MXNet Change Log

1.0.0

Performance

• Enhanced the performance of sparse.dot operator.
• MXNet now automatically set OpenMP to use all available CPU cores to maximize CPU utilization when NUM_OMP_THREADS is not set.
• Unary and binary operators now avoid using OpenMP on small arrays if using OpenMP actually hurts performance due to multithreading overhead.
• Significantly improved performance of broadcast_add, broadcast_mul, etc on CPU.
• Added bulk execution to imperative mode. You can control segment size with mxnet.engine.bulk. As a result, the speed of Gluon in hybrid mode is improved, especially on small networks and multiple GPUs.
• Improved speed for ctypes invocation from Python frontend.

New Features - Gradient Compression [Experimental]

• Speed up multi-GPU and distributed training by compressing communication of gradients. This is especially effective when training networks with large fully-connected layers. In Gluon this can be activated with compression_params in Trainer.

New Features - Support of NVIDIA Collective Communication Library (NCCL) [Experimental]

• Use kvstore=’nccl’ for (in some cases) faster training on multiple GPUs.
• Significantly faster than kvstore=’device’ when batch size is small.
• It is recommended to set environment variable NCCL_LAUNCH_MODE to PARALLEL when using NCCL version 2.1 or newer.

New Features - Advanced Indexing [General Availability]

• NDArray now supports advanced indexing (both slice and assign) as specified by the numpy standard: https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html#combining-advanced-and-basic-indexing with the following restrictions:
  • if key is a list type, only a list of integers is supported, e.g. key=[1, 2] is supported, while not for key=[[1, 2]].
  • Ellipsis (...) and np.newaxis are not supported.
  • Boolean array indexing is not supported.

New Features - Gluon [General Availability]

• Performance optimizations discussed above.
• Added support for loading data in parallel with multiple processes to gluon.data.DataLoader. The number of workers can be set with num_worker. Does not support windows yet.
• Added Block.cast to support networks with different data types, e.g. float16.
• Added Lambda block for wrapping a user defined function as a block.
• Generalized gluon.data.ArrayDataset to support arbitrary number of arrays.

New Features - ARM / Raspberry Pi support [Experimental]

• MXNet now compiles and runs on ARMv6, ARMv7, ARMv64 including Raspberry Pi devices. See https://github.com/apache/incubator-mxnet/tree/master/docker_multiarch for more information.

New Features - NVIDIA Jetson support [Experimental]

• MXNet now compiles and runs on NVIDIA Jetson TX2 boards with GPU acceleration.
• You can install the python MXNet package on a Jetson board by running - $ pip install mxnet-jetson-tx2.

New Features - Sparse Tensor Support [General Availability]

• Added more sparse operators: contrib.SparseEmbedding, sparse.sum and sparse.mean.
• Added asscipy() for easier conversion to scipy.
• Added check_format() for sparse ndarrays to check if the array format is valid.

Bug-fixes

• Fixed a[-1] indexing doesn't work on NDArray.
• Fixed expand_dims if axis < 0.
• Fixed a bug that causes topk to produce incorrect result on large arrays.
• Improved numerical precision of unary and binary operators for float64 data.
• Fixed derivatives of log2 and log10. They used to be the same with log.
• Fixed a bug that causes MXNet to hang after fork. Note that you still cannot use GPU in child processes after fork due to limitations of CUDA.
• Fixed a bug that causes CustomOp to fail when using auxiliary states.
• Fixed a security bug that is causing MXNet to listen on all available interfaces when running training in distributed mode.

Doc Updates

• Added a security best practices document under FAQ section.
• Fixed License Headers including restoring copyright attributions.
• Documentation updates.
• Links for viewing source.

For more information and examples, see full release notes