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

This features uses trees to perform the Reduce and Broadcast. It uses the idea of minimum spanning trees to do a binary tree Reduce communication pattern to improve it. This topology aware approach reduces the existing limitations for single machine communication shown by mehods like parameter server and NCCL ring reduction. It is an experimental feature (#11591 )
Paper followed for implementation: Optimal message scheduling for aggregation.

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.

TensorRT provides significant acceleration of model inference on NVIDIA GPUs compared to running the full graph in MxNet using unfused GPU operators. In addition to faster fp32 inference, TensorRT optimizes fp16 inference, and is capable of int8 inference (provided the quantization steps are performed). Besides increasing throughput, TensorRT significantly reduces inference latency, especially for small batches.
This feature in MXNet now introduces runtime integration of TensorRT into MxNet, in order to accelerate inference. (#11325)
Currently, its in contrib package.

MXNet already provides fused RNN operators for users to run on GPU with CuDNN interfaces. But there was no support to use these operators on CPU.
Now with this release, MXNet provides these operators for CPUs too!
Fused RNN operators added for CPU: LSTM(#10104), vanilla RNN(#10104), GRU(#10311)

This is the first step towards optimizing dynamic neural networks by adding symbolic and imperative control flow operators. Proposal
New operators introduced: foreach(#11531), while_loop(#11566), cond(#11760).

Fix Flaky Tests

Submodule:: Last updated by MXNet:: Last update in submodule