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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)