Demo

We’ll start with a simple example that runs locally.

import cubed
import cubed.array_api as xp
spec = cubed.Spec(work_dir="tmp", allowed_mem="100kB")
a = xp.asarray([[1, 2, 3], [4, 5, 6], [7, 8, 9]], chunks=(2, 2), spec=spec)

Cubed implements the Python Array API standard, which is essentially a subset of NumPy, and is imported as xp by convention.

Notice that we also specify chunks, just like in Dask Array, and a Spec object that describes the resources available to run the computation.

b = xp.asarray([[1, 1, 1], [1, 1, 1], [1, 1, 1]], chunks=(2, 2), spec=spec)
c = xp.add(a, b)

Cubed uses lazy evaluation, so nothing has been computed yet.

c.compute()

This runs the computation using the (default) local Python executor and prints the result (if run interactively):

array([[ 2,  3,  4],
       [ 5,  6,  7],
       [ 8,  9, 10]])

See the examples README for more examples that run on a single multi-core machine, or in the cloud.