CuPy
Original author(s) | Seiya Tokui |
---|---|
Developer(s) | Community, Preferred Networks, Inc. |
Initial release | September 2, 2015; 9 years ago (2015-09-02).[1] |
Stable release | v13.3.0[2] / August 22, 2024; 20 days ago (2024-08-22)[2] |
Repository | github |
Written in | Python, Cython, CUDA |
Operating system | Linux, Windows |
Platform | Cross-platform |
Type | Numerical analysis |
License | MIT |
Website | cupy |
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them.[3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU. CuPy supports Nvidia CUDA GPU platform, and AMD ROCm GPU platform starting in v9.0.[4][5]
CuPy has been initially developed as a backend of Chainer deep learning framework, and later established as an independent project in 2017.[6]
CuPy is a part of the NumPy ecosystem array libraries[7] and is widely adopted to utilize GPU with Python,[8] especially in high-performance computing environments such as Summit,[9] Perlmutter,[10] EULER,[11] and ABCI.[12]
CuPy is a NumFOCUS sponsored project.[13]
Features
CuPy implements NumPy/SciPy-compatible APIs, as well as features to write user-defined GPU kernels or access low-level APIs.[14][15]
NumPy-compatible APIs
The same set of APIs defined in the NumPy package (numpy.*
) are available under cupy.*
package.
- Multi-dimensional array (
cupy.ndarray
) for boolean, integer, float, and complex data types - Module-level functions
- Linear algebra functions
- Fast Fourier transform
- Random number generator
SciPy-compatible APIs
The same set of APIs defined in the SciPy package (scipy.*
) are available under cupyx.scipy.*
package.
- Sparse matrices (
cupyx.scipy.sparse.*_matrix
) of CSR, COO, CSC, and DIA format - Discrete Fourier transform
- Advanced linear algebra
- Multidimensional image processing
- Sparse linear algebra
- Special functions
- Signal processing
- Statistical functions
User-defined GPU kernels
- Kernel templates for element-wise and reduction operations
- Raw kernel (CUDA C/C++)
- Just-in-time transpiler (JIT)
- Kernel fusion
Distributed computing
- Distributed communication package (
cupyx.distributed
), providing collective and peer-to-peer primitives
Low-level CUDA features
- Stream and event
- Memory pool
- Profiler
- Host API binding
- CUDA Python support[16]
Interoperability
- DLPack[17]
- CUDA Array Interface[18]
- NEP 13 (
__array_ufunc__
)[19] - NEP 18 (
__array_function__
)[20][21] - Array API Standard[22][23]
Examples
Array creation
>>> import cupy as cp >>> x = cp.array([1, 2, 3]) >>> x array([1, 2, 3]) >>> y = cp.arange(10) >>> y array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
Basic operations
>>> import cupy as cp >>> x = cp.arange(12).reshape(3, 4).astype(cp.float32) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.]], dtype=float32) >>> x.sum(axis=1) array([ 6., 22., 38.], dtype=float32)
Raw CUDA C/C++ kernel
>>> import cupy as cp >>> kern = cp.RawKernel(r''' ... extern "C" __global__ ... void multiply_elemwise(const float* in1, const float* in2, float* out) { ... int tid = blockDim.x * blockIdx.x + threadIdx.x; ... out[tid] = in1[tid] * in2[tid]; ... } ... ''', 'multiply_elemwise') >>> in1 = cp.arange(16, dtype=cp.float32).reshape(4, 4) >>> in2 = cp.arange(16, dtype=cp.float32).reshape(4, 4) >>> out = cp.zeros((4, 4), dtype=cp.float32) >>> kern((4,), (4,), (in1, in2, out)) # grid, block and arguments >>> out array([[ 0., 1., 4., 9.], [ 16., 25., 36., 49.], [ 64., 81., 100., 121.], [144., 169., 196., 225.]], dtype=float32)
Applications
- spaCy[24][25]
- XGBoost[26]
- turboSETI (Berkeley SETI)[27]
- NVIDIA RAPIDS[28][29][30][31]
- einops[32][33]
- scikit-learn[34]
- MONAI
- Chainer[35]
See also
References
- ^ "Release v1.3.0 – chainer/chainer". Retrieved 25 June 2022 – via GitHub.
- ^ a b "Releases – cupy/cupy". Retrieved 8 September 2024 – via GitHub.
- ^ Okuta, Ryosuke; Unno, Yuya; Nishino, Daisuke; Hido, Shohei; Loomis, Crissman (2017). CuPy: A NumPy-Compatible Library for NVIDIA GPU Calculations (PDF). Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS).
- ^ "CuPy 9.0 Brings AMD GPU Support To This Numpy-Compatible Library - Phoronix". Phoronix. 29 April 2021. Retrieved 21 June 2022.
