Highlights - 1.0.0-beta5 Release

  • Added model server - remote inference of SameDiff and DL4J models using JSON or (optionally) binary serialization

  • Added Scala 2.12 support, dropped Scala 2.10 support. Modules with Scala dependencies are now released with Scala 2.11 and 2.12 versions

  • Apache Spark 1.x support dropped (now only Spark 2.x is supported). Note: Spark version suffix dropped: For upgrading: 1.0.0-beta4_spark2 -> 1.0.0-beta5

  • Added FastText support to deeplearning4j-nlp

  • CUDA support for all ND4J/SameDiff Operations

    • In 1.0.0-beta4, some operations were CPU only. Now, all operations have full CUDA support

  • Added support for new data types in ND4J (and DL4J/SameDiff): BFLOAT16, UINT16, UINT32, UINT64

  • ND4J: Implicit broadcasting support added to INDArray (already present in SameDiff - for example shape [3,1]+[3,2]=[3,2])

  • CUDA 9.2, 10.0 and 10.1-Update2 still supported

    • NOTE: For CUDA 10.1, CUDA 10.1 update 2 is recommended. CUDA 10.1 and 10.1 Update 1 will still run, but rare internal cuBLAS issues may be encountered in heavily multi-threaded code on some systems

  • Dependency upgrades: Jackson (2.5.1 to 2.9.9/, Commons Compress (1.16.1 to 1.18), Play Framework (2.4.8 to 2.7.3), Guava: (20.0 to 28.0-jre, and shaded to avoid dependency clashes)

  • CUDA: now host (RAM) buffers are only allocated when required (previously: host buffers were always allocated), in addition to device (GPU) buffer


Deeplearning4J: Features and Enhancements

  • Added FastText - inference and training, including OOV (out of vocabulary) support (Link)

  • Scala 2.12 support added, Scala 2.10 support dropped (Link)

  • Added model server (DL4J and SameDiff models, JSON and binary communication) - JsonModelServer, JsonRemoteInference, Link, Link

  • Added saved model format validation utilities - DL4JModelValidator, DL4JKerasModelValidator (Link)

  • Added LabelLastTimeStepPreProcessor (Link)

  • BertIterator: added option to prepend token to the output (such as [cls] expected by some models) (Link)

  • Added trace level logging to MultiLayerNetwork and ComputationGraph assist with debugging certain issues (Link)

  • Upsampling3D: Added NDHWC support (Link)

  • MergeVertex now supports broadcasting (Link)

  • LSTM and Dropout will now fall back on built-in implementations if an exception is encountered from cuDNN (same as Subsampling/ConvolutionLayer) (Link)

  • Improved JavaDoc and cleanup up API for WordVectorSerializer (Link, Link)

Deeplearning4J: Bug Fixes and Optimizations

  • Updated deeplearning4j-ui theme (Link)

  • Fixed an issue with MergeVertex and CNN3D activations (Link)

  • Fixed typo in Yolo2OutputLayer builder/configuration method name (Link)

  • Improved ComputationGraph builder InputType validation (Link)

  • Removed dl4j-spark-ml module until it can be properly maintained (Link)

  • Fixed an issue with BertWordPieceTokenizerFactory and bad character encoding (Link)

  • Fixed an issue with LearnedSelfAttentionLayer and variable minibatch size (Link, Link)

  • Fixed issue with SharedTrainingMaster controller address when set from environment variable (Link)

  • Fixed issue with SameDiffOutputLayer initialization under some circumstances (Link)

  • https is now used by default for data and zoo model downloads (Link, Link)

  • Fixed an issue where UI WebJars dependencies would check for updates on every single build (Link, Link)

  • Fixed issue where Upsampling layer memory report could produce an OOM exception (Link)

  • Improved UX/validation for RecordReaderDataSetIterator (Link)

  • Fixed an issue where EmbeddingSequenceLayer would not check mask array datatype (Link)

  • Improved validation when initializing networks with a non rank-2 (shape [1, numParams]) array (Link)

  • Fixed a DataType issue for BertIterator (Link)

