/incubator-mxnet

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MXNet 1.0.0

@cjolivier01 cjolivier01 released this Dec 4, 2017 · 17 commits to v1.0.0 since this release

Assets

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]

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]

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

Pre-release
This commit was created on GitHub.com and signed with a verified signature using GitHub’s key.
GPG key ID: 4AEE18F83AFDEB23 Learn about signing commits

MXNet 1.0.0.rc1

@cjolivier01 cjolivier01 released this Nov 30, 2017 · 17 commits to v1.0.0 since this release

Assets

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]

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]

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

Pre-release

MXNet 1.0.0.rc0

@cjolivier01 cjolivier01 released this Nov 24, 2017 · 21 commits to v1.0.0 since this release

Assets

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]

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]

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

MXNet 0.12.1

@cjolivier01 cjolivier01 released this Nov 15, 2017

Assets

MXNet Change Log

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.
Pre-release

MXNet 0.12.1.rc0

@cjolivier01 cjolivier01 released this Nov 8, 2017

Assets

MXNet Change Log

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.

MXNet 0.12.0

@sandeep-krishnamurthy sandeep-krishnamurthy released this Oct 30, 2017 · 14 commits to v0.12.0 since this release

Assets

MXNet Change Log

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

Pre-release

MXNet 0.12.0 Release Candidate 0

@cjolivier01 cjolivier01 released this Oct 20, 2017 · 14 commits to v0.12.0 since this release

Assets

MXNet Change Log

0.12.0

Performance

  • Added full support for NVIDIA Volta GPU Architecture and CUDA 9. Training is up to 3.5x faster than Pascal when using float16.
  • 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

MXNet 0.11.0

@nswamy nswamy released this Sep 5, 2017 · 671 commits to master since this release

Assets

0.11.0

Major Features

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

  • Fixed a bug in Adam that causes weight decay to be handled incorrectly. If you are using Adam, you may need to tune learning rate a little to get the same performance as previous versions.
  • Remove WaitToRead in dist-kvstore: Improves performance 20-30% for distributed training.
  • 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: dmlc#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.
  • Fixed image-classification example code.

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.

Known Issues

  • Inception-V3 model can be converted into CoreML format but is unable to run on Xcode.
Pre-release

MXNet 0.11.0 Release Candidate 3

@nswamy nswamy released this Aug 30, 2017 · 3 commits to v0.11.0 since this release

Assets

0.11.0.rc3

Major Features

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

  • Fixed a bug in Adam that causes weight decay to be handled incorrectly. If you are using Adam, you may need to tune learning rate a little to get the same performance as previous versions.
  • Remove WaitToRead in dist-kvstore: Improves performance 20-30% for distributed training.
  • 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: dmlc#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.
  • Fixed image-classification example code.

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.

Known Issues

  • Inception-V3 model can be converted into CoreML format but is unable to run on Xcode.
Pre-release

MXNet 0.11.0 Release Candidate 2

@nswamy nswamy released this Aug 16, 2017 · 9 commits to v0.11.0 since this release

Assets

0.11.0.rc2

Major Features

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

  • Remove WaitToRead in dist-kvstore: Improves performance 20-30%
  • 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: dmlc#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.

Known Issues

  • Inception-V3 model can be converted into CoreML format but is unable to run on Xcode.