update ref for mgpu

docs/v1.md

@@ -36,6 +36,7 @@ The underlying changes have simplified mead considerably, making it easier to de
       - The batching operation on the examples is now completely generalized whih makes adding custom features simple
   - **Easier Extension Points**: We have removed the complexity of `addon` registration, preferring instead simple decorators to the previous method of convention-based plugins.  Documentation can be found [here](https://github.com/dpressel/baseline/blob/feature/v1/docs/addons.md)
   - **Training Simplifications**: A design goal was that a user should easily be able to train a model without using `mead`.  [It should be easier use the Baseline API to train](https://github.com/dpressel/baseline/blob/feature/v1/api-examples/tf-train-classifier-from-scratch.py)
+    - Multi-GPU support is consistent, defaults to all `CUDA_VISIBLE_DEVICES`
   - **More Documentation**: There is more code documentation, as well as API examples that show how to use the **Baseline** API directly.  These are also used to self-verify that the API is as simple to use as possible.  There is forthcoming documentation on the way that `addons` work under the hood, as this has been a point of confusion for some users
   - **Standardized Abstractions**: We have attempted to unify a set of patterns for each model/task and to try and ensure that the routines making up execution share a common naming convention and flow across each framework
 - **mead**: mead has been simplified to have better underlying (and reusable) methods to reduce code.  It also has a new style of configuration file that is more cohesive, and more powerful than before.  Here is an [example of a tagger configuration using multiple embeddings and different vectorizers](https://github.com/dpressel/baseline/blob/feature/v1/python/mead/config/twpos.json) without adding any custom components.  These models tend to perform better than single embedding models but require no custom code.