Convert an object to a JAX array.
JAX implementation of numpy.array().
object (Any) – an object that is convertible to an array. This includes JAX arrays, NumPy arrays, Python scalars, Python collections like lists and tuples, objects with an __array__ method, and objects supporting the Python buffer protocol.
dtype (DTypeLike | None | None) – optionally specify the dtype of the output array. If not specified it will be inferred from the input.
copy (bool) – specify whether to force a copy of the input. Default: True.
order (str | None) – not implemented in JAX
ndmin (int) – integer specifying the minimum number of dimensions in the output array.
device (xc.Device | Sharding | None | None) – optional Device or Sharding to which the created array will be committed.
A JAX array constructed from the input.
Examples
Constructing JAX arrays from Python scalars:
>>> jnp.array(True) Array(True, dtype=bool) >>> jnp.array(42) Array(42, dtype=int32, weak_type=True) >>> jnp.array(3.5) Array(3.5, dtype=float32, weak_type=True) >>> jnp.array(1 + 1j) Array(1.+1.j, dtype=complex64, weak_type=True)
Constructing JAX arrays from Python collections:
>>> jnp.array([1, 2, 3]) # list of ints -> 1D array
Array([1, 2, 3], dtype=int32)
>>> jnp.array([(1, 2, 3), (4, 5, 6)]) # list of tuples of ints -> 2D array
Array([[1, 2, 3],
[4, 5, 6]], dtype=int32)
>>> jnp.array(range(5))
Array([0, 1, 2, 3, 4], dtype=int32)
Constructing JAX arrays from NumPy arrays:
>>> jnp.array(np.linspace(0, 2, 5)) Array([0. , 0.5, 1. , 1.5, 2. ], dtype=float32)
Constructing a JAX array via the Python buffer interface, using Python’s built-in array module.
>>> from array import array
>>> pybuffer = array('i', [2, 3, 5, 7])
>>> jnp.array(pybuffer)
Array([2, 3, 5, 7], dtype=int32)
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