New Features - Export MXNet models to ONNX format

• With this feature, now MXNet models can be exported to ONNX format.(#11213 ) Currently, MXNet supports ONNX v1.2.1 . API documentation.
• Checkout this example which shows how to use MXNet to ONNX exporter APIs. ONNX protobuf so that those models can be imported in other frameworks for inference.

New Features - Topology-aware AllReduce

New Features - Clojure package (experimental)

• MXNet now supports the Clojure programming language. The MXNet Clojure package brings flexible and efficient GPU computing and state-of-art deep learning to Clojure. It enables you to write seamless tensor/matrix computation with multiple GPUs in Clojure. It also lets you construct and customize the state-of-art deep learning models in Clojure, and apply them to tasks, such as image classification and data science challenges.(#11205)
  
• Checkout examples and API documentation here.

New Features - TensorRT Runtime Integration

• This feature introduces runtime integration of TensorRT into MxNet, in order to accelerate inference. (#11325)
  

New Features - Sparse Tensor support for Gluon

• #11429, #11231, #11197, #11001, #10924 experimental feature
  
  

New Features - Fused RNN Operators for CPU

• #10104 #11399 #10311 vRNN/LSTM/GRU PR has been merged

New Features - Control flow operators

• This is the first step towards optimizing dynamic neural networks by adding symbolic and imperative control flow operators (foreach, while_loop, maybe ifelse). foreach and while_loop have been merged. ifelse has been submitted for review, but is unlikely merged in this release.

MKL-DNN

Fix Flaky Tests