NEWS.md

MXNet Change Log

1.2.1

Deprecations

The usage of save_params described in the gluon book did not reflect the intended usage of the API and led MXNet users to depend on the unintended usage of save_params and load_params. In 1.2.0 release an internal bug fix was made which broke the unintended usage use case and users scripts. To correct the API change, the behavior of save_params API has been reverted to the behavior of MXNet v1.1.0 in v1.2.1. The intended and correct use are now supported with the new APIs save_parameters and load_parameters. With v1.2.1, usage of save_params and load_params APIs will resume their former functionality and a deprecation warning will appear. All scripts to save and load parameters for a Gluon model should use the new APIs: save_parameters and load_parameters. If your model is hybridizable and you want to export a serialized structure of the model as well as parameters you should migrate your code to use export API and the newly added imports API instead of save_params and load_params API. Please refer to the Saving and Loading Gluon Models Tutorial for more information.

User Code Changes

• If you have been using the save_params and load_params API, below are the recommendations on how to update your code:

1. If you save parameters to load it back into a SymbolBlock, it is strongly recommended to use export and imports API instead. For more information, please see the Saving and Loading Gluon Models Tutorial.
2. If you created gluon layers without a name_scope using MXNet 1.2.0, you must replace save_params with save_parameters. Otherwise, your models saved in 1.2.1 will fail to load back, although this worked in 1.2.0.
3. For the other use cases, such as models created within a name_scope (inside a with name_scope() block) or models being loaded back into gluon and not SymbolBlock, we strongly recommend replacing save_params and load_params with save_parameters and load_parameters. Having said that, your code won't break in 1.2.1 but will give you a deprecated warning message for save_params and load_params.

Incompatible API Changes

• We are breaking semantic versioning by making a backwards incompatible change from 1.2.0 in the 1.2.1 patch release. The breaking use case is documented in point 2 above. The reason for doing this is because the 1.2.0 release broke a documented use case from the gluon book and this release reverts the breakage.
• We did break the promise with semantic versioning due to the API behavior change in 1.2.0 and the backward incompatible change between 1.2.0 and 1.2.1 patch release. The breaking use case is documented in point 2 above. The reason for doing this is because the 1.2.0 release broke a documented use case from the gluon book and this release reverts the breakage. As a community, we apologize for the inconvenience caused and will continue to strive to uphold semantic versioning.

Bug Fixes

• Fixed MKLDNN bugs (#10613, #10021, #10616, #10764, #10591, #10731, #10918, #10706, #10651, #10979).
• Fixed Scala Inference Memory leak (#11216).
• Fixed Cross Compilation for armv7 (#11054).

Performance Improvements

• Reduced memory consumption from inplace operation for ReLU activation (#10847).
• Improved slice operator performance by 20x (#11124).
• Improved performance of depthwise convolution by using cudnnv7 if available (#11076).
• Improved performance and memory usage of Conv1D, by adding back cuDNN support for Conv1D (#11270). This adds a known issue: The cuDNN convolution operator may throw CUDNN_STATUS_EXECUTION_FAILED when req == "add" and cudnn_tune != off with large inputs(e.g. 64k channels). If you encounter this issue, please consider setting MXNET_CUDNN_AUTOTUNE_DEFAULT to 0.

1.2.0

New Features - Added Scala Inference APIs

• Implemented new Scala Inference APIs which offer an easy-to-use, Scala Idiomatic and thread-safe high level APIs for performing predictions with deep learning models trained with MXNet (#9678). Implemented a new ImageClassifier class which provides APIs for classification tasks on a Java BufferedImage using a pre-trained model you provide (#10054). Implemented a new ObjectDetector class which provides APIs for object and boundary detections on a Java BufferedImage using a pre-trained model you provide (#10229).

New Features - Added a Module to Import ONNX models into MXNet

• Implemented a new ONNX module in MXNet which offers an easy to use API to import ONNX models into MXNet's symbolic interface (#9963). Checkout the example on how you could use this API to import ONNX models and perform inference on MXNet. Currently, the ONNX-MXNet Import module is still experimental. Please use it with caution.

New Features - Added Support for Model Quantization with Calibration

• Implemented model quantization by adopting the TensorFlow approach with calibration by borrowing the idea from Nvidia's TensorRT. The focus of this work is on keeping quantized models (ConvNets for now) inference accuracy loss under control when compared to their corresponding FP32 models. Please see the example on how to quantize a FP32 model with or without calibration (#9552).