  • Fixed Word2Vec model backward compatibilty (beta3 and earlier models now loadable again) Link

  • Fixed issue where some Keras import models could fail with Could not read abnormally long HDF5 attribute (Link)

  • Added validation for RnnOutputLayer - feature/label array lengths (Link)

  • Fixed an issue where SameDiffOutputLayer would not support variable minibatch size (Link)

  • Fixed DL4J SameDiff layer mask support (Link)

  • DL4J UI: Fixed an issue where tab switching did not work when visualizing saved/stored data (Link, Link)

  • DL4J UI: Fixed a rare UI threading issue (Link)

  • Fixed a Keras import issue with JSON format change (Link)

  • Fixed a Keras import issue where updater learning rate schedule could be imported incorrectly (Link)

  • Fixed an issue with CnnSentenceDataSetIterator when using UnknownWordHandling.UseUnknownVector (Link, Link)

  • Fixes and optimizations to DL4J SameDiff layers (Link)

  • MultiLayerNetwork/ComputationGraph will now log the original exception if a second exception occurs during workspace closing, instead of swallowing it (inference/fit operation try/finally blocks) (Link)

  • Upgraded dependencies: Jackson (2.5.1 to 2.9.9/, Commons Compress (1.16.1 to 1.18), Play Framework (2.4.8 to 2.7.3), Guava: (20.0 to 28.0-jre, shaded to avoid dependency clashes) (Link)

  • Logging framework can now be configured for DL4J UI (due to Play framework dependency upgrade) (Link)

  • Reduced amount of garbage produced by MnistDataFetcher (impacts MNIST and EMNIST DataSetIterators) (Link)

  • Activation function backpropagation has been optimized for many activation functions (Link, Link)

Deeplearning4j: Transition Guide, 1.0.0-beta4 to 1.0.0-beta5

  • DL4J AsyncDataSetIterator and AsyncMultiDataSetIterator moved to ND4J, use org.nd4j.linalg.dataset.Async(Multi)DataSetIterator instead

  • Saved models with custom layers from 1.0.0-alpha and before can no longer be loaded. Workaround: load in 1.0.0-beta4, and re-save the model (Link). Models without custom layers can still be loaded back to 0.5.0

  • Apache Spark 1.x support dropped (now only Spark 2.x is supported). Note: Spark version suffix dropped: For upgrading, change versions as follows: 1.0.0-beta4_spark2 -> 1.0.0-beta5

  • Scala 2.10 dropped, Scala 2.12 added (for modules with Scala dependencies)

Deeplearning4j: 1.0.0-beta5 Known Issues

  • dl4j-spark_2.11 and _2.12 dependencies incorrectly pull in datavec-spark_2.11/2.12 version 1.0.0-SNAPSHOT. Workaround: control version using dependency management as per here or here

  • Some layers (such as LSTM) may run slower on 1.0.0-beta5 than 1.0.0-beta4 on CUDA when not using cuDNN, due to added synchronization. This synchronization will be removed in the next release after 1.0.0-beta5

  • CUDA 10.1: Rare internal cuBLAS issues may be encountered in heavily multi-threaded code on some systems, when running CUDA 10.1 Update 1 (and maybe 10.1). CUDA 10.1 update 2 is recommended.

ND4J and SameDiff

ND4J/SameDiff: Features and Enhancements

  • Added new data types: BFLOAT16, UINT16, UINT32, UINT64 (Link)

  • CUDA support for all operations without CUDA implementations (Link, Link, Link, Link, Link)

  • Added model server (DL4J and SameDiff models, JSON and binary communication) - JsonModelServer, JsonRemoteInference, Link, Link

  • Added support for empty arrays with zeros in shape, for compatibility with TensorFlow import (Link)

  • CUDA: now host (RAM) buffers are only allocated when required (previously: host buffers were always allocated), in addition to device (GPU) buffer

  • Improved SameDiff training API - added "in line" test set evaluation, returning History object with loss curve, etc (Link)

  • Added saved model format validation utilities - Nd4jValidator, Nd4jCommonValidator (Link)

