ak.to_backend#

Defined in awkward.operations.ak_to_backend on line 16.

ak.to_backend(array, backend, *, highlevel=True, behavior=None, attrs=None)#
Parameters:

  • array – Array-like data (anything ak.to_layout recognizes).

  • backend ("cpu", "cuda", "jax", or "typetracer") – If "cpu", the array structure is recursively copied (if need be) to main memory for use with the default Numpy backend; if "cuda", the structure is copied to the GPU(s) for use with CuPy. If "jax", the structure is copied to the CPU for use with JAX.

  • highlevel (bool) – If True, return an ak.Array; otherwise, return a low-level ak.contents.Content subclass.

  • behavior (None or dict) – Custom ak.behavior for the output array, if high-level.

  • attrs (None or dict) – Custom attributes for the output array, if high-level.

Converts an array from "cpu", "cuda", "jax" kernels to "cpu", "cuda", "jax", or "typetracer" .

Any components that are already in the desired backend are viewed, rather than copied, so this operation can be an inexpensive way to ensure that an array is ready for a particular library.

To use "cuda", the cupy package must be installed, either with:

pip install cupy

or:

conda install -c conda-forge cupy

To use "jax", the jax package must be installed, either with:

pip install jax

or:

conda install -c conda-forge jax

See ak.kernels.