New Features - MKL-DNN Integration

• MXNet now integrates with Intel MKL-DNN to accelerate neural network operators: Convolution, Deconvolution, FullyConnected, Pooling, Batch Normalization, Activation, LRN, Softmax, as well as some common operators: sum and concat (#9677). This integration allows NDArray to contain data with MKL-DNN layouts and reduces data layout conversion to get the maximal performance from MKL-DNN. Currently, the MKL-DNN integration is still experimental. Please use it with caution.

New Features - Added Exception Handling Support for Operators

• Implemented Exception Handling Support for Operators in MXNet. MXNet now transports backend C++ exceptions to the different language front-ends and prevents crashes when exceptions are thrown during operator execution (#9681).

New Features - Enhanced FP16 support

• Added support for distributed mixed precision training with FP16. It supports storing of master copy of weights in float32 with the multi_precision mode of optimizers (#10183). Improved speed of float16 operations on x86 CPU by 8 times through F16C instruction set. Added support for more operators to work with FP16 inputs (#10125, #10078, #10169). Added a tutorial on using mixed precision with FP16 (#10391).

New Features - Added Profiling Enhancements

• Enhanced built-in profiler to support native Intel:registered: VTune:tm: Amplifier objects such as Task, Frame, Event, Counter and Marker from both C++ and Python -- which is also visible in the Chrome tracing view(#8972). Added Runtime tracking of symbolic and imperative operators as well as memory and API calls. Added Tracking and dumping of aggregate profiling data. Profiler also no longer affects runtime performance when not in use.

Breaking Changes

• Changed Namespace for MXNet scala from ml.dmlc.mxnet to org.apache.mxnet (#10284).
• Changed API for the Pooling operator from mxnet.symbol.Pooling(data=None, global_pool=_Null, cudnn_off=_Null, kernel=_Null, pool_type=_Null, pooling_convention=_Null, stride=_Null, pad=_Null, name=None, attr=None, out=None, **kwargs) to mxnet.symbol.Pooling(data=None, kernel=_Null, pool_type=_Null, global_pool=_Null, cudnn_off=_Null, pooling_convention=_Null, stride=_Null, pad=_Null, name=None, attr=None, out=None, **kwargs). This is a breaking change when kwargs are not provided since the new api expects the arguments starting from global_pool at the fourth position instead of the second position. (#10000).

Bug Fixes

• Fixed tests - Flakiness/Bugs - (#9598, #9951, #10259, #10197, #10136, #10422). Please see: Tests Improvement Project
• Fixed cudnn_conv and cudnn_deconv deadlock (#10392).
• Fixed a race condition in io.LibSVMIter when batch size is large (#10124).
• Fixed a race condition in converting data layouts in MKL-DNN (#9862).
• Fixed MKL-DNN sigmoid/softrelu issue (#10336).
• Fixed incorrect indices generated by device row sparse pull (#9887).
• Fixed cast storage support for same stypes (#10400).
• Fixed uncaught exception for bucketing module when symbol name not specified (#10094).
• Fixed regression output layers (#9848).
• Fixed crash with mx.nd.ones (#10014).
• Fixed sample_multinomial crash when get_prob=True (#10413).
• Fixed buggy type inference in correlation (#10135).
• Fixed race condition for CPUSharedStorageManager->Free and launched workers at iter init stage to avoid frequent relaunch (#10096).
• Fixed DLTensor Conversion for int64 (#10083).
• Fixed issues where hex symbols of the profiler were not being recognized by chrome tracing tool(#9932)
• Fixed crash when profiler was not enabled (#10306)
• Fixed ndarray assignment issues (#10022, #9981, #10468).
• Fixed incorrect indices generated by device row sparse pull (#9887).
• Fixed print_summary bug in visualization module (#9492).
• Fixed shape mismatch in accuracy metrics (#10446).
• Fixed random samplers from uniform and random distributions in R bindings (#10450).
• Fixed a bug that was causing training metrics to be printed as NaN sometimes (#10437).
• Fixed a crash with non positive reps for tile ops (#10417).

