.. _random:

Random

Random sampling functions in MLX use an implicit global PRNG state by default. However, all function take an optional key keyword argument for when more fine-grained control or explicit state management is needed.

For example, you can generate random numbers with:

.. code-block:: python

for _ in range(3): print(mx.random.uniform())

which will print a sequence of unique pseudo random numbers. Alternatively you can explicitly set the key:

.. code-block:: python

key = mx.random.key(0) for _ in range(3): print(mx.random.uniform(key=key))

which will yield the same pseudo random number at each iteration.

Following JAX's PRNG design <https://jax.readthedocs.io/en/latest/jep/263-prng.html>_ we use a splittable version of Threefry, which is a counter-based PRNG.

.. currentmodule:: mlx.core.random

.. autosummary:: :toctree: _autosummary

bernoulli categorical gumbel key normal multivariate_normal randint seed split truncated_normal uniform laplace permutation