Moved CHANGELOG to docs (#2252)

* 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

CHANGELOG.md

@@ -1,74 +1,3 @@
-# Changelog
+# Change Log
 
-These are the release notes for JAX.
+See [docs/CHANGELOG.rst](https://jax.readthedocs.io/en/latest/CHANGELOG.html).
-
-## jax 0.1.60 (unreleased)
-
-### New features
-
-* `pmap` has `static_broadcast_argnums` argument which allows the user to 
-    specify arguments that should be treated as compile-time constants and 
-    should be broadcasted to all devices. It works analogously to 
-    `static_argnums` in `jit`. 
-
-## jax 0.1.59 (February 11, 2020)
-
-### Breaking changes
-
-* The minimum jaxlib version is now 0.1.38.
-* Simplified `Jaxpr` by removing the `Jaxpr.freevars` and 
-  `Jaxpr.bound_subjaxprs`. The call primitives (`xla_call`, `xla_pmap`,
-   `sharded_call`, and `remat_call`) get a new parameter `call_jaxpr` with a
-   fully-closed (no `constvars`) JAXPR.
-
-### New features
-
-* Reverse-mode automatic differentiation (e.g. `grad`) of `lax.cond`, making it
-  now differentiable in both modes (https://github.com/google/jax/pull/2091)
-* JAX now supports DLPack, which allows sharing CPU and GPU arrays in a
-  zero-copy way with other libraries, such as PyTorch.
-* JAX GPU DeviceArrays now support `__cuda_array_interface__`, which is another
-  zero-copy protocol for sharing GPU arrays with other libraries such as CuPy
-  and Numba.
-* JAX CPU device buffers now implement the Python buffer protocol, which allows
-  zero-copy buffer sharing between JAX and NumPy.
-* Added JAX_SKIP_SLOW_TESTS environment variable to skip tests known as slow.
-
-## jaxlib 0.1.38 (January 29, 2020)
-
-* CUDA 9.0 is no longer supported.
-* CUDA 10.2 wheels are now built by default.
-
-## jax 0.1.58 (January 28, 2020)
-
-### Breaking changes
-
-* JAX has dropped Python 2 support, because Python 2 reached its end of life on
-  January 1, 2020. Please update to Python 3.5 or newer.
-
-### New features
-
-* Forward-mode automatic differentiation (`jvp`) of while loop
-  (https://github.com/google/jax/pull/1980)
-* New NumPy and SciPy functions:
-  * `jax.numpy.fft.fft2`
-  * `jax.numpy.fft.ifft2`
-  * `jax.numpy.fft.rfft`
-  * `jax.numpy.fft.irfft`
-  * `jax.numpy.fft.rfft2`
-  * `jax.numpy.fft.irfft2`
-  * `jax.numpy.fft.rfftn`
-  * `jax.numpy.fft.irfftn`
-  * `jax.numpy.fft.fftfreq`
-  * `jax.numpy.fft.rfftfreq`
-  * `jax.numpy.linalg.matrix_rank`
-  * `jax.numpy.linalg.matrix_power`
-  * `jax.scipy.special.betainc`
-* Batched Cholesky decomposition on GPU now uses a more efficient batched
-  kernel.
-
-
-### Notable bug fixes
-
-* With the Python 3 upgrade, JAX no longer depends on `fastcache`, which should
-  help with installation.

README.md

@@ -7,9 +7,10 @@
 [**Quickstart**](#quickstart-colab-in-the-cloud)
 | [**Transformations**](#transformations)
 | [**Install guide**](#installation)
+| [**Change logs**](https://jax.readthedocs.io/en/latest/CHANGELOG.html)
 | [**Reference docs**](https://jax.readthedocs.io/en/latest/)
 
-**Announcement:** JAX 0.1.58 has dropped Python 2 support, and requires Python 3.5 or newer. See [CHANGELOG.md](https://github.com/google/jax/blob/master/CHANGELOG.md).
+**Announcement:** JAX 0.1.58 has dropped Python 2 support, and requires Python 3.5 or newer. See [docs/CHANGELOG.rst](https://jax.readthedocs.io/en/latest/CHANGELOG.html).
 
