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Release MXNet 1.1.0 · apache/mxnet · GitHub
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
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