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. For Gluon tutorials, see The Straight Dope.
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 API HybridBlock.export for exporting gluon models to MXNet format.
Added gluon.contrib package.
- Convolutional recurrent network cells for RNN, LSTM and GRU.
- VariationalDropoutCell

Added enhancements to autograd package, which enables automatic differentiation of NDArray operations.
autograd.Function allows defining both forward and backward computation for custom operators. See documentation for examples.
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.

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.
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. See documentation for examples.
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.

Added support for sparse matrices. See documentation for more info.
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.

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.

Fixed a bug that causes argsort operator to fail on large tensors.
Fixed numerical stability issues when summing large tensors.
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.

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