1.0.0-beta6

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EN 1.0.0-M2.1
1.0.0-beta6
Highlights - 1.0.0-beta6 Release
Added support for CUDA 10.2. 1.0.0-beta6 released with CUDA 9.2, 10.0, 10.1 and 10.2 support
SameDiff optimizations - memory use for inference and training significantly reduced, with some performance improvements also
Deeplearning4j UI - Play framework replaced with Vertx; deeplearning4j-ui dependency now no longer has Scala dependency or Scala version suffix Link​
Note: No API changes, only artifact ID change: replace deeplearning4j-ui_2.1x with deeplearning4j-ui
ND4j namespace operation methods: operations are available through the Nd4j.math, Nd4j.random, Nd4j.bitwise, Nd4j.nn (neural network), for example Nd4j.math.abs(INDArray), Nd4j.random.logNormal etc Link.
Note that additional ND4J namespaces API will have additions (new namespaces and methods), and may have some API changes, in the next release
OpenMP replaced with thread pool c++ parallelism framework; enabled c++ parallelism for platforms without C++-level threading for operations
Deeplearning4J
Deeplearning4J: Features and Enhancements
DNNL (MKL-DNN) upgraded to version 1.1
Added causal convolution mode for Convolution1D layer (ConvolutionMode.Causal) and added causal conv1d support for Keras import Link​
Keras import now supports scaled identity weight initialization Link​
Added Mish activation function Link, Link​
BertIterator now has a BertIterator.featurizeSentences(List<String>) method for inference Link, Link​
BertIterator now supports sentence pairs for supervised training Link​
Added Sparse multi-class cross entropy for both Deeplearning4j and Keras import Link, Link​
Deeplearning4j UI: migrated from Play to Vertx for web serving backend, also removing dependency on Scala libraries; no API changes, only artifact ID change - replace deeplearning4j-ui_2.1x with deeplearning4j-ui Link, Link​
Added TimeDistributed wrapper layer Link​
Deeplearning4J: Bug Fixes and Optimizations
KDTree implementation optimized Link​
Deeplearning4j zoo models and datasets hosting location updated Link​
Fixed nIn validation for Deconv2D layer Link​
Fixed an issue with incorrect Deconvolution2d results for Keras import models Link​
Added DNNL/MKLDNN support for batch normalization layer Link, Link​
Fixed various integer casts to avoid overflows for very large arrays (with dimensions or length > Integer.MAX_VALUE) Link​
Fixed an issue with UNet non-pretrained model architecture (last layer kernel size) Link​
Deeplearning4j SameDiff layers now use DL4J workspaces for better performance and reduced memory consumption Link​
Updated broken links in afew error messages Link​
Cleaned up a few unused dependencies in various modules Link​
Cleaned up duplicate SamplingDataSetIterator class Link​
Fixed an issue where ComputationGraph instances with a single input going into multiple embedding layers could throw a NPE Link​
Fixed an issue where loss function weights were not automatically cast to network datatype, resulting in an exception if not already correct type Link​
Shaded Jackson version upgraded from 2.9.9/2.9.9.3 to 2.10.1 Link​
Fixed an issue with KNN where getMostPopulatedClusters actually returned the least populated clusters Link​
Deeplearning4j: Transition Guide, 1.0.0-beta5 to 1.0.0-beta6
Deeplearning4j UI artifact ID has changed: deeplearning4j-ui_2.1x (beta5 and earlier) with deeplearning4j-ui
ND4J and SameDiff
ND4J/SameDiff: Features and Enhancements
Added suport for CUDA 10.2 Link​
DNNL (MKL-DNN) upgraded to version 1.1 Link​
Added ND4j namespaces to match SameDiff: Nd4j.math, Nd4j.random, Nd4j.bitwise, Nd4j.nn (neural network) Link​
Added SameDiff.calculateGradientsAndOutputs method Link Link​
Additional SameDiff single batch .output method overloads for DataSet/MultiDataSet added Link​
TensorFlow import ops coverage enhanced (significant number of additional ops supported) Link, Link, Link, Link, Link​
PRelu op added Link​
adjust_contrast, igamma and igammac ops added Link​
ND4J/SameDiff: BitCast, CompareAndBitpack, DivideNoNan, DrawBoundingBoxes, FakeQuantWithMinMaxVarsPerChannel ops added Link​
non_max_suppression_overlaps op added Link​
ImagePreProcessingScaler now supports segmentation use cases Link​
concat operation now supports the concatenation axis being specified via the last input array Link​
Added Gamma and Poisson RNG distributions Link​
SameDiff’s use of DeviceLocal for variables/constants etc is now configurable Link​
Uniform distribution op now supports random integer generation, not just random floating point generation Link​
SameDiff: Added simple OpBenchmarkListener for benchmarking purposes Link​
Added the ability to disable platform helpers (DNNL/MKLDNN etc) via Nd4jCPU.Environment.getInstance().allowHelpers(false); and Nd4jCuda.Environment.getInstance().allowHelpers(false); Link​
Added draw_bounding_boxes operation Link​
Added resize_bicubic operation Link​
