Jaxlib release v0.1.74

jaxlib-v0.1.74

Jaxlib v0.1.74

JAX release v0.2.25

• New features:

  • (Experimental) jax.distributed.initialize exposes multi-host GPU backend.
  • jax.random.permutation supports new independent keyword argument
    ({jax-issue}#8430)

• Breaking changes

  • Moved jax.experimental.stax to jax.example_libraries.stax
  • Moved jax.experimental.optimizers to jax.example_libraries.optimizers

• New features:

  • Added jax.lax.linalg.qdwh.

Jax release v0.2.24

• New features:
  • jax.random.choice and jax.random.permutation now support
    multidimensional arrays and an optional axis argument (#8158)
• Breaking changes:
  • jax.numpy.take and jax.numpy.take_along_axis now require array-like inputs
    (see #7737)

Jaxlib release v0.1.73

Update the workspace file

PiperOrigin-RevId: 404076864

jaxlib release v0.1.72

Merge pull request #8181 from skye:workspace

PiperOrigin-RevId: 402632543

Jax release v0.2.21

• New features:

  • Added jax.numpy.insert implementation (#7936 ).

• Breaking Changes

  • jax.api has been removed. Functions that were available as jax.api.*
    were aliases for functions in jax.*; please use the functions in
    jax.* instead.
  • jax.partial, jax.lax.partial, and jax.util.partial were accidental
    exports that have now been removed. Use functools.partial from the Python
    standard library instead.
  • Boolean scalar indices now raise a TypeError; previously this silently
    returned wrong results (#7925 ).
  • Many more jax.numpy functions now require array-like inputs, and will error
    if passed a list (#7747 #7802 #7907 ).
    See #7737 for a discussion of the rationale behind this change.
  • When inside a transformation such as jax.jit, jax.numpy.array always
    stages the array it produces into the traced computation. Previously
    jax.numpy.array would sometimes produce a on-device array, even under
    a jax.jit decorator. This change may break code that used JAX arrays to
    perform shape or index computations that must be known statically; the
    workaround is to perform such computations using classic NumPy arrays
    instead.
  • jnp.ndarray is now a true base-class for JAX arrays. In particular, this
    means that for a standard numpy array x, isinstance(x, jnp.ndarray) will
    now return False (#7927).

Jax release v0.2.20

Merge pull request #7793 from yashk2810:update_pypi

PiperOrigin-RevId: 394697075

Jaxlib release v0.1.71

Merge pull request #7774 from yashk2810:workspace

PiperOrigin-RevId: 394233543

jaxlib v0.1.70 release

Update WORKSPACE

PiperOrigin-RevId: 389037137

jaxlib-v0.1.55

jaxlib version 0.1.55