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MXNet Change Log

0.10.0

  • Overhauled documentation for commonly used Python APIs, Installation instructions, Tutorials, HowTos and MXNet Architecture.
  • Updated mxnet.io for improved readability.
  • Pad operator now support reflection padding.
  • Fixed a memory corruption error in threadedengine.
  • Added CTC loss layer to contrib package. See mx.contrib.sym.ctc_loss.
  • Added new sampling operators for several distributions (normal,uniform,gamma,exponential,negative binomial).
  • Added documentation for experimental RNN APIs.

0.9.3

  • Move symbolic API to NNVM @tqchen
    • Most front-end C API are backward compatible
    • Removed symbolic API in MXNet and relies on NNVM
  • New features:
    • MXNet profiler for profiling operator-level executions
    • mxnet.image package for fast image loading and processing
  • Change of JSON format
    • param and attr field are merged to attr
    • New code is backward-compatible can load old json format
  • OpProperty registration now is deprecated
    • New operators are encouraged to register their property to NNVM op registry attribute
  • Known features removed limitations to be fixed
    • Bulk segment execution not yet added.

v0.8

This is the last release before the NNVM refactor.

  • CaffeOp and CaffeIter for interfacing with Caffe by @HrWangChengdu @cjolivier01
  • WrapCTC plugin for sequence learning by @xlvector
  • Improved Multi-GPU performance by @mli
  • CuDNN RNN support by @sbodenstein
  • OpenCV plugin for parallel image IO by @piiswrong
  • More operators as simple op
    • Simple OP @tqchen
    • element wise op with axis and broadcast @mli @sxjscience
  • Cudnn auto tuning for faster convolution by @piiswrong
  • More applications
    • Faster RCNN by @precedenceguo

v0.7

  • 0.6 is skipped because there are a lot of improvements since initial release
  • More math operators
    • elementwise ops and binary ops
  • Attribute support in computation graph
    • Now user can use attributes to give various hints about specific learning rate, allocation plans etc
  • MXNet is more memory efficient
    • Support user defined memory optimization with attributes
  • Support mobile applications by @antinucleon
  • Refreshed update of new documents
  • Model parallel training of LSTM by @tqchen
  • Simple operator refactor by @tqchen
    • add operator_util.h to enable quick registration of both ndarray and symbolic ops
  • Distributed training by @mli
  • Support Torch Module by @piiswrong
    • MXNet now can use any of the modules from Torch.
  • Support custom native operator by @piiswrong
  • Support data types including fp16, fp32, fp64, int32, and uint8 by @piiswrong
  • Support monitor for easy printing and debugging by @piiswrong
  • Support new module API by @pluskid
    • Module API is a middle level API that can be used in imperative manner like Torch-Module
  • Support bucketing API for variable length input by @pluskid
  • Support CuDNN v5 by @antinucleon
  • More applications

v0.5 (initial release)

  • All basic modules ready