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.
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 ({jax-issue}[#7925](https://github.com/google/jax/issues/7925)).
Many more jax.numpy functions now require array-like inputs, and will error
if passed a list ({jax-issue}[#7747](https://github.com/google/jax/issues/7747) {jax-issue}[#7802](https://github.com/google/jax/issues/7802) {jax-issue}[#7907](https://github.com/google/jax/issues/7907)).
See {jax-issue}[#7737](https://github.com/google/jax/issues/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.
Support for NumPy 1.17 has been dropped, per the
deprecation policy.
Please upgrade to a supported NumPy version.
The jit decorator has been added around the implementation of a number of
operators on JAX arrays. This speeds up dispatch times for common
operators such as +.
This change should largely be transparent to most users. However, there is
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Bumps jax from 0.2.8 to 0.2.21.
Release notes
Sourced from jax's releases.
Changelog
Sourced from jax's changelog.
... (truncated)
Commits
dbeb97d
Create 0.2.21 jax releaseb6318f8
Merge pull request #7993 from gnecula:tf_share_constb992034
Merge pull request #7983 from jakevdp:fix-std-basisb1ad9e3
[jax2tf] Ensure that shared constants in JAX are shared also in the converted...9389db1
Merge pull request #7975 from google:custom-derivatives-api-boundary2c2f403
Move contents of jax.lib to jax._src.lib.94cd1ea
Merge pull request #7978 from gnecula:tf_comment322a954
disable pytype on custom derivative calls0957e81
Use traced identity in jacobian std_basis484b5af
Merge pull request #7982 from hawkinsp:checktolDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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