NervanaSystems Neon Versions Save

Intel® Nervana™ reference deep learning framework committed to best performance on all hardware

v1.8.0

7 years ago
  • Skip Thought Vectors (http://arxiv.org/abs/1506.06726) example
  • Dilated convolution support
  • Nesterov Accelerated Gradient option to SGD optimizer
  • MultiMetric class to allow wrapping Metric classes
  • Support for serializing and deserializing encoder-decoder models
  • Allow specifying the number of time steps to evaluate during beam search
  • A new community-contributed Docker image
  • Improved error messages when a tensor is created with an invalid shape or reshaped to an incompatible size
  • Fix bugs in MultiCost support
  • Documentation fixes [#331]

v1.7.0

7 years ago
  • Update Data Loader to aeon https://github.com/NervanaSystems/aeon for flexible, multi-threaded data loading and transformations
  • Add Neural Machine Translation model
  • Remove Fast RCNN model (use Faster RCNN model instead)
  • Remove music_genres example
  • Fix super blocking for small N with 1D conv
  • Fix update-direct conv kernel for small N
  • Add gradient clipping to Adam optimizer
  • Documentation updates and bug fixes

v1.6.0

7 years ago
  • Faster RCNN model
  • Sequence to Sequence container and char_rae recurrent autoencoder model
  • Reshape Layer that reshapes the input [#221]
  • Pip requirements in requirements.txt updated to latest versions [#289]
  • Remove deprecated data loaders and update docs
  • Use NEON_DATA_CACHE_DIR envvar as archive dir to store DataLoader ingested data
  • Eliminate type conversion for FP16 for CUDA compute capability >= 5.2
  • Use GEMV kernels for batch size 1
  • Alter delta buffers for nesting of merge-broadcast layers
  • Support for ncloud real-time logging
  • Add fast_style Makefile target
  • Fix Python 3 builds on Ubuntu 16.04
  • Run setup.py for sysinstall to generate version.py [#282]
  • Fix broken link in mnist docs
  • Fix conv/deconv tests for CPU execution and fix i32 data type
  • Fix for average pooling with batch size 1
  • Change default scale_min to allow random cropping if omitted
  • Fix yaml loading
  • Fix bug with image resize during injest
  • Update references to the ModelZoo and neon examples to their new locations

v1.5.4

7 years ago
  • Python2/Python3 compatibility [#191]
  • Support for Pascal GPUs
  • Persistent RNN kernels [#262]
  • Implement Binarized Neural Networks from http://arxiv.org/pdf/1602.02830v3.pdf (added in v1.5.4)
  • Dataloader enhancements (audio loader with examples)
  • HDF5 file data iterator
  • Convolution kernel improvements
  • API documentation improvements [#234, #244, #263]
  • Cache directory cleanup
  • Reorganization of all unit tests
  • Bug fixes [#182, #183, #231, #241, #252, #253, #257, #259, #267, #268]

v1.5.3

7 years ago
  • Python2/Python3 compatibility [#191]
  • Support for Pascal GPUs
  • Persistent RNN kernels [#262]
  • Dataloader enhancements (audio loader with examples)
  • HDF5 file data iterator
  • Convolution kernel improvements
  • API documentation improvements [#234, #244, #263]
  • Cache directory cleanup
  • Reorganization of all unit tests
  • Bug fixes [#182, #183, #231, #241, #252, #253, #257, #259, #267]

v1.5.2

7 years ago
  • Python2/Python3 compatibility [#191]
  • Support for Pascal GPUs
  • Persistent RNN kernels [#262]
  • Dataloader enhancements (audio loader with examples)
  • HDF5 file data iterator
  • Convolution kernel improvements
  • API documentation improvements [#234, #244, #263]
  • Cache directory cleanup
  • Reorganization of all unit tests
  • Bug fixes [#182, #183, #231, #241, #252, #253, #257, #259]

v1.5.1

7 years ago
  • Python2/Python3 compatibility [#191]
  • Support for Pascal GPUs
  • Persistent RNN kernels [#262]
  • Dataloader enhancements (audio loader with examples)
  • HDF5 file data iterator
  • Convolution kernel improvements
  • API documentation improvements [#234, #244, #263]
  • Cache directory cleanup
  • Reorganization of all unit tests
  • Bug fixes [#182, #183, #231, #241, #252, #253, #257, #259]

v1.4.0

8 years ago
  • VGG16 based Fast R-CNN model using winograd kernels
  • new, backward compatible, generic data loader
  • C3D video loader model trained on UCF101 dataset
  • Deep Dream example
  • make conv layer printout more informative [#222]
  • fix some examples to use new arg override capability
  • improve performance for relu for small N
  • better support for arbitrary batch norm layer placement
  • documentation updates [#210, #213, #236]

v1.3.0

8 years ago
  • winograd kernels and associated autotuning routines
  • benchmarking scripts
  • deprecation of deterministic argument for backend constructor
  • improve batch norm stability with fp16 backend
  • allow strided support for dimshuffle kernel
  • speed up zero momentum gradient descent

v1.2.2

8 years ago
  • benchmarking enhancements
  • fast dimshuffle, transpose, other kernel speedups and refactoring
  • batch norm states fix, deterministic updates
  • example fixes for fast rcnn and conv_autoencoder
  • image decoding rescaling method fix
  • deserialization fixes for RNN's, refactoring
  • caffe compatibility fixes
  • documentation updates