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.
CuPy is a tool in the Data Science Tools category of a tech stack.
CuPy is an open source tool with 9.5K GitHub stars and 855 GitHub forks. Here’s a link to CuPy's open source repository on GitHub
Who uses CuPy?
Developers
7 developers on StackShare have stated that they use CuPy.
CuPy's Features
- It's interface is highly compatible with NumPy in most cases it can be used as a drop-in replacement
- Supports various methods, indexing, data types, broadcasting and more
- You can easily make a custom CUDA kernel if you want to make your code run faster, requiring only a small code snippet of C++
- It automatically wraps and compiles it to make a CUDA binary
- Compiled binaries are cached and reused in subsequent runs
CuPy Alternatives & Comparisons
What are some alternatives to CuPy?
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.
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