Basics of CuPy

In this section, you will learn about the following things:

  • Basics of cupy.ndarray
  • The concept of current device
  • host-device and device-device array transfer

Basics of cupy.ndarray

CuPy is a GPU array backend that implements a subset of NumPy interface. In the following code, cp is an abbreviation of cupy, as np is numpy as is customarily done:

>>> import numpy as np
>>> import cupy as cp

The cupy.ndarray class is in its core, which is a compatible GPU alternative of numpy.ndarray.

>>> x_gpu = cp.array([1, 2, 3])

x_gpu in the above example is an instance of cupy.ndarray. You can see its creation of identical to NumPy‘s one, except that numpy is replaced with cupy. The main difference of cupy.ndarray from numpy.ndarray is that the content is allocated on the device memory. Its data is allocated on the current device, which will be explained later.

Most of the array manipulations are also done in the way similar to NumPy. Take the Euclidean norm (a.k.a L2 norm) for example. NumPy has numpy.lina.g.norm to calculate it on CPU.

>>> x_cpu = np.array([1, 2, 3])
>>> l2_cpu = np.linalg.norm(x_cpu)

We can calculate it on GPU with CuPy in a similar way:

>>> x_gpu = cp.array([1, 2, 3])
>>> l2_gpu = cp.linalg.norm(x_gpu)

CuPy implements many functions on cupy.ndarray objects. See the reference for the supported subset of NumPy API. Understanding NumPy might help utilizing most features of CuPy. So, we recommend you to read the NumPy documentation.

Current Device

CuPy has a concept of the current device, which is the default device on which the allocation, manipulation, calculation etc. of arrays are taken place. Suppose the ID of current device is 0. The following code allocates array contents on GPU 0.

>>> x_on_gpu0 = cp.array([1, 2, 3, 4, 5])

The current device can be changed by cupy.cuda.Device.use() as follows:

>>> x_on_gpu0 = cp.array([1, 2, 3, 4, 5])
>>> cp.cuda.Device(1).use()
>>> x_on_gpu1 = cp.array([1, 2, 3, 4, 5])

If you switch the current GPU temporarily, with statement comes in handy.

>>> with cp.cuda.Device(1):
...    x_on_gpu1 = cp.array([1, 2, 3, 4, 5])
>>> x_on_gpu0 = cp.array([1, 2, 3, 4, 5])

Most operations of CuPy is done on the current device. Be careful that if processing of an array on a non-current device will cause an error:

>>> with cp.cuda.Device(0):
...    x_on_gpu0 = cp.array([1, 2, 3, 4, 5])
>>> with cp.cuda.Device(1):
...    x_on_gpu0 * 2  # raises error
Traceback (most recent call last):
ValueError: Array device must be same as the current device: array device = 0 while current = 1

cupy.ndarray.device attribute indicates the device on which the array is allocated.

>>> with cp.cuda.Device(1):
...    x = cp.array([1, 2, 3, 4, 5])
>>> x.device
<CUDA Device 1>


If the environment has only one device, such explicit device switching is not needed.

Data Transfer

Move arrays to a device

cupy.asarray() can be used to move a numpy.ndarray, a list, or any object that can be passed to numpy.array() to the current device:

>>> x_cpu = np.array([1, 2, 3])
>>> x_gpu = cp.asarray(x_cpu)  # move the data to the current device.

cupy.asarray() can accept cupy.ndarray, which means we can transfer the array between devices with this function.

>>> with cp.cuda.Device(0):
...     x_gpu_0 = cp.ndarray([1, 2, 3])  # create an array in GPU 0
>>> with cp.cuda.Device(1):
...     x_gpu_1 = cp.asarray(x_gpu_0)  # move the array to GPU 1


cupy.asarray() does not copy the input array if possible. So, if you put an array of the current device, it returns the input object itself.

If we do copy the array in this situation, you can use cupy.array() with copy=True. Actually cupy.asarray() is equivalent to cupy.array(arr, dtype, copy=False).

Move array from a device to the host

Moving a device array to the host can be done by cupy.asnumpy() as follows:

>>> x_gpu = cp.array([1, 2, 3])  # create an array in the current device
>>> x_cpu = cp.asnumpy(x_gpu)  # move the array to the host.

We can also use cupy.ndarray.get():

>>> x_cpu = x_gpu.get()


If you work with Chainer, you can also use to_cpu() and to_gpu() to move arrays back and forth between a device and a host, or between different devices. Note that to_gpu() has device option to specify the device which arrays are transferred.

How to write CPU/GPU agnostic code

The compatibility of CuPy with NumPy enables us to write CPU/GPU generic code. It can be made easy by the cupy.get_array_module() function. This function returns the numpy or cupy module based on arguments. A CPU/GPU generic function is defined using it like follows:

>>> # Stable implementation of log(1 + exp(x))
>>> def softplus(x):
...     xp = cp.get_array_module(x)
...     return xp.maximum(0, x) + xp.log1p(xp.exp(-abs(x)))