Performance Improvements

• On average, after the MKL-DNN change, the inference speed of MXNet + MKLDNN outperforms MXNet + OpenBLAS by a factor of 32, outperforms MXNet + MKLML by 82% and outperforms MXNet + MKLML with the experimental flag by 8%. The experiments were run for the image classifcation example, for different networks and different batch sizes.
• Improved sparse SGD, sparse AdaGrad and sparse Adam optimizer speed on GPU by 30x (#9561, #10312, #10293, #10062).
• Improved sparse.retain performance on CPU by 2.5x (#9722)
• Replaced std::swap_ranges with memcpy (#10351)
• Implemented DepthwiseConv2dBackwardFilterKernel which is over 5x faster (#10098)
• Implemented CPU LSTM Inference (#9977)
• Added Layer Normalization in C++ (#10029)
• Optimized Performance for rtc (#10018)
• Improved CPU performance of ROIpooling operator by using OpenMP (#9958)
• Accelerated the calculation of F1 (#9833)

API Changes

• Block.save_params now match parameters according to model structure instead of names to avoid prefix mismatching problems during saving and loading (#10511).
• Added an optional argument ctx to mx.random.seed. Seeding with ctx option produces random number sequence independent of device id. (#10367).
• Added copy flag for astype (#10347).
• Added context parameter to Scala Infer API - ImageClassifier and ObjectDetector (#10252).
• Added axes support for dropout in gluon (#10032).
• Added default ctx to cpu for gluon.Block.load_params (#10160).
• Added support for variable sequence length in gluon.RecurrentCell (#9934).
• Added convenience fluent method for squeeze op (#9734).
• Made array.reshape compatible with numpy (#9790).
• Added axis support and gradient for L2norm (#9740).

Sparse Support

• Added support for multi-GPU training with row_sparse weights using device KVStore (#9987).
• Added Module.prepare API for multi-GPU and multi-machine training with row_sparse weight (#10285).
• Added deterministic option for contrib.SparseEmbedding operator (#9846).
• Added sparse.broadcast_mul and sparse.broadcast_div with CSRNDArray and 1-D dense NDArray on CPU (#10208).
• Added sparse support for Custom Operator (#10374).
• Added Sparse feature for Perl (#9988).
• Added force_deterministic option for sparse embedding (#9882).
• Added sparse.where with condition being csr ndarray (#9481).

Deprecations

• Deprecated profiler_set_state (#10156).

Other Features

• Added constant parameter for gluon (#9893).
• Added contrib.rand.zipfian (#9747).
• Added Gluon PreLU, ELU, SELU, Swish activation layers for Gluon (#9662)
• Added Squeeze Op (#9700).
• Added multi-proposal operator (CPU version) and fixed bug in multi-proposal operator (GPU version) (#9939).
• Added in Large-Batch SGD with a warmup, and a LARS startegy (#8918).
• Added Language Modelling datasets and Sampler (#9514).
• Added instance norm and reflection padding to Gluon (#7938).
• Added micro-averaging strategy for F1 metric (#9777).
• Added Softsign Activation Function (#9851).
• Added eye operator, for default storage type (#9770).
• Added TVM bridge support to JIT NDArray Function by TVM (#9880).
• Added float16 support for correlation operator and L2Normalization operator (#10125, #10078).
• Added random shuffle implementation for NDArray (#10048).
• Added load from buffer functions for CPP package (#10261).

Usability Improvements

• Added embedding learning example for Gluon (#9165).
• Added tutorial on how to use data augmenters (#10055).
• Added tutorial for Data Augmentation with Masks (#10178).
• Added LSTNet example (#9512).
• Added MobileNetV2 example (#9614).
• Added tutorial for Gluon Datasets and DataLoaders (#10251).
• Added Language model with Google's billion words dataset (#10025).
• Added example for custom operator using RTC (#9870).
• Improved image classification examples (#9799, #9633).
• Added reshape predictor function to c_predict_api (#9984).
• Added guide for implementing sparse ops (#10081).
• Added naming tutorial for gluon blocks and parameters (#10511).

Known Issues

• MXNet crash when built with USE_GPERFTOOLS = 1 (#8968).
• DevGuide.md in the 3rdparty submodule googletest licensed under CC-BY-2.5.
• Incompatibility in the behavior of MXNet Convolution operator for certain unsupported use cases: Raises an exception when MKLDNN is enabled, fails silently when it is not.
• MXNet convolution generates wrong results for 1-element strides (#10689).
• Tutorial on fine-tuning an ONNX model fails when using cpu context.
• Example in Scala Inference API will fail to download models if data.mxnet.io is down. Please see this PR (#10617) for a workaround, if you face the issue.

