mars.tensor.random.randint(low, high=None, size=None, dtype='l', density=None, chunk_size=None, gpu=None)[source]

Return random integers from low (inclusive) to high (exclusive).

Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then results are from [0, low).

  • low (int) – Lowest (signed) integer to be drawn from the distribution (unless high=None, in which case this parameter is one above the highest such integer).

  • high (int, optional) – If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if high=None).

  • size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.

  • dtype (data-type, optional) – Desired dtype of the result. All dtypes are determined by their name, i.e., ‘int64’, ‘int’, etc, so byteorder is not available and a specific precision may have different C types depending on the platform. The default value is ‘’.

  • density (float, optional) – if density specified, a sparse tensor will be created

  • chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension

  • gpu (bool, optional) – Allocate the tensor on GPU if True, False as default

  • dtype – Data-type of the returned tensor.


outsize-shaped tensor of random integers from the appropriate distribution, or a single such random int if size not provided.

Return type

int or Tensor of ints

See also


similar to randint, only for the closed interval [low, high], and 1 is the lowest value if high is omitted. In particular, this other one is the one to use to generate uniformly distributed discrete non-integers.


>>> import mars.tensor as mt
>>> mt.random.randint(2, size=10).execute()
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
>>> mt.random.randint(1, size=10).execute()
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

Generate a 2 x 4 tensor of ints between 0 and 4, inclusive:

>>> mt.random.randint(5, size=(2, 4)).execute()
array([[4, 0, 2, 1],
       [3, 2, 2, 0]])