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* Moved CHANGELOG to docs This puts the documentation also on RTD, with TOC. Also changed its format to .rst, for consistency. Added GitHub links to the change log. * Actually add the CHANGELOG.rst * Added reminder comments to the CHANGELOG.rst
92 lines
3.4 KiB
ReStructuredText
92 lines
3.4 KiB
ReStructuredText
Change Log
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==========
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.. This is a comment.
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Remember to leave an empty line before the start of an itemized list,
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and to align the itemized text with the first line of an item.
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These are the release notes for JAX.
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jax 0.1.60 (unreleased)
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-----------------------
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.. PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.
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* `GitHub commits <https://github.com/google/jax/compare/jax-v0.1.59...master>`_.
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* New features:
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* :py:func:`jax.pmap` has ``static_broadcast_argnums`` argument which allows the user to
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specify arguments that should be treated as compile-time constants and
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should be broadcasted to all devices. It works analogously to
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``static_argnums`` in :py:func:`jax.jit`.
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* Improved error messages for when tracers are mistakenly saved in global state.
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* Added :py:func:`jax.nn.one_hot` utility function.
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jax 0.1.59 (February 11, 2020)
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------------------------------
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* `GitHub commits <https://github.com/google/jax/compare/jax-v0.1.58...jax-v0.1.59>`_.
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* Breaking changes
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* The minimum jaxlib version is now 0.1.38.
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* Simplified :py:class:`Jaxpr` by removing the ``Jaxpr.freevars`` and
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``Jaxpr.bound_subjaxprs``. The call primitives (``xla_call``, ``xla_pmap``,
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``sharded_call``, and ``remat_call``) get a new parameter ``call_jaxpr`` with a
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fully-closed (no ``constvars``) JAXPR. Also, added a new field ``call_primitive``
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to primitives.
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* New features:
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* Reverse-mode automatic differentiation (e.g. ``grad``) of ``lax.cond``, making it
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now differentiable in both modes (https://github.com/google/jax/pull/2091)
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* JAX now supports DLPack, which allows sharing CPU and GPU arrays in a
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zero-copy way with other libraries, such as PyTorch.
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* JAX GPU DeviceArrays now support ``__cuda_array_interface__``, which is another
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zero-copy protocol for sharing GPU arrays with other libraries such as CuPy
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and Numba.
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* JAX CPU device buffers now implement the Python buffer protocol, which allows
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zero-copy buffer sharing between JAX and NumPy.
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* Added JAX_SKIP_SLOW_TESTS environment variable to skip tests known as slow.
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jaxlib 0.1.38 (January 29, 2020)
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--------------------------------
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* CUDA 9.0 is no longer supported.
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* CUDA 10.2 wheels are now built by default.
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jax 0.1.58 (January 28, 2020)
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-----------------------------
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* `GitHub commits <https://github.com/google/jax/compare/46014da21...jax-v0.1.58>`_.
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* Breaking changes
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* JAX has dropped Python 2 support, because Python 2 reached its end of life on
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January 1, 2020. Please update to Python 3.5 or newer.
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* New features
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* Forward-mode automatic differentiation (`jvp`) of while loop
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(https://github.com/google/jax/pull/1980)
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* New NumPy and SciPy functions:
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* :py:func:`jax.numpy.fft.fft2`
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* :py:func:`jax.numpy.fft.ifft2`
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* :py:func:`jax.numpy.fft.rfft`
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* :py:func:`jax.numpy.fft.irfft`
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* :py:func:`jax.numpy.fft.rfft2`
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* :py:func:`jax.numpy.fft.irfft2`
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* :py:func:`jax.numpy.fft.rfftn`
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* :py:func:`jax.numpy.fft.irfftn`
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* :py:func:`jax.numpy.fft.fftfreq`
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* :py:func:`jax.numpy.fft.rfftfreq`
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* :py:func:`jax.numpy.linalg.matrix_rank`
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* :py:func:`jax.numpy.linalg.matrix_power`
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* :py:func:`jax.scipy.special.betainc`
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* Batched Cholesky decomposition on GPU now uses a more efficient batched
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kernel.
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Notable bug fixes
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^^^^^^^^^^^^^^^^^
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* With the Python 3 upgrade, JAX no longer depends on ``fastcache``, which should
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help with installation.
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