For more information and examples, see full release notes

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 brake 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

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

0.12.1

Bug-fixes

• Added GPU support for the syevd operator which ensures that there is GPU support for all linalg-operators.
• Bugfix for syevd on CPU such that it works for float32.
• Fixed API call when OMP_NUM_THREADS environment variable is set.
• Fixed MakeNonlossGradNode bug.
• Fixed bug related to passing dtype to array().
• Fixed some minor bugs for sparse distributed training.
• Fixed a bug on Slice accessing uninitialized memory in param.begin in the file matrix_op-inl.h.
• Fixed gluon.data.RecordFileDataset.
• Fixed a bug that caused autograd to crash on some networks.

0.12.0

Performance

• Added full support for NVIDIA Volta GPU Architecture and CUDA 9. Training CNNs is up to 3.5x faster than Pascal when using float16 precision.
• 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.
• Improved ImageRecordIO image loading performance and added indexed RecordIO support.
• Added better openmp thread management to improve CPU performance.

New Features - Gluon

• Added enhancements to the Gluon package, a high-level interface designed to be easy to use while keeping most of the flexibility of low level API. Gluon supports both imperative and symbolic programming, making it easy to train complex models imperatively with minimal impact on performance. Neural networks (and other machine learning models) can be defined and trained with gluon.nn and gluon.rnn packages.
• Added new loss functions - SigmoidBinaryCrossEntropyLoss, CTCLoss, HuberLoss, HingeLoss, SquaredHingeLoss, LogisticLoss, TripletLoss.
• gluon.Trainer now allows reading and setting learning rate with trainer.learning_rate property.
• Added API HybridBlock.export for exporting gluon models to MXNet format.
• Added gluon.contrib package.
  • Convolutional recurrent network cells for RNN, LSTM and GRU.
  • VariationalDropoutCell

New Features - Autograd

• Added enhancements to autograd package, which enables automatic differentiation of NDArray operations.
• autograd.Function allows defining both forward and backward computation for custom operators.
• Added mx.autograd.grad and experimental second order gradient support (most operators don't support second order gradient yet).
• Autograd now supports cross-device graphs. Use x.copyto(mx.gpu(i)) and x.copyto(mx.cpu()) to do computation on multiple devices.

New Features - Sparse Tensor Support

• Added support for sparse matrices.
• Added limited cpu support for two sparse formats in Symbol and NDArray - CSRNDArray and RowSparseNDArray.
• Added a sparse dot product operator and many element-wise sparse operators.
• Added a data iterator for sparse data input - LibSVMIter.
• Added three optimizers for sparse gradient updates: Ftrl, SGD and Adam.
• Added push and row_sparse_pull with RowSparseNDArray in distributed kvstore.

Other New Features

• Added limited support for fancy indexing, which allows you to very quickly access and modify complicated subsets of an array's values. x[idx_arr0, idx_arr1, ..., idx_arrn] is now supported. Features such as combining and slicing are planned for the next release. Checkout master to get a preview.
• Random number generators in mx.nd.random.* and mx.sym.random.* now support 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.
• Better support for IDE auto-completion. IDEs like PyCharm can now correctly parse mxnet operators.

API Changes

• Operators like mx.sym.linalg_* and mx.sym.random_* are now moved to mx.sym.linalg.* and mx.sym.random.*. The old names are still available but deprecated.
• sample_* and random_* are now merged as random.*, which supports both scalar and NDArray distribution parameters.

Bug-fixes

• Fixed a bug that causes argsort operator to fail on large tensors.
• Fixed numerical stability issues when summing large tensors.
• Fixed a bug that causes arange operator to output wrong results for large ranges.
• Improved numerical precision for unary and binary operators on float64 inputs.

For more information and examples, see full release notes

0.11.0

Major Features

• Apple Core ML model converter
• Support for Keras v1.2.2
• For more information see full release notes

API Changes

• Added CachedOp. You can now cache the operators that’s called frequently with the same set of arguments to reduce overhead.
• Added sample_multinomial for sampling from multinomial distributions.
• Added trunc operator for rounding towards zero.
• Added linalg_gemm, linalg_potrf, ... operators for lapack support.
• Added verbose option to Initializer for printing out initialization details.
• Added DeformableConvolution to contrib from the Deformable Convolutional Networks paper.
• Added float64 support for dot and batch_dot operator.
• allow_extra is added to Module.set_params to ignore extra parameters.
• Added mod operator for modulo.
• Added multi_precision option to SGD optimizer to improve training with float16. Resnet50 now achieves the same accuracy when trained with float16 and gives 50% speedup on Titan XP.

