesn't work and ends up with an error that seems to indicate that CUDNN v6 is needed (I may be wrong on the cause of the error).

Here is the command I'm using:

...

ANSWER

Answered 2017-Nov-09 at 06:48

Tensorflow r1.4 release notes suggest using cuDNN6. You can find all release info here.

All our prebuilt binaries have been built with CUDA 8 and cuDNN 6. We anticipate releasing TensorFlow 1.5 with CUDA 9 and cuDNN 7.

Prior to r1.4 cuDNN 5 works fine.

Source https://stackoverflow.com/questions/47188012

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

Vulnerabilities

No vulnerabilities reported

Install gputools

Or the developmental version:.
Download the correct pyopencl wheel for your platform
Install it via pip install pyopencl‑2020.2.2+cl21‑cp38‑cp38‑win_amd64.whl