cupy | NumPy & SciPy for GPU | GPU library

 by   cupy Python Version: 13.0.0b1 License: MIT

kandi X-RAY | cupy Summary

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 install using 'pip install cupy' or download it from GitHub, PyPI.

NumPy & SciPy for GPU

            kandi-support Support

              cupy has a highly active ecosystem.
              It has 6962 star(s) with 687 fork(s). There are 129 watchers for this library.
              There were 2 major release(s) in the last 6 months.
              There are 435 open issues and 1513 have been closed. On average issues are closed in 98 days. There are 68 open pull requests and 0 closed requests.
              It has a negative sentiment in the developer community.
              The latest version of cupy is 13.0.0b1

            kandi-Quality Quality

              cupy has 0 bugs and 0 code smells.

            kandi-Security 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.

            kandi-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.

            kandi-Reuse Reuse

              cupy 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.
              It has 84026 lines of code, 7945 functions and 556 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            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.
            • Generate documentation
            • Generate the rst text for a csv file
            • Get a set of functions
            • Return the section of the css section
            • Compute the CSR product of a and b
            • Check if x is a csr matrix
            • R Check if x is a csc_matrix
            • Return a dictionary of the relevant features
            • Create a Feature from a dictionary
            • Compute the Syrk decomposition of a matrix
            • Computes spgemm
            • Compute the geam
            • Applies an affine transformation to a 2D array
            • Install CUDA library
            • Returns the name of the compiler
            • Evaluate a function
            • Solve the product of two arrays
            • Matrix - matrix product
            • Return the name of the numpy array
            • Generate dockerfile
            • Qr decomposition
            • Normalize x
            • Internal function to generate a ND kernel
            • Generate a histogram
            • Compute a greedy path
            • Compare two CSR matrices
            Get all kandi verified functions for this library.

            cupy Key Features

            No Key Features are available at this moment for cupy.

            cupy Examples and Code Snippets

            SmallPebble,Training a convolutional neural network on CIFAR-10, using CuPy
            Pythondot img1Lines of Code : 121dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            "Load the CIFAR dataset."
            X_train, y_train, _, _ = load_data('cifar')  # load/download from
            X_train = X_train/255  # normalize
            """Plot, to check it's the right data.
            (This cell's code is from:  
            Brief guide to using SmallPebble,Switching between NumPy and CuPy
            Pythondot img2Lines of Code : 13dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            import cupy
            import numpy
            import smallpebble as sp
            # Switch to CuPy
            print(sp.array_library.library.__name__)  # should be 'cupy'
            # Switch back to NumPy:
            print(sp.array_library.library.__name__)  # should be 'numpy'

            Community Discussions


            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.



            Answered 2022-Apr-11 at 05:54

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



            Cupy sparse matrix does not correspond to its Scipy equivalence?
            Asked 2022-Apr-02 at 08:13

            I digged the documentation for cupy sparse matrix.

            as in scipy I expect to have something like this:



            Answered 2022-Apr-02 at 08:13

            as stated in the error, you need to convert the datatype to either bool, float32/64, or complex64/128:



            python multiprocessing error along using cupy
            Asked 2022-Mar-17 at 11:43

            Consider simplified example using multiprocessing inside a class that use cupy for simulation. this part



            Answered 2022-Mar-17 at 11:43

            Adding an answer here to wrap this one up. Didn't stumble upon a Stack Overflow thread when researching this issue so I'm assuming this thread will get more views in the future.

            The issue has to do with the default start method not working with CUDA Multiprocessing. By explicitly setting the start method to spawn with multiprocessing.set_start_method('spawn', force=True) this issue is resolved.



            cupy.var (variance) performance much slower than numpy.var trying to understand why
            Asked 2022-Jan-15 at 07:10

            I am hoping to move my custom camera video pipeline to use video memory with a combination of numba and cupy and avoid passing data back to the host memory if at all possible. As part of doing this I need to port my sharpness detection routine to use cuda. The easiest way to do this seemed to be to use cupy as essential all I do is compute the variance of a laplace transform of each image. The trouble I am hitting is the cupy variance computation appears to be ~ 8x slower than numpy. This includes the time it takes to copy the device ndarray to the host and perform the variance computation on the cpu using numpy. I am hoping to gain a better understanding of why the variance computation ReductionKernel employed by cupy on the GPU is so much slower. I'll start by including the test I ran below.



            Answered 2022-Jan-14 at 21:58

            I have a partial hypothesis about the problem (not a full explanation) and a work-around. Perhaps someone can fill in the gaps. I've used a quicker-and-dirtier benchmark, for brevity's sake.

            The work-around: reduce one axis at a time

            Cupy is much faster when reduction is performed on one axis at a time. In stead of:


            prefer this:


            Note that the results of these computations may differ due to rounding error.

