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kjappelbaum / pyepal

Multiobjective active learning with tunable accuracy/efficiency tradeoff and clear stopping criterion.
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chore(deps-dev): update jax requirement from <=0.2.12 to <0.4.3 #273

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

Updates the requirements on jax to permit the latest version.

Changelog

Sourced from jax's changelog.

jax 0.4.2 (Jan 20, 2023)

  • Breaking changes

    • Deleted jax.experimental.callback
    • Operations with dimensions in presence of jax2tf shape polymorphism have been generalized to work in more scenarios, by converting the symbolic dimension to JAX arrays. Operations involving symbolic dimensions and np.ndarray now can raise errors when the result is used as a shape value ({jax-issue}[#14106](https://github.com/google/jax/issues/14106)).
    • jaxpr objects now raise an error on attribute setting in order to avoid problematic mutations ({jax-issue}14102)

  • Changes

    • {func}jax2tf.call_tf has a new parameter has_side_effects (default True) that can be used to declare whether an instance can be removed or replicated by JAX optimizations such as dead-code elimination ({jax-issue}[#13980](https://github.com/google/jax/issues/13980)).

jaxlib 0.4.2 (Jan 20, 2023)

  • Changes
    • Set JAX_USE_PJRT_C_API_ON_TPU=1 to enable new Cloud TPU runtime, featuring automatic device memory defragmentation.

jax 0.4.1 (Dec 13, 2022)

  • Changes
    • Support for Python 3.7 has been dropped, in accordance with JAX's {ref}version-support-policy.
    • We introduce jax.Array which is a unified array type that subsumes DeviceArray, ShardedDeviceArray, and GlobalDeviceArray types in JAX. The jax.Array type helps make parallelism a core feature of JAX, simplifies and unifies JAX internals, and allows us to unify jit and pjit. jax.Array has been enabled by default in JAX 0.4 and makes some breaking change to the pjit API. The jax.Array migration guide can help you migrate your codebase to jax.Array. You can also look at the Distributed arrays and automatic parallelization tutorial to understand the new concepts.
    • PartitionSpec and Mesh are now out of experimental. The new API endpoints are jax.sharding.PartitionSpec and jax.sharding.Mesh. jax.experimental.maps.Mesh and jax.experimental.PartitionSpec are deprecated and will be removed in 3 months.
    • with_sharding_constraints new public endpoint is jax.lax.with_sharding_constraint.
    • If using ABSL flags together with jax.config, the ABSL flag values are no longer read or written after the JAX configuration options are initially populated from the ABSL flags. This change improves performance of reading jax.config options, which are used pervasively in JAX.
    • The jax2tf.call_tf function now uses for TF lowering the first TF device of the same platform as used by the embedding JAX computation.

... (truncated)

Commits
  • 838bc45 Merge pull request #14148 from skye:version
  • c4ad27c Update libtpu version for jaxlib 0.4.2 release (again)
  • 09794be Use jax.config instead of config because pickle does not like using the c...
  • 5aea7d9 [sparse] Add function that fixes out-of-bound indices.
  • 7d2031b Merge pull request #14135 from skye:version
  • 9a7f29a Update WORKSPACE for jaxlib 0.4.2 release (again)
  • b621373 Cache the creation of ClosedJaxpr in pjit_transpose which if not cached break...
  • bbccf55 Merge pull request #14131 from jakevdp:mypy-update
  • 94af71a CI: fix mypy jaxlib version
  • 7064be1 Skip unneccessary unflattening of avals in pjit lowering path.
  • Additional commits viewable in compare view


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dependabot[bot] commented 1 year ago

Superseded by #278.