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MXNet Change Log

0.12.0

New Features - Sparse Tensor Support

  • Added limited cpu support for two sparse formats for Symbol and NDArray - CSRNDArray and RowSparseNDArray
  • Added a sparse dot product operator and many element-wise sparse operators
  • Added a data iterator for sparse data input - LibSVMIter
  • Added three optimizers for sparse gradient updates: Ftrl, SGD and Adam
  • Added push and row_sparse_pull with RowSparseNDArray in distributed kvstore

New Features - Autograd and Gluon

  • New loss functions added - SigmoidBinaryCrossEntropyLoss, CTCLoss, HuberLoss, HingeLoss, SquaredHingeLoss, LogisticLoss, TripletLoss
  • gluon.Trainer now allows reading and setting learning rate with trainer.learning_rate property.
  • Added mx.autograd.grad and experimental second order gradient support (though most operators don't support second order gradient yet)
  • Added ConvLSTM etc to gluon.contrib
  • Autograd now supports cross-device graphs. Use x.copyto(mx.gpu(i)) and x.copyto(mx.cpu()) to do computation on multiple devices.

Other New Features

  • Limited support for fancy indexing. x[idx_arr0, idx_arr1, ..., idx_arrn] is now supported. Full support coming soon in next release. Checkout master to get a preview.
  • Random number generators in mx.nd.random.* and mx.sym.random.* now supports both CPU and GPU
  • NDArray and Symbol now supports "fluent" methods. You can now use x.exp() etc instead of mx.nd.exp(x) or mx.sym.exp(x)
  • Added mx.rtc.CudaModule for writing and running CUDA kernels from python
  • Added multi_precision option to optimizer for easier float16 training

Performance

  • Enabled JIT compilation. Autograd and Gluon hybridize now use less memory and has faster speed. Performance is almost the same with old symbolic style code.
  • Full support for NVidia Volta GPU Architecture and Cuda 9. Training is up to 3.5x faster than Pascal when using float16.

API Changes

  • Operators like mx.sym.linalg_* and mx.sym.random_* are now moved to mx.sym.linalg.* and mx.sym.random.*. The old names are still available but deprecated.
  • sample_* and random_* are now merged as random.*, which supports both scalar and NDArray distribution parameters.

Bug-fixes

  • Fixed a bug that causes argsort operator to fail on large tensors.
  • Fixed numerical stability issues when summing large tensors. For more information see full release notes

0.11.0

Major Features

  • Apple Core ML model converter
  • Support for Keras v1.2.2
  • For more information see full release notes

API Changes

  • Added CachedOp. You can now cache the operators that’s called frequently with the same set of arguments to reduce overhead.
  • Added sample_multinomial for sampling from multinomial distributions.
  • Added trunc operator for rounding towards zero.
  • Added linalg_gemm, linalg_potrf, ... operators for lapack support.
  • Added verbose option to Initializer for printing out initialization details.
  • Added DeformableConvolution to contrib from the Deformable Convolutional Networks paper.
  • Added float64 support for dot and batch_dot operator.
  • allow_extra is added to Module.set_params to ignore extra parameters.
  • Added mod operator for modulo.
  • Added multi_precision option to SGD optimizer to improve training with float16. Resnet50 now achieves the same accuracy when trained with float16 and gives 50% speedup on Titan XP.

Performance Improvements

  • ImageRecordIter now stores data in pinned memory to improve GPU memcopy speed.

Bugfixes

  • Cython interface is fixed. make cython and python setup.py install --with-cython should install the cython interface and reduce overhead in applications that use imperative/bucketing.
  • Fixed various bugs in Faster-RCNN example: apache#6486
  • Fixed various bugs in SSD example.
  • Fixed out argument not working for zeros, ones, full, etc.
  • expand_dims now supports backward shape inference.
  • Fixed a bug in rnn. BucketingSentenceIter that causes incorrect layout handling on multi-GPU.
  • Fixed context mismatch when loading optimizer states.
  • Fixed a bug in ReLU activation when using MKL.
  • Fixed a few race conditions that causes crashes on shutdown.

Refactors

  • Refactored TShape/TBlob to use int64 dimensions and DLTensor as internal storage. Getting ready for migration to DLPack. As a result TBlob::dev_mask_ and TBlob::stride_ are removed.

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