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- Added comprehensive support for sparse matrices. See help on `mx.sym.sparse` and `mx.nd.sparse` for more info.
- 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.
New Features - Autograd and Gluon
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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.
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