MXNet Change Log

1.1.0

Usability Improvements

• Improved the usability of examples and tutorials

Bug-fixes

• 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 sparse.dot(dense, 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)

Deprecations

• 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/ndarray_softmax.py" is broken

For more information and examples, see full release notes