CUDA and Backend Utilities¶

Utilities across backends¶

`chainer.backend.copyto`	Copies the elements of an ndarray to those of another one.
`chainer.backend.get_array_module`	Gets an appropriate one from `numpy`, `cupy`, or `chainerx`.

CUDA¶

Device, context and memory management on CuPy.

Note

The package chainer.cuda has been renamed to chainer.backends.cuda as of v4.0.0, but the previous module path chainer.cuda is also available.

Chainer uses CuPy (with very thin wrapper) to exploit the speed of GPU computation. Following modules and classes defined in CuPy are imported to chainer.backends.cuda module for convenience (refer to this table when reading chainer’s source codes).

imported name	original name
`chainer.backends.cuda.cupy`	`cupy`
`chainer.backends.cuda.cupyx`	`cupyx`
`chainer.backends.cuda.ndarray`	`cupy.ndarray`
`chainer.backends.cuda.cupy.cuda`	`cupy.cuda`
`chainer.backends.cuda.Device`	`cupy.cuda.Device`
`chainer.backends.cuda.Event`	`cupy.cuda.Event`
`chainer.backends.cuda.Stream`	`cupy.cuda.Stream`

Chainer replaces the default allocator of CuPy by its memory pool implementation. It enables us to reuse the device memory over multiple forward/backward computations, and temporary arrays for consecutive elementwise operations.

Devices¶

`chainer.backends.cuda.get_device`	Gets the device from a device object, an ID integer or an array object.
`chainer.backends.cuda.get_device_from_id`	Gets the device from an ID integer.
`chainer.backends.cuda.get_device_from_array`	Gets the device from a list of CuPy array or a single CuPy array.

CuPy array allocation and copy¶

`chainer.backends.cuda.copy`	Copies a `cupy.ndarray` object using the default stream.
`chainer.backends.cuda.to_cpu`	Copies the given GPU array to host CPU.
`chainer.backends.cuda.to_gpu`	Copies the given CPU array to the specified device.

Kernel definition utilities¶

`chainer.backends.cuda.memoize`	Makes a function memoizing the result for each argument and device.
`chainer.backends.cuda.clear_memo`	Clears the memoized results for all functions decorated by memoize.
`chainer.backends.cuda.elementwise`	Creates an elementwise kernel function.
`chainer.backends.cuda.raw`	Creates a raw kernel function.
`chainer.backends.cuda.reduce`	Creates a global reduction kernel function.

CPU/GPU generic code support¶

chainer.backends.cuda.get_array_module

Gets an appropriate one from numpy or cupy.

cuDNN support¶

`chainer.backends.cuda.set_max_workspace_size`	Sets the workspace size for cuDNN.
`chainer.backends.cuda.get_max_workspace_size`	Gets the workspace size for cuDNN.

iDeep¶

iDeep is a module that provides NumPy-like API and DNN acceleration using MKL-DNN for Intel CPUs. See Tips and FAQs and Performance Best Practices for details.

chainer.backends.intel64.is_ideep_available

Returns if iDeep is available.