Update jax[cpu] requirement from <0.4.4 to <0.4.5

Updates the requirements on jax[cpu] to permit the latest version.

Changelog

jax 0.4.4 (Feb 16, 2023)

Changes

The implementation of jit and pjit has been merged. Merging jit and pjit changes the internals of JAX without affecting the public API of JAX. Before, jit was a final style primitive. Final style means that the creation of jaxpr was delayed as much as possible and transformations were stacked on top of each other. With the jit-pjit implementation merge, jit becomes an initial style primitive which means that we trace to jaxpr as early as possible. For more information see this section in autodidax. Moving to initial style should simplify JAX's internals and make development of features like dynamic shapes, etc easier. You can disable it only via the environment variable i.e. os.environ['JAX_JIT_PJIT_API_MERGE'] = '0'. The merge must be disabled via an environment variable since it affects JAX at import time so it needs to be disabled before jax is imported.

axis_resources argument of with_sharding_constraint is deprecated. Please use shardings instead. There is no change needed if you were using axis_resources as an arg. If you were using it as a kwarg, then please use shardings instead. axis_resources will be removed after 3 months from Feb 13, 2023.

added the {mod}jax.typing module, with tools for type annotations of JAX functions.

The following names have been deprecated:

jax.xla.Device and jax.interpreters.xla.Device: use jax.Device.

jax.experimental.maps.Mesh. Use jax.sharding.Mesh instead.

jax.experimental.pjit.NamedSharding: use jax.sharding.NamedSharding.

jax.experimental.pjit.PartitionSpec: use jax.sharding.PartitionSpec.

jax.interpreters.pxla.Mesh: use jax.sharding.Mesh.

jax.interpreters.pxla.PartitionSpec: use jax.sharding.PartitionSpec.

Breaking Changes

the initial argument to reduction functions like :func:jax.numpy.sum is now required to be a scalar, consistent with the corresponding NumPy API. The previous behavior of broadcating the output against non-scalar initial values was an unintentional implementation detail ({jax-issue}[#14446](https://github.com/google/jax/issues/14446)).

jaxlib 0.4.4 (Feb 16, 2023)

Breaking changes

Support for NVIDIA Kepler series GPUs has been removed from the default jaxlib builds. If Kepler support is needed, it is still possible to build jaxlib from source with Kepler support (via the --cuda_compute_capabilities=sm_35 option to build.py), however note that CUDA 12 has completely dropped support for Kepler GPUs.

jax 0.4.3 (Feb 8, 2023)

Breaking changes

Deleted {func}jax.scipy.linalg.polar_unitary, which was a deprecated JAX extension to the scipy API. Use {func}jax.scipy.linalg.polar instead.

... (truncated)

Commits

58e46b4 Prepare for jax and jaxlib 0.4.4 release
c6a99b6 Remove jax.interpreters.xla.lower_fun.
a9e886f [jax2tf] Enable all native lowering jax2tf tests
454e4de [shape_poly] Fix the lowering for symbolic dimension expressions for division
d0b42f2 Fix the simple bug on call_tf.replace_non_float and add unittest for floating...
26045c4 remove core.{aval_method,aval_property}
d8514d0 Merge pull request #14500 from jakevdp:bcsr-matmul-test
0af9fff Replace uses of deprecated JAX sharding APIs with their new names in jax.shar...
1b2a318 remove core.axis_substitution_rules
768960b Fix pytype errors.
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probabilistic-numerics / probnum

Update jax[cpu] requirement from <0.4.4 to <0.4.5 #791

jax 0.4.4 (Feb 16, 2023)

jaxlib 0.4.4 (Feb 16, 2023)

jax 0.4.3 (Feb 8, 2023)

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