Performance

• Enhanced the performance of `sparse.dot` operator
• Performance regression automation (vikram)

New Features - Gradient Compression

• Enabled users to train models faster by reducing communication bandwidth between compute nodes

New Features - Support of NVIDIA Collective Communication Library

• Implemented multi-GPU and multi-node collective communication primitives that are performance optimized for NVIDIA GPUs
• Enabled users to train models faster

New Features - Advanced Indexing

• Enabled users to leverage the powerful array operations in MXNet (e.g. supports MXNet NDArray and Numpy ndarray as index

New Features - Caffe to MXNet translator

• Enabled users to migrate Caffe code to MXNet using a new code translation tool

New Features - Gluon

• Gluon - GA (vikram)
• Gluon IO performance (sukwon, Mu)

Other Planned Features

• Added `group2ctxs` argument to `Module` API to support model parallelism on CPU & GPU
• Advanced indexing Support: https://github.com/apache/incubator-mxnet/pull/8246 (Jun)
• slice operator supporting parameter step: https://github.com/apache/incubator-mxnet/pull/8558 (Jun)
• PR 8489 - https://github.com/apache/incubator-mxnet/pull/8489 (Eric)
• Nvidia PR - https://github.com/apache/incubator-mxnet/pull/8294

Bug Fixes

• Fixed a[-1] indexing doesn't work on NDArray
• Fixed `expand_dims` if axis < 0

Doc Updates

• License Headers: https://github.com/apache/incubator-mxnet/issues/7749 (Meghna)
• Documentation updates (markhama)
  
  

It is now more intuitive for developers to leverage the powerful array operations in MXNet. They can use the advanced indexing capability by leveraging existing knowledge of Numpy/SciPy arrays. For example, it supports MXNet NDArray and Numpy ndarray as index, e.g. (a[mx.nd.array([1,2], dtype = ‘int32’)