Preparing for 0.12.0.rc0: Final changes before RC (#8301)

* Final changes before RC

* Updates to NEWS.md

* Updates

NEWS.md

@@ -1,34 +1,48 @@
 MXNet Change Log
 ================
 ## 0.12.0
-### New Features - Sparse Tensor Support
-  - Added limited cpu support for two sparse formats for `Symbol` and `NDArray` - `CSRNDArray` and `RowSparseNDArray`
-  - Added a sparse dot product operator and many element-wise sparse operators
-  - Added a data iterator for sparse data input - `LibSVMIter`
-  - Added three optimizers for sparse gradient updates: `Ftrl`, `SGD` and `Adam`
-  - Added `push` and `row_sparse_pull` with `RowSparseNDArray` in distributed kvstore
-### New Features - Autograd and Gluon
-  - New loss functions added - `SigmoidBinaryCrossEntropyLoss`, `CTCLoss`, `HuberLoss`, `HingeLoss`, `SquaredHingeLoss`, `LogisticLoss`, `TripletLoss`
+### Performance
+  - Added full support for NVIDIA Volta GPU Architecture and CUDA 9. Training is up to 3.5x faster than Pascal when using float16.
+  - Enabled JIT compilation. Autograd and Gluon hybridize now use less memory and has faster speed. Performance is almost the same with old symbolic style code.
+  - Improved ImageRecordIO image loading performance and added indexed RecordIO support.
+  - Added better openmp thread management to improve CPU performance.
+### New Features - Gluon
+  - Added enhancements to the Gluon package, a high-level interface designed to be easy to use while keeping most of the flexibility of low level API. Gluon supports both imperative and symbolic programming, making it easy to train complex models imperatively with minimal impact on performance. Neural networks (and other machine learning models) can be defined and trained with `gluon.nn` and `gluon.rnn` packages. 
+  - Added new loss functions - `SigmoidBinaryCrossEntropyLoss`, `CTCLoss`, `HuberLoss`, `HingeLoss`, `SquaredHingeLoss`, `LogisticLoss`, `TripletLoss`.
   - `gluon.Trainer` now allows reading and setting learning rate with `trainer.learning_rate` property.
-  - Added `mx.autograd.grad` and experimental second order gradient support (though most operators don't support second order gradient yet)
-  - Added `ConvLSTM` etc to `gluon.contrib`
+  - Added API `HybridBlock.export` for exporting gluon models to MXNet format.
+  - Added `gluon.contrib` package.
+    - Convolutional recurrent network cells for RNN, LSTM and GRU.
+    - `VariationalDropoutCell`
+### New Features - Autograd
+  - Added enhancements to `autograd` package, which enables automatic differentiation of NDArray operations.
+  - `autograd.Function` allows defining both forward and backward computation for custom operators.
+  - Added `mx.autograd.grad` and experimental second order gradient support (most operators don't support second order gradient yet).
   - Autograd now supports cross-device graphs. Use `x.copyto(mx.gpu(i))` and `x.copyto(mx.cpu())` to do computation on multiple devices.
+### New Features - Sparse Tensor Support
+  - Added support for sparse matrices. 
+  - Added limited cpu support for two sparse formats in `Symbol` and `NDArray` - `CSRNDArray` and `RowSparseNDArray`.
+  - Added a sparse dot product operator and many element-wise sparse operators.
+  - Added a data iterator for sparse data input - `LibSVMIter`.
+  - Added three optimizers for sparse gradient updates: `Ftrl`, `SGD` and `Adam`.
+  - Added `push` and `row_sparse_pull` with `RowSparseNDArray` in distributed kvstore.
 ### Other New Features
-  - Limited support for fancy indexing. x[idx_arr0, idx_arr1, ..., idx_arrn] is now supported. Full support coming soon in next release. Checkout master to get a preview.
-  - Random number generators in `mx.nd.random.*` and `mx.sym.random.*` now supports both CPU and GPU
-  - `NDArray` and `Symbol` now supports "fluent" methods. You can now use `x.exp()` etc instead of `mx.nd.exp(x)` or `mx.sym.exp(x)`
-  - Added `mx.rtc.CudaModule` for writing and running CUDA kernels from python
-  - Added `multi_precision` option to optimizer for easier float16 training
-### Performance
-  - Enabled JIT compilation. Autograd and Gluon hybridize now use less memory and has faster speed. Performance is almost the same with old symbolic style code.
-  - Full support for NVidia Volta GPU Architecture and Cuda 9. Training is up to 3.5x faster than Pascal when using float16.
+  - Added limited support for fancy indexing, which allows you to very quickly access and modify complicated subsets of an array's values. `x[idx_arr0, idx_arr1, ..., idx_arrn]` is now supported. Features such as combining and slicing are planned for the next release. Checkout master to get a preview.
+  - Random number generators in `mx.nd.random.*` and `mx.sym.random.*` now support both CPU and GPU.
+  - `NDArray` and `Symbol` now supports "fluent" methods. You can now use `x.exp()` etc instead of `mx.nd.exp(x)` or `mx.sym.exp(x)`.
+  - Added `mx.rtc.CudaModule` for writing and running CUDA kernels from python. 
+  - Added `multi_precision` option to optimizer for easier float16 training.
+  - Better support for IDE auto-completion. IDEs like PyCharm can now correctly parse mxnet operators.
 ### API Changes
   - Operators like `mx.sym.linalg_*` and `mx.sym.random_*` are now moved to `mx.sym.linalg.*` and `mx.sym.random.*`. The old names are still available but deprecated.
   - `sample_*` and `random_*` are now merged as `random.*`, which supports both scalar and  `NDArray` distribution parameters.
 ### Bug-fixes
   - Fixed a bug that causes `argsort` operator to fail on large tensors.
   - Fixed numerical stability issues when summing large tensors.
-For more information see [full release notes](https://cwiki.apache.org/confluence/display/MXNET/MXNet+0.12.0+Release+Notes)
+  - Fixed a bug that causes arange operator to output wrong results for large ranges.
+  - Improved numerical precision for unary and binary operators on `float64` inputs.
+
+For more information and examples, see [full release notes](https://cwiki.apache.org/confluence/display/MXNET/MXNet+0.12.0+Release+Notes)
 
 
 ## 0.11.0

README.md

@@ -22,6 +22,7 @@ deep learning systems, and interesting insights of DL systems for hackers.
 
 What's New
 ----------
+* [Version 0.12.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/0.12.0) - MXNet 0.12.0 Release.
 * [Version 0.11.0 Release](https://github.com/apache/incubator-mxnet/releases/tag/0.11.0) - MXNet 0.11.0 Release.
 * [Apache Incubator](http://incubator.apache.org/projects/mxnet.html) - We are now an Apache Incubator project.
 * [Version 0.10.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.10.0) - MXNet 0.10.0 Release.

setup-utils/install-mxnet-osx-python.sh

@@ -33,7 +33,7 @@ then
 	# TODO: Change this to latest tag
 	#       to avoid updating this value for every release
 	#
-	export MXNET_TAG="v0.10.0"
+	export MXNET_TAG="0.12.0"
 fi
 
 export TARIKH=`/bin/date +%Y-%m-%d-%H:%M:%S`