            Here are faster mean and var functions:



            RAM blowing up on computation
            Asked 2022-Jan-13 at 10:56

            Below is a runnable code snippet using dask and cupy, which I have problems with. I run this on Google Colab with GPU activated.

            Basically my problem is, that A and At are arrays which are too big for RAM, thats why I use Dask. On these too big for RAM arrays, I run operations, but I would like to obtain AtW1[:,k] (as a cupy array) without blowing my RAM or GPU Memory, because i need this value for further operations. How can I achieve this?



            Answered 2022-Jan-12 at 11:31

            Although the idea of rechunking makes a lot of sense on paper, in practice rechunking needs to be done with great care, since it will only be able to reshape the work that can be blocked in principle.

            For example, compare the following two approaches:



            Linear programming with cupy
            Asked 2021-Dec-08 at 12:49

            I am trying to improve codes efficiency with cupy. But I find no ways to carry linear programming within cupy. This problem comes from the following parts:



            Answered 2021-Dec-08 at 12:49

            I’ve seen papers that propose to use GPU for linear programming. Some of them even reported outstanding improvement. But from what I saw, they compare their GPU implementation of the simplex method with their sequential implementation, not with Gurobi, Cplex, or even CLP. And I never heard about an efficient GPU-base LP solver that beats good LP solvers. Such flagman like Gurobi does not support GPU. And I know there are some doubts that GPU actually can help in large-scale LP.

            • Large-scale LPs are sparse, and GPU is not good for sparse.
            • Optimization in general is mostly a sequential process (paralleling in modern LP solvers is very specific and cannot utilize GPU).

            If you want to try to implement your own GPU-base LP solver, I encourage you to try. Whatever you get it would be a great experience.

            But if you only need to speed up your solution process then get a different solver. Linprog from SciPy may be a good choice to prototype. But GLPK or CLP/CBC will give you much better speed. You can invoke them through Pyomo or PULP.



            Large cupy array running out of GPU Ram
            Asked 2021-Nov-05 at 18:57

            This is a total newbie question but I've been searching for a couple days and cannot find the answer.

            I am using cupy to allocate a large array of doubles (circa 655k rows x 4k columns ) which is about 16Gb in ram. I'm running on p2.8xlarge (the aws instance that claims to have 96GB of GPU ram and 8 GPUs), but when I allocate the array it gives me out of memory error.

            Is this happening becaues the 96GB of ram is split into 8x12 GB lots that are only accessible to each GPU? Is there no concept of pooling the GPU ram across the GPUs (like regular ram in multiple CPU situation) ?



            Answered 2021-Nov-05 at 18:57

            From playing around with it a fair bit, I think the answer is no, you cannot pool memory across GPUs. You can move data back and forth between GPUs and CPU but there's no concept of unified GPU ram accessible to all GPUs



            Is there any way to set number of threads , number of blocks and grids for CuPy computation? How?
            Asked 2021-Oct-25 at 14:58

            I am using Cupy with following code,



            Answered 2021-Oct-25 at 13:56

            For high-level, NumPy-like APIs, there is currently no public interface to change the grid/block configuration. In addition, many linalg APIs (such as eigh in your example) delegate the job to the CUDA Math Libraries solvers, which do not allow users to set grid/block configuration either. I wonder what prompts to this need. It'd be nice if you could elaborate.



            Cupy indexing in 2D Cuda Grid Kernels?
            Asked 2021-Oct-19 at 18:18

            I'm trying to start using Cupy for some Cuda Programming. I need to write my own kernels. However, I'm struggling with 2D kernels. It seems that Cupy does not work the way I expected. Here is a very simple example of a 2D kernel in Numba Cuda:



            Answered 2021-Oct-19 at 18:18

            Memory in C is stored in a row-major-order. So, we need to index following this order. Also, since I'm passing int arrays, I changed the argument types of my kernel. Here is the code:



            Install cupy on MacOS without GPU support
            Asked 2021-Oct-19 at 13:50

            I've been making the rounds on forums trying out different ways to install cupy on MacOS running on a device without a Nvidia GPU. So far, nothing has worked. I've tried both a Homebrew install of Python 3.7 and a conda install of Python 3.7 and attempted each of the following:

            • conda install -c conda-forge cupy
            • conda install cupy
            • pip install cupy
            • ...


            Answered 2021-Oct-19 at 13:50

            There is no Mac support in CuPy since NVIDIA no longer supports MacOS. Whatever you read is outdated. I know because I sent a PR to remove the last broken bits from CuPy's codebase, and I also maintain the CuPy package on conda-forge.


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


            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 option to install pre-releases (e.g., pip install cupy-cuda114 -f See the Installation Guide if you are using Conda/Anaconda or building from source.


            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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            pip install cupy

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            gh repo clone cupy/cupy

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