• New features
  • Dynamic graph and custom operator support
  • OpPerf utility enabled in the binary distribution
  • MKL-DNN 
    • MKL-DNN as the default CPU backend in binary distribution
    • Branding change to DNNL
    • Support bfloat16 datatype
  • New operators
• Feature improvements
  • Numpy compatible interface
  • Large tensor supportMKLDNN
  • MKL-DNN enhancement
  • TensorRT integration
  • Quantization
  • Profiler
  • ONNX
  • New models
  • Operator improvements
  • Bug fixes
• Front end API
  • Gluon
  • Symbol
• Language Bindings
  • Python
  • C/C++
  • R
  • Clojure
  • Julia
  • Perl
  • Scala
• Performance improvements
• Example and tutorials
• Website and documentation
• CI/CD
• License
• Miscellaneous changes
• How to build MXNet
  • List of submodules used by Apache MXNet (Incubating) and when they were updated last

New features

Dynamic subgraph and customer operator support

...

• [Large Tensor] Add support to Random Sample & Pdf ops (#17445)
• [Large Tensor] Add LT support for NN optimizers and 1 activation function (#17444)
• [Large Tensor] Fixed SoftmaxActivation op (#17634)
• [Large Tensor] Fixed col2im op (#17622)
• [Large Tensor] Fixed Spatial Transformer op (#17617)
• [Large Tensor] Fix ravel_multi_index op (#17644)
• Sparse int64 Large tensor support (#16898)
• Re-Enabling Large Tensor Nightly on GPU (#16164)
• enabling build stage gpu_int64 to enable large tensor nightly runs (#17546)

MKL-DNN enhancement

• MKLDNN FC : Add error info when mkldnn fc bias dimension is wrong (#16692)
• [MKLDNN] support mkldnn gelu (#16710)
• [MKLDNN] Fix int8 convolution/fc bias overflow (#16734)
• [MKLDNN] use dim_t instead of int in slice/transpose operators (#16737)
• Mkldnn fullyConnect bwd bug fix (#16890)
• Revert Mkldnn fullyConnect bwd bug fix (#16890) (#16907)
• [MKLDNN] Use MKLDNNRun (#16772)
• [MKLDNN] mkldnn RNN operator enhancement (#17075)
• [MKLDNN] enable MaxPooling with full pooling convention (#16860)
• update mkldnn to v1.1.2 (#17165)
• improve mkldnn doc (#17198)
• [MKLDNN] Fix _copyto  (#17173)
• [MKLDNN] Support channel wise quantization for FullyConnected (#17187)
• fixed seed for mkldnn test (#17386)
• add mkldnn softmax backward  (#17170)
• cmake: copy dnnl headers to include/mkldnn (#17647)
• [mkldnn]Mkldnn bn opt backport from master to 1.7x (#18009)
• [v1.x] Update 3rdparty/mkldnn remote URL and pin to v1.3 (#17972) (#18033)
• [v1.x] backport #17900 [MKLDNN] support using any format in pooling backward (#18067)
• Static link MKL-DNN library (#16731)
• Add large tensor nightly tests for MKL-DNN operators (#16184)
•  [MKL-DNN] Enable and Optimization for s8 eltwise_add (#16931)
• [MKL-DNN] Enhance Quantization Method (#17161)
• Static Build and CD for mxnet-cu102/mxnet-cu102mkl (#17074)
• MKL-DNN RNN backward path enhancement (#17183)
• cmake: check USE_OPENMP and pass proper MKL-DNN build flags (#17356)
• update mkl to 2020.0 (#17355)
• Enable MKL-DNN by default in pip packages (#16899)
• Enable MKL-DNN FullyConnected backward (#17318)
• Softmax primitive cache and in-place computation (#17152)
• boolean_mask_assign with start_axis (#16886)
• use identity_with_cast (#16913)
• change error tolerance for bf16 bn (#18110)
• [v1.x] Backport #17689 and #17884 to v1.x branch (#18064)
• refactor codes and add an option to skip/check weight's version to reduce overhead (#17707) (#18039)
• [v1.x] Backport #17702 and #17872 to v1.x branch (#18038)

...