CuPy
v2.5.0
  • Overview
  • Tutorial
  • Reference Manual
    • Indices and tables
    • Reference
      • Multi-Dimensional Array (ndarray)
      • Universal Functions (ufunc)
      • Routines
      • Sparse matrix
      • NumPy-CuPy Generic Code Support
      • Low-Level CUDA Support
      • Kernel binary memoization
      • Custom kernels
      • Testing Modules
      • Profiling
      • Environment variables
      • Difference between CuPy and NumPy

Development

  • API Compatibility Policy
  • Contribution Guide

Misc Notes

  • Installation Guide
  • Upgrade Guide
  • License
CuPy
  • Docs »
  • Reference Manual
  • Edit on GitHub

Reference Manual¶

This is the official reference of CuPy, a multi-dimensional array on CUDA with a subset of NumPy interface.

Indices and tables¶

  • Index
  • Module Index

Reference¶

  • Multi-Dimensional Array (ndarray)
    • cupy.ndarray
    • Code compatibility features
    • Conversion to/from NumPy arrays
  • Universal Functions (ufunc)
    • Ufunc class
    • Available ufuncs
    • ufunc.at
  • Routines
    • Array Creation Routines
    • Array Manipulation Routines
    • Repeating part of arrays along axis
    • Rearranging elements
    • Binary Operations
    • Indexing Routines
    • Input and Output
    • Linear Algebra
    • Logic Functions
    • Mathematical Functions
    • Padding
    • Random Sampling (cupy.random)
    • Sorting, Searching, and Counting
    • Statistics
    • External Functions
  • Sparse matrix
    • Sparse matrix classes
    • Functions
  • NumPy-CuPy Generic Code Support
    • cupy.get_array_module
  • Low-Level CUDA Support
    • Device management
    • Memory management
    • Memory hook
    • Streams and events
    • Profiler
  • Kernel binary memoization
    • cupy.memoize
    • cupy.clear_memo
  • Custom kernels
    • cupy.ElementwiseKernel
    • cupy.ReductionKernel
  • Testing Modules
    • Standard Assertions
    • NumPy-CuPy Consistency Check
    • Parameterized dtype Test
    • Parameterized order Test
  • Profiling
    • time range
  • Environment variables
    • For install
  • Difference between CuPy and NumPy
    • Cast behavior from float to integer
    • Random methods support dtype argument
    • Out-of-bounds indices
    • Duplicate values in indices
    • Reduction methods return zero-dimensional array
    • Data types
    • Array creation from Python objects
    • Universal Functions only work with CuPy array or scalar
Next Previous

© Copyright 2015, Preferred Networks, inc. and Preferred Infrastructure, inc.. Revision 2274875a.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: v2.5.0
Versions
latest
stable
v4.0.0b3
v4.0.0b2
v4.0.0b1
v3.0.0a1
v2.5.0
v2.4.0
v2.3.0
v2.2.0
v2.1.0.1
v2
v1.0.3
v1.0.2
v1.0.1
v1.0.0
Downloads
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.