Updating READMEs

README.md

@@ -26,10 +26,10 @@ The intention of `amp` is to be the "on-ramp" to easy FP16 training: achieve all
 
 The intention of `FP16_Optimizer` is to be the "highway" for FP16 training: achieve most of the numerically stability of full FP32 training, and almost all the performance benefits of full FP16 training.
 
-[Python Source](https://github.com/NVIDIA/apex/tree/master/apex/fp16_utils)
-
 [API Documentation](https://nvidia.github.io/apex/fp16_utils.html#automatic-management-of-master-params-loss-scaling)
 
+[Python Source](https://github.com/NVIDIA/apex/tree/master/apex/fp16_utils)
+
 [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)

examples/FP16_Optimizer_simple/README.md

 # Simple examples of FP16_Optimizer functionality
 
+#### Minimal Working Sample
 `minimal.py` shows the basic usage of `FP16_Optimizer` with either static or dynamic loss scaling.  Test via
 ```bash
 python minimal.py
 ```
 
+#### Closures
 `FP16_Optimizer` supports closures with the same control flow as ordinary Pytorch optimizers.  
 `closure.py` shows an example.  Test via
 ```bash
@@ -12,6 +14,7 @@ python closure.py
 ```
 See [the API documentation](https://nvidia.github.io/apex/fp16_utils.html#apex.fp16_utils.FP16_Optimizer.step) for more details.
 
+#### Checkpointing
 `FP16_Optimizer` also supports checkpointing with the same control flow as ordinary Pytorch optimizers.
 `save_load.py` shows an example.  Test via
 ```bash
@@ -19,6 +22,7 @@ python save_load.py
 ```
 See [the API documentation](https://nvidia.github.io/apex/fp16_utils.html#apex.fp16_utils.FP16_Optimizer.load_state_dict) for more details.
 
+#### Distributed
 **distributed_pytorch** shows an example using `FP16_Optimizer` with Pytorch DistributedDataParallel.
 The usage of `FP16_Optimizer` with distributed does not need to change from ordinary single-process 
 usage. Run via