- ^ "AMD Leads High Performance Computing Towards Exascale and Beyond". 28 June 2021. Retrieved 21 June 2022.
Most recently, CuPy, an open-source array library with Python, has expanded its traditional GPU support with the introduction of version 9.0 that now offers support for the ROCm stack for GPU-accelerated computing.
- ^ "Preferred Networks released Version 2 of Chainer, an Open Source framework for Deep Learning - Preferred Networks, Inc". 2 June 2017. Retrieved 18 June 2022.
- ^ "NumPy". numpy.org. Retrieved 21 June 2022.
- ^ Gorelick, Micha; Ozsvald, Ian (April 2020). High Performance Python: Practical Performant Programming for Humans (2nd ed.). O'Reilly Media, Inc. p. 190. ISBN 9781492055020.
- ^ Oak Ridge Leadership Computing Facility. "Installing CuPy". OLCF User Documentation. Retrieved 21 June 2022.
- ^ National Energy Research Scientific Computing Center. "Using Python on Perlmutter". NERSC Documentation. Retrieved 21 June 2022.
- ^ ETH Zurich. "CuPy". ScientificComputing. Retrieved 21 June 2022.
- ^ National Institute of Advanced Industrial Science and Technology. "Chainer". ABCI 2.0 User Guide. Retrieved 21 June 2022.
- ^ "Sponsored Projects - NumFOCUS". Retrieved 8 September 2024.
- ^ "Overview". CuPy documentation. Retrieved 18 June 2022.
- ^ "Comparison Table". CuPy documentation. Retrieved 18 June 2022.
- ^ "CUDA Python | NVIDIA Developer". Retrieved 21 June 2022.
- ^ "Welcome to DLPack's documentation!". DLPack 0.6.0 documentation. Retrieved 21 June 2022.
- ^ "CUDA Array Interface (Version 3)". Numba 0.55.2+0.g2298ad618.dirty-py3.7-linux-x86_64.egg documentation. Retrieved 21 June 2022.
- ^ "NEP 13 — A mechanism for overriding Ufuncs — NumPy Enhancement Proposals". numpy.org. Retrieved 21 June 2022.
- ^ "NEP 18 — A dispatch mechanism for NumPy's high level array functions — NumPy Enhancement Proposals". numpy.org. Retrieved 21 June 2022.
- ^ Charles R Harris; K. Jarrod Millman; Stéfan J. van der Walt; et al. (16 September 2020). "Array programming with NumPy" (PDF). Nature. 585 (7825): 357–362. arXiv:2006.10256. doi:10.1038/S41586-020-2649-2. ISSN 1476-4687. PMC 7759461. PMID 32939066. Wikidata Q99413970.
- ^ "2021 report - Python Data APIs Consortium" (PDF). Retrieved 21 June 2022.
- ^ "Purpose and scope". Python array API standard 2021.12 documentation. Retrieved 21 June 2022.
- ^ "Install spaCy". spaCy Usage Documentation. Retrieved 21 June 2022.
- ^ Patel, Ankur A.; Arasanipalai, Ajay Uppili (May 2021). Applied Natural Language Processing in the Enterprise (1st ed.). O'Reilly Media, Inc. p. 68. ISBN 9781492062578.
- ^ "Python Package Introduction". xgboost 1.6.1 documentation. Retrieved 21 June 2022.
- ^ "UCBerkeleySETI/turbo_seti: turboSETI -- python based SETI search algorithm". GitHub. Retrieved 21 June 2022.
- ^ "Open GPU Data Science | RAPIDS". Retrieved 21 June 2022.
- ^ "API Docs". RAPIDS Docs. Retrieved 21 June 2022.
- ^ "Efficient Data Sharing between CuPy and RAPIDS". Retrieved 21 June 2022.
- ^ "10 Minutes to cuDF and CuPy". Retrieved 21 June 2022.
- ^ Alex, Rogozhnikov (2022). Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation. International Conference on Learning Representations.
- ^ "arogozhnikov/einops: Deep learning operations reinvented (for pytorch, tensorflow, jax and others)". GitHub. Retrieved 21 June 2022.
- ^ "Array API support (experimental) — scikit-learn documentation". Retrieved 8 September 2024.
- ^ Tokui, Seiya; Okuta, Ryosuke; Akiba, Takuya; Niitani, Yusuke; Ogawa, Toru; Saito, Shunta; Suzuki, Shuji; Uenishi, Kota; Vogel, Brian; Vincent, Hiroyuki Yamazaki (2019). Chainer: A Deep Learning Framework for Accelerating the Research Cycle. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi:10.1145/3292500.3330756.
External links
- Official website
- cupy on GitHub
- v
- t
- e
- NumPy
- SciPy
- matplotlib
- pandas
- scikit-learn
- scikit-image
- MayaVi
- more