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@cjolivier01 cjolivier01 released this Dec 4, 2017 · 19 commits to v1.0.0 since this release

MXNet Change Log



  • Enhanced the performance of 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]

New Features - Gluon [General Availability]

  • Performance optimizations discussed above.
  • Added support for loading data in parallel with multiple processes to 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 to support arbitrary number of arrays.

New Features - ARM / Raspberry Pi support [Experimental]

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.


  • 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

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