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MXNet 1.1.0

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@yzhliu yzhliu released this 19 Feb 23:10
· 2 commits to v1.1.0 since this release

MXNet Change Log


Usability Improvements

  • Improved the usability of examples and tutorials


  • Fixed I/O multiprocessing for too many open file handles (#8904), race condition (#8995), deadlock (#9126).
  • Fixed image IO integration with OpenCV 3.3 (#8757).
  • Fixed Gluon block printing (#8956).
  • Fixed float16 argmax when there is negative input. (#9149)
  • Fixed random number generator to ensure sufficient randomness. (#9119, #9256, #9300)
  • Fixed custom op multi-GPU scaling (#9283)
  • Fixed gradient of gather_nd when duplicate entries exist in index. (#9200)
  • Fixed overriden contexts in Module group2ctx option when using multiple contexts (#8867)
  • Fixed swap_axes operator with "add_to" gradient req (#9541)

New Features

  • Added experimental API in contrib.text for building vocabulary, and loading pre-trained word embeddings, with built-in support for 307 GloVe and FastText pre-trained embeddings. (#8763)
  • Added experimental structural blocks in gluon.contrib: Concurrent, HybridConcurrent, Identity. (#9427)
  • Added, csr) operator (#8938)
  • Added Khatri-Rao operator (#7781)
  • Added FTML and Signum optimizer (#9220, #9262)
  • Added ENABLE_CUDA_RTC build option (#9428)

API Changes

  • Added zero gradients to rounding operators including rint, ceil, floor, trunc, and fix (#9040)
  • Added use_global_stats in nn.BatchNorm (#9420)
  • Added axis argument to SequenceLast, SequenceMask and SequenceReverse operators (#9306)
  • Added lazy_update option for standard SGD & Adam optimizer with row_sparse gradients (#9468, #9189)
  • Added select option in Block.collect_params to support regex (#9348)
  • Added support for (one-to-one and sequence-to-one) inference on explicit unrolled RNN models in R (#9022)


  • The Scala API name space is still called ml.dmlc. The name space is likely be changed in a future release to org.apache and might break existing applications and scripts (#9579, #9324)

Performance Improvements

  • Improved GPU inference speed by 20% when batch size is 1 (#9055)
  • Improved SequenceLast operator speed (#9306)
  • Added multithreading for the class of broadcast_reduce operators on CPU (#9444)
  • Improved batching for GEMM/TRSM operators with large matrices on GPU (#8846)

Known Issues

  • "Predict with pre-trained models" tutorial is broken
  • "example/numpy-ops/" is broken

For more information and examples, see full release notes