 ## What is JAX?
 

docs/CHANGELOG.rst

@@ -0,0 +1,91 @@
+Change Log
+==========
+
+.. This is a comment.
+   Remember to leave an empty line before the start of an itemized list,
+   and to align the itemized text with the first line of an item.
+
+These are the release notes for JAX.
+
+jax 0.1.60 (unreleased)
+-----------------------
+
+.. PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.
+
+* `GitHub commits <https://github.com/google/jax/compare/jax-v0.1.59...master>`_.
+* New features:
+
+  * :py:func:`jax.pmap` has ``static_broadcast_argnums`` argument which allows the user to
+    specify arguments that should be treated as compile-time constants and
+    should be broadcasted to all devices. It works analogously to
+    ``static_argnums`` in :py:func:`jax.jit`.
+  * Improved error messages for when tracers are mistakenly saved in global state.
+  * Added :py:func:`jax.nn.one_hot` utility function.
+
+jax 0.1.59 (February 11, 2020)
+------------------------------
+
+* `GitHub commits <https://github.com/google/jax/compare/jax-v0.1.58...jax-v0.1.59>`_.
+* Breaking changes
+
+  * The minimum jaxlib version is now 0.1.38.
+  * Simplified :py:class:`Jaxpr` by removing the ``Jaxpr.freevars`` and
+    ``Jaxpr.bound_subjaxprs``. The call primitives (``xla_call``, ``xla_pmap``,
+    ``sharded_call``, and ``remat_call``) get a new parameter ``call_jaxpr`` with a
+    fully-closed (no ``constvars``) JAXPR. Also, added a new field ``call_primitive``
+    to primitives.
+* New features:
+
+  * Reverse-mode automatic differentiation (e.g. ``grad``) of ``lax.cond``, making it
+    now differentiable in both modes (https://github.com/google/jax/pull/2091)
+  * JAX now supports DLPack, which allows sharing CPU and GPU arrays in a
+    zero-copy way with other libraries, such as PyTorch.
+  * JAX GPU DeviceArrays now support ``__cuda_array_interface__``, which is another
+    zero-copy protocol for sharing GPU arrays with other libraries such as CuPy
+    and Numba.
+  * JAX CPU device buffers now implement the Python buffer protocol, which allows
+    zero-copy buffer sharing between JAX and NumPy.
+  * Added JAX_SKIP_SLOW_TESTS environment variable to skip tests known as slow.
+
+jaxlib 0.1.38 (January 29, 2020)
+--------------------------------
+
+* CUDA 9.0 is no longer supported.
+* CUDA 10.2 wheels are now built by default.
+
+jax 0.1.58 (January 28, 2020)
+-----------------------------
+
+* `GitHub commits <https://github.com/google/jax/compare/46014da21...jax-v0.1.58>`_.
+* Breaking changes
+
+  * JAX has dropped Python 2 support, because Python 2 reached its end of life on
+    January 1, 2020. Please update to Python 3.5 or newer.
+* New features
+
+    * Forward-mode automatic differentiation (`jvp`) of while loop
+      (https://github.com/google/jax/pull/1980)
+    * New NumPy and SciPy functions:
+
+      * :py:func:`jax.numpy.fft.fft2`
+      * :py:func:`jax.numpy.fft.ifft2`
+      * :py:func:`jax.numpy.fft.rfft`
+      * :py:func:`jax.numpy.fft.irfft`
+      * :py:func:`jax.numpy.fft.rfft2`
+      * :py:func:`jax.numpy.fft.irfft2`
+      * :py:func:`jax.numpy.fft.rfftn`
+      * :py:func:`jax.numpy.fft.irfftn`
+      * :py:func:`jax.numpy.fft.fftfreq`
+      * :py:func:`jax.numpy.fft.rfftfreq`
+      * :py:func:`jax.numpy.linalg.matrix_rank`
+      * :py:func:`jax.numpy.linalg.matrix_power`
+      * :py:func:`jax.scipy.special.betainc`
+    * Batched Cholesky decomposition on GPU now uses a more efficient batched
+      kernel.
+
+
+Notable bug fixes
+^^^^^^^^^^^^^^^^^
+
+* With the Python 3 upgrade, JAX no longer depends on ``fastcache``, which should
+  help with installation.

docs/index.rst

@@ -34,6 +34,7 @@ For an introduction to JAX, start at the
    :maxdepth: 1
    :caption: Notes
 
+   CHANGELOG
    jaxpr
    async_dispatch
    concurrency