Readme updates + version in Sphinx

apex/fp16_utils/README.md

 fp16_optimizer.py contains `FP16_Optimizer`, a Python class designed to wrap an existing Pytorch optimizer and automatically enable master parameters and loss scaling in a manner transparent to the user.  To use `FP16_Optimizer`, only two lines of one's Python model need to change.
 
-### [FP16_Optimizer API documentation](https://nvidia.github.io/apex/fp16_utils.html#automatic-management-of-master-params-loss-scaling)
+#### [FP16_Optimizer API documentation](https://nvidia.github.io/apex/fp16_utils.html#automatic-management-of-master-params-loss-scaling)
 
-### [Simple examples with FP16_Optimizer](https://github.com/NVIDIA/apex/tree/master/examples/FP16_Optimizer_simple)
+#### [Simple examples with FP16_Optimizer](https://github.com/NVIDIA/apex/tree/master/examples/FP16_Optimizer_simple)
 
-### [Imagenet with FP16_Optimizer](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
+#### [Imagenet with FP16_Optimizer](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
 
-### [word_language_model with FP16_Optimizer](https://github.com/NVIDIA/apex/tree/master/examples/word_language_model)
+#### [word_language_model with FP16_Optimizer](https://github.com/NVIDIA/apex/tree/master/examples/word_language_model)
 
 
 fp16_util.py contains a number of utilities to manually manage master parameters and loss scaling, if the user chooses.  
 
-### [Manual management documentation](https://nvidia.github.io/apex/fp16_utils.html#manual-master-parameter-management)
+#### [Manual management documentation](https://nvidia.github.io/apex/fp16_utils.html#manual-master-parameter-management)
 
 The [Imagenet with FP16_Optimizer](https://github.com/NVIDIA/apex/tree/master/examples/imagenet) and [word_language_model with FP16_Optimizer](https://github.com/NVIDIA/apex/tree/master/examples/word_language_model) directories also contain `main.py` files that demonstrate manual management of master parameters and static loss scaling.  These examples illustrate what sort of operations `FP16_Optimizer` is performing automatically.

apex/parallel/README.md

@@ -6,10 +6,10 @@ transfers required.
 
 multiproc.py contains the source code for `apex.parallel.multiproc`, a launch utility that places one process on each of the node's available GPUs.
 
-### [API Documentation](https://nvidia.github.io/apex/parallel.html)
+#### [API Documentation](https://nvidia.github.io/apex/parallel.html)
 
-### [Example/Walkthrough](https://github.com/NVIDIA/apex/tree/master/examples/distributed)
+#### [Example/Walkthrough](https://github.com/NVIDIA/apex/tree/master/examples/distributed)
 
-### [Imagenet Example w/Mixed Precision](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
+#### [Imagenet Example w/Mixed Precision](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
 
 

docs/source/conf.py

@@ -73,10 +73,10 @@ author = 'Christian Sarofeen, Natalia Gimelshein, Michael Carilli, Raul Puri'
 # The short X.Y version.
 # TODO: change to [:2] at v1.0
 # version = 'master (' + torch.__version__ + ' )'
-version = '0.0'
+version = '0.1'
 # The full version, including alpha/beta/rc tags.
 # TODO: verify this works as expected
-release = '0.0.0'
+release = '0.1.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

examples/FP16_Optimizer_simple/README.md

@@ -5,7 +5,7 @@ half and float parameters, only two lines of your training script need to change
 1. Construct an `FP16_Optimizer` instance from an existing optimizer.
 2. Replace `loss.backward()` with `optimizer.backward(loss)`.
 
-[Full API Documentation](https://nvidia.github.io/apex/fp16_utils.html#automatic-management-of-master-params-loss-scaling)
+#### [Full API Documentation](https://nvidia.github.io/apex/fp16_utils.html#automatic-management-of-master-params-loss-scaling)
 
 See "Other Options" at the bottom of this page for some cases that require special treatment.
 

examples/distributed/README.md

@@ -13,11 +13,11 @@ global interpreter lock contention versus having a single process (with a single
 overlapping communication with computation during ``backward()`` and bucketing smaller gradient
 transfers to reduce the total number of transfers required.
 
-[API Documentation](https://nvidia.github.io/apex/parallel.html)
+#### [API Documentation](https://nvidia.github.io/apex/parallel.html)
 
-[Source Code](https://github.com/NVIDIA/apex/tree/master/apex/parallel)
+#### [Source Code](https://github.com/NVIDIA/apex/tree/master/apex/parallel)
 
-[Another Example: Imagenet with mixed precision](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
+#### [Another Example: Imagenet with mixed precision](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
 
 ## Getting started
 Prior to running please run