Added causal padding mode to conv1d operation Link​
DNNL (MKLDNN) is included and enabled by default for non-AVX builds Link​
Added SameDiff ArraySavingListener for debugging purposes Link​
ND4J/SameDiff: Bug Fixes and Optimizations
OpenMP replaced with ThreadPool abstraction, enables parallelism for platforms without OpenMP support Link​
SameDiff memory management overheauled for (in some cases significantlny) reduced memory consumption and improved performance Link, Link​
Switched to Clang instead of gcc for OSX compilation to avoid compiler-related issues Link​
Removed SameDiff.outputs() “best guess” output inference due to being unreliable, in favor of explicit SameDiff.setOutputs(String...) call Link​
Fixed an issue with Nd4j.hstack on 1D arrays Link​
SameDiff no longer allows empty arrays for variables Link​
Fixed an issue with Nadam updater LR schedules not being cloned Link​
Cleaned up IActivation interface Link​
Added new LSTM op implementation with DNNL/MKLDNN support (forward pass only so far) Link​
SameDiff API cleaned up; deprecated methods removed Link​
Switched SameDiff variable initialization to non-lazy, to avoid unexpected behaviour when mixing execution and ND4J RNG seed setting Link​
SameDiff.zero and .one methods now create constants, not vairables Link​
Moved CUDA build version and device logging to Java logging, from c++ stdout to enable disabling logging (via ND4J config or slf4j config) Link​
Added DNNL/MKLDNN support for batch normalization Link​
SameDiff: Fixed an issue where listeners weren’t being called for gradient calculation Link​
Added DNNL/MKLDNN support for deconv2d/3d operations Link​
Fixed an issue with biasadd_bp operation and NHWC data format Link​
Fixed an issue with certain strided slice backprop configurations Link, Link​
Fixed an issue with LogSumExp reduction operation backprop for along dimension case Link, Link​
INDArray.toString() now has correct brackets for rank 1+ scalars to avoid ambiguity Link​
Fixed an issue where some ND4J methods could fail when the library is compiled on Java 9+ but run on Java 8 Link​
Fixed empty array input case for is_strictly_increasing, non_decreasing and non_max_suppression ops Link, Link​
Fixed empty input arrays for legacy ops (transform, scalar, pairwise, broadcast) Link​
CUDA compute capability 3.0 is supported again Link​
Improved performance for Scatter operations (1D case) + index validation Link​
Fixed an issue where SameDiff TrainingConfig serialization would fail if evaluation instances are set Link, Link​
SameDiff execution will now throw an exception when assertion operations in the graph fail Link​
PolyGamma function now returns NaNs when passed double for args requiring integer values Link​
Fixed some issues for pad and mirror_pad ops to ensure they conform with Tensorflow for imported networks Link​
Updated and fixed some issues for TensorFlow graph runner Link​
Improved performance for Reverse operation Link​
Removed/cleanup up unused ND4J list functionality Link​
Fixed reduce bool operation results (such as any, all, IsInf, etc) for empty array inputs Link​
ND4J: Transition Guide, 1.0.0-beta5 to 1.0.0-beta6
SameDiff.outputs() now requires user to call SameDiff.setOutputs(String...) first; previous “best guess” output inference was unreliable Link​
SameDiff.zero and .one methods now create constants, not vairables Link​
DataVec
DataVec: Bug Fixes and Optimizations
NativeImageLoader now checks for empty input streams and throws an exception instead of crashing Link​
NDArrayScalarOpTransform now supports modulus operator Link​
RL4J
RL4J: Features and Enhancements
Added AsyncTrainingListener Link​
Replaced multiple uses of java.util.Random with ND4J Random Link​
Added Observable and LegacyMDPWrapper Link​
RL4J: Bug Fixes and Optimizations
Refactored RL4J video recording to separate VideoRecorder class Link​
Fixed an issue with target for DQN Link, Link​
Refactoring for DQN and double DQN for improved maintainability Link​
Internal refactoring and various bug fixes Link​
PyDataVec
PyDataVec Features and Enhancements
PyDataVec TransformProcess now supports non-inplace operations Link​
PyDataVec Bug Fixes and Optimizations
Fixed various issues with PyDataVec Link​
Fixed an issue with data locality that could cause incorrect results under some circumstances when running on CUDA Link​
1.0.0-beta7
1.0.0-beta5
Last modified 1mo ago
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Highlights - 1.0.0-beta6 Release
Deeplearning4J
Deeplearning4J: Features and Enhancements
Deeplearning4J: Bug Fixes and Optimizations
Deeplearning4j: Transition Guide, 1.0.0-beta5 to 1.0.0-beta6
ND4J and SameDiff
ND4J/SameDiff: Features and Enhancements
ND4J/SameDiff: Bug Fixes and Optimizations
ND4J: Transition Guide, 1.0.0-beta5 to 1.0.0-beta6
DataVec
DataVec: Bug Fixes and Optimizations
RL4J
RL4J: Features and Enhancements
RL4J: Bug Fixes and Optimizations
PyDataVec
PyDataVec Features and Enhancements
PyDataVec Bug Fixes and Optimizations