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CuPy vs SciPy

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CuPy
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SciPy
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SciPy vs CuPy: What are the differences?

What is SciPy? Scientific Computing Tools for Python. Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

What is CuPy? A NumPy-compatible matrix library accelerated by CUDA. It is an open-source matrix library accelerated with NVIDIA CUDA. CuPy provides GPU accelerated computing with Python. It uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture.

SciPy and CuPy can be categorized as "Data Science" tools.

SciPy and CuPy are both open source tools. It seems that SciPy with 7.12K GitHub stars and 3.25K forks on GitHub has more adoption than CuPy with 4.14K GitHub stars and 373 GitHub forks.

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What is CuPy?

It is an open-source matrix library accelerated with NVIDIA CUDA. CuPy provides GPU accelerated computing with Python. It uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture.

What is SciPy?

Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

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What companies use CuPy?
What companies use SciPy?
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    What tools integrate with CuPy?
    What tools integrate with SciPy?
    What are some alternatives to CuPy and SciPy?
    NumPy
    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
    Numba
    It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.
    PyTorch
    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
    CUDA
    A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
    TensorFlow
    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
    See all alternatives

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