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cupy | A NumPycompatible array library accelerated by CUDA | GPU library

 by   cupy Python Version: v10.3.1 License: MIT

 by   cupy Python Version: v10.3.1 License: MIT

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kandi X-RAY | cupy Summary

cupy is a Python library typically used in Hardware, GPU, Pytorch, Numpy applications. cupy has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can download it from GitHub.
Website | Install | Tutorial | Examples | Documentation | API Reference | Forum. CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy acts as a drop-in replacement to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms. CuPy also provides access to low-level CUDA features. You can pass ndarray to existing CUDA C/C++ programs via RawKernels, use Streams for performance, or even call CUDA Runtime APIs directly.
Support
Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • cupy has a highly active ecosystem.
  • It has 5918 star(s) with 586 fork(s). There are 121 watchers for this library.
  • There were 7 major release(s) in the last 6 months.
  • There are 333 open issues and 1334 have been closed. On average issues are closed in 108 days. There are 55 open pull requests and 0 closed requests.
  • It has a negative sentiment in the developer community.
  • The latest version of cupy is v10.3.1
cupy Support
Best in #GPU
Average in #GPU
cupy Support
Best in #GPU
Average in #GPU

quality kandi Quality

  • cupy has 0 bugs and 0 code smells.
cupy Quality
Best in #GPU
Average in #GPU
cupy Quality
Best in #GPU
Average in #GPU

securitySecurity

  • cupy has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • cupy code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
cupy Security
Best in #GPU
Average in #GPU
cupy Security
Best in #GPU
Average in #GPU

license License

  • cupy is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
cupy License
Best in #GPU
Average in #GPU
cupy License
Best in #GPU
Average in #GPU

buildReuse

  • cupy releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • It has 84026 lines of code, 7945 functions and 556 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
cupy Reuse
Best in #GPU
Average in #GPU
cupy Reuse
Best in #GPU
Average in #GPU
Top functions reviewed by kandi - BETA

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

  • Linear Grammarization problem .
  • Pad an array with padding .
  • Return a list of module names .
  • Linear linear operator .
  • Calculate the minimum resolver .
  • Compute the Einsum operator .
  • Calculate the gradient of a function .
  • Generate documentation .
  • Get a CUFFT plan for a given shape .
  • Preconfigure modules

cupy Key Features

NumPy & SciPy for GPU

cupy Examples and Code Snippets

  • CuPy : NumPy & SciPy for GPU
  • Run on Docker
  • Reference
  • Python create different functions in a loop
  • Cupy sparse matrix does not correspond to its Scipy equivalence?
  • cupy.var (variance) performance much slower than numpy.var trying to understand why
  • RAM blowing up on computation
  • Cupy indexing in 2D Cuda Grid Kernels?
  • why np.einsum() can run on GPU if the arrays are defined in GPU?
  • FAST: 1D overlaps with rows in 2D?
  • cuPy error : Implicit conversion to a host NumPy array via __array__ is not allowed,
  • How to create unique ID column in DASK_CUDF
  • AttributeError: 'cupy.core.core.ndarray' object has no attribute 'iloc'

CuPy : NumPy & SciPy for GPU

>>> import cupy as cp
>>> x = cp.arange(6).reshape(2, 3).astype('f')
>>> x
array([[ 0.,  1.,  2.],
       [ 3.,  4.,  5.]], dtype=float32)
>>> x.sum(axis=1)
array([  3.,  12.], dtype=float32)

Community Discussions

Trending Discussions on cupy
  • Python create different functions in a loop
  • Cupy sparse matrix does not correspond to its Scipy equivalence?
  • python multiprocessing error along using cupy
  • cupy.var (variance) performance much slower than numpy.var trying to understand why
  • RAM blowing up on computation
  • Linear programming with cupy
  • Large cupy array running out of GPU Ram
  • Is there any way to set number of threads , number of blocks and grids for CuPy computation? How?
  • Cupy indexing in 2D Cuda Grid Kernels?
  • Install cupy on MacOS without GPU support
Trending Discussions on cupy

QUESTION

Python create different functions in a loop

Asked 2022-Apr-11 at 05:54

suppose I need to define functions that when the input is numpy array, it returns the numpy version of the function. And when the input is a cupy array, it returns cupy version of the function.

import numpy as np
import cupy as cp
def tan(arr):
    return cp.tan(arr) if arr.__class__ is cp.ndarray else np.tan(arr)

I also need to create functions such as sin, cos,tanh etc. Is there a way to create those in a loop to avoid typos, or are there better ways? Thanks!

ANSWER

Answered 2022-Apr-11 at 05:54

To insert into the current module 3 functions with a loop:

from functools import partial

for fn in ('tan','sin','cos'):
  globals()[fn] = partial(lambda fname, arr: getattr(cp if isinstance(arr, cp.ndarray) else np, fname)(arr), fn)

How it works.. globals() returns a dict where you have every function & variable in the current module. So we insert lambda functions there for each func you want to define. In the lambda function we use getattr() to get the corresponding function from np or cp module and call it with arr.

The partial() is used to "freeze" the fname parameter.

Not sure if this is the best way but it works..

Source https://stackoverflow.com/questions/71820858

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

Vulnerabilities

No vulnerabilities reported

Install cupy

Wheels (precompiled binary packages) are available for Linux (x86_64) and Windows (amd64). Choose the right package for your platform. (*) ROCm support is an experimental feature. Refer to the docs for details. Use -f https://pip.cupy.dev/pre option to install pre-releases (e.g., pip install cupy-cuda114 -f https://pip.cupy.dev/pre). See the Installation Guide if you are using Conda/Anaconda or building from source.

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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