Move the main documentation of the transfer guard from "Notes" to "Reference documentation" section for better visibility.
Add a link to the main documentation to the docstring of jax.transfer_guard(), which currently shows up as the top result when searching for "jax transfer_guard".
* 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
Change the JAX type promotion table to prefer inexact types during type promotion.
NumPy's type promotion rules tend to promote aggressively to float64, which isn't a very accelerator-friendly behavior when not all accelerators (e.g., TPUs) support 64-bit floating point types. Even on accelerators that support 64-bit floating point types (e.g., GPUs), promotion to a 64-bit type comes with a significant performance cost.
This change makes JAX type promotion between inexact and exact types closer to PyTorch's promotion semantics, which are a better fit for modern accelerators:
e.g.,
```
import numpy as onp
from jax import numpy as np
In [1]: onp.promote_types(onp.float32, onp.int32)
Out[1]: dtype('float64')
In [2]: onp.promote_types(onp.float16, onp.int64)
Out[2]: dtype('float64')
In [3]: np.promote_types(onp.float32, onp.int32)
Out[3]: dtype('float32')
In [4]: np.promote_types(onp.float16, onp.int64)
Out[4]: dtype('float16')
```
This change is in preparation for enabling x64 mode by default on all platforms.
* Moved all notebooks to docs/notebooks.
Now all notebooks are in the same place, thus all are subject
to auto-doc generation at readthedocs.io and to automated testing
with travis.
Some notebooks are too slow, exclude them at docs/conf.py:exclude_patterns.
Cleanup a bit the section headings in notebooks so that they show
up well in readtehdocs.io.
* Increase the cell timeout for executing notebooks
* Exclude also the neural network notebook from auto-generation (timing out)
* Disable the score_matching notebook from auto-doc (travis does not have sklearn)
* Cleaned up use of section levels
* Renamed ma to multiply_add and sq_add to square_add
* Other minor clarifications
* Separated the Colabs into Tutorials and Advanced Tutorials
Using threading within a traced context still won't work, but that is perhaps less important than the ability to call JIT-ted computations from separate threads.
(Revives https://github.com/google/jax/pull/734.)
