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
kandi X-RAY | cupy Summary
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
Quality
- cupy has 0 bugs and 0 code smells.
Security
- 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.
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.
Reuse
- 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.
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
>>> 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
QUESTION
Python create different functions in a loop
Asked 2022-Apr-11 at 05:54suppose 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 05:54To 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..
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install cupy
Support
DOWNLOAD this Library from
Reuse Trending Solutions
Reuse Solution Kits and Libraries Curated by Popular Use Cases
Save this library and start creating your kit
Reuse Solution Kits and Libraries Curated by Popular Use Cases
Save this library and start creating your kit