Performance Improvements

• ImageRecordIter now stores data in pinned memory to improve GPU memcopy speed.

Bugfixes

• Cython interface is fixed. make cython and python setup.py install --with-cython should install the cython interface and reduce overhead in applications that use imperative/bucketing.
• Fixed various bugs in Faster-RCNN example: apache#6486
• Fixed various bugs in SSD example.
• Fixed out argument not working for zeros, ones, full, etc.
• expand_dims now supports backward shape inference.
• Fixed a bug in rnn. BucketingSentenceIter that causes incorrect layout handling on multi-GPU.
• Fixed context mismatch when loading optimizer states.
• Fixed a bug in ReLU activation when using MKL.
• Fixed a few race conditions that causes crashes on shutdown.

Refactors

• Refactored TShape/TBlob to use int64 dimensions and DLTensor as internal storage. Getting ready for migration to DLPack. As a result TBlob::dev_mask_ and TBlob::stride_ are removed.

0.10.0

• Overhauled documentation for commonly used Python APIs, Installation instructions, Tutorials, HowTos and MXNet Architecture.
• Updated mxnet.io for improved readability.
• Pad operator now support reflection padding.
• Fixed a memory corruption error in threadedengine.
• Added CTC loss layer to contrib package. See mx.contrib.sym.ctc_loss.
• Added new sampling operators for several distributions (normal,uniform,gamma,exponential,negative binomial).
• Added documentation for experimental RNN APIs.

0.9.3

• Move symbolic API to NNVM @tqchen
  • Most front-end C API are backward compatible
  • Removed symbolic API in MXNet and relies on NNVM
• New features:
  • MXNet profiler for profiling operator-level executions
  • mxnet.image package for fast image loading and processing
• Change of JSON format
  • param and attr field are merged to attr
  • New code is backward-compatible can load old json format
• OpProperty registration now is deprecated
  • New operators are encouraged to register their property to NNVM op registry attribute
• Known features removed limitations to be fixed
  • Bulk segment execution not yet added.

v0.8

This is the last release before the NNVM refactor.

• CaffeOp and CaffeIter for interfacing with Caffe by @HrWangChengdu @cjolivier01
• WrapCTC plugin for sequence learning by @xlvector
• Improved Multi-GPU performance by @mli
• CuDNN RNN support by @sbodenstein
• OpenCV plugin for parallel image IO by @piiswrong
• More operators as simple op
  • Simple OP @tqchen
  • element wise op with axis and broadcast @mli @sxjscience
• Cudnn auto tuning for faster convolution by @piiswrong
• More applications
  • Faster RCNN by @precedenceguo

v0.7

• 0.6 is skipped because there are a lot of improvements since initial release
• More math operators
  • elementwise ops and binary ops
• Attribute support in computation graph
  • Now user can use attributes to give various hints about specific learning rate, allocation plans etc
• MXNet is more memory efficient
  • Support user defined memory optimization with attributes
• Support mobile applications by @antinucleon
• Refreshed update of new documents
• Model parallel training of LSTM by @tqchen
• Simple operator refactor by @tqchen
  • add operator_util.h to enable quick registration of both ndarray and symbolic ops
• Distributed training by @mli
• Support Torch Module by @piiswrong
  • MXNet now can use any of the modules from Torch.
• Support custom native operator by @piiswrong
• Support data types including fp16, fp32, fp64, int32, and uint8 by @piiswrong
• Support monitor for easy printing and debugging by @piiswrong
• Support new module API by @pluskid
  • Module API is a middle level API that can be used in imperative manner like Torch-Module
• Support bucketing API for variable length input by @pluskid
• Support CuDNN v5 by @antinucleon
• More applications
  • Speech recognition by @yzhang87
  • Neural art by @antinucleon
  • Detection, RCNN bt @precedenceguo
  • Segmentation, FCN by @tornadomeet
  • Face identification by @tornadomeet
  • More on the example

v0.5 (initial release)

• All basic modules ready