  • Added SameDiff ScoreListener (equivalent to DL4J ScoreIterationListener/PerformanceListener) (Link, Link)

  • Added SameDiff.convertDataTypes method, for variable dtype conversion (Link)

  • Added crop and resize op (Link)

  • DL4J AsyncDataSetIterator and AsyncMultiDataSetIterator moved to ND4J Link

  • Added basic/MVP SameDiff UI listener (Link)

  • Added SameDiff CheckpointListener (Link, Link)

  • Added SameDiff name scopes (Link)

  • SameDiff: Updater state and training configuration is now written to FlatBuffers format (Link)

  • Added c++ benchmark suite callable from Java - call using Nd4j.getExecutioner().runLightBenchmarkSuit() and Nd4j.getExecutioner().runFullBenchmarkSuit() (Link)

  • Added SameDiff.save/load methods with InputStream/OutputStream arguments (Link, Link)

  • Added axis configuraiton for evaluation instances (Evaluation, RegressionEvaluation, ROC, etc - getAxis and setAxis methods) to allow different data formats (NCHW vs. NHWC for CNNs, for example) (Link)

  • SameDiff: Added support to convert constants to placeholders, via SDVariable.convertToConstant() method (Link)

  • SameDiff: Added GradCheckUtil.checkActivationGradients method to check activation gradients for SameDiff instance (not just parameter gradients as in existing gradient check methods) (Link)

  • Added CheckNumerics op (Link)

  • Added FakeQuantWithMinMaxArgs and FakeQuantWithMinMaxVars ops (Link)

  • Added INDArray reduction methods with "keep dimensions" option - for example, INDArray.mean(boloean, int... dimension) (Link)

  • Added Nd4j SystemInfo class - SystemInfo.getSystemInfo, .writeSystemInfo(File) to aid with debugging issues (Link, Link)

  • Added INDArray.toString(NDArrayStrings options), toStringFull() and toString overloads for easier control of array printing (Link)

  • Added HashCode op, INDArray.hashCode() (Link)

  • SameDiff: added whileLoop, ifCond methods for loops/conditional ops (Link)

  • Cleaned up some infrequently used Nd4j methods (Link, Link, Link, Link)

  • Added bitwise integer operations: left/right bit shift, left/right cyclical bit shift, bitwise Hamming distance (Link, Link, Link, Link, Link)

  • deeplearning4j-nlp: renamed AggregatingSentencePreProcessor to sentencePreProcessor method (Link)

  • Upgraded (and shaded) Protobuf version - 3.5.1 to 3.8.0 (Link)

  • Switched to c=style error handling for libnd4j native operations (Link)

  • Renamed FlatBuffers enum org.nd4j.graph.DataType to org.nd4j.graph.DType to avoid users importing incorrect type when using Nd4j methods (Link, Link)

  • Added SameDiff.bitwise namespace for bitwise ops (Link, Link)

ND4J/SameDiff: Bug Fixes and Optimizations

  • Updated to JavaCPP/JavaCV 1.5.1-1 (Link)

  • SameDiff: Placeholders must now only be provided if required to calculate the requested variables (Link)

  • SameDiff: Fixed an issue with duplicate variable name validation (Link)

  • SameDiff: Fixed an issue with SDVariable.getArr for scalars (Link)

  • Added delayed mode to DeviceLocalNDArray (don't replicate to device until needed) (Link)

  • ND4J: Fixed an issue with writing 0d (scalar) NDArrays in numpy .npy format (Link)

  • Fixed an issue with Pad operation for some constant cases (Link)

  • Fixed some issues with strided_slice operation (Link, Link, Link)

  • SameDiff: Fixed issue with DataType inference for some ops using ND4J default datatype (Link)

  • INDArray.castTo(DataType) is now a no-op when array is already the correct type (Link)

  • SameDiff: Fixed an issue with training mixed precision networks (Link)

  • Fixed an issue where Evaluation class was incorrectly reporting macro-averaged precision for binary case (Link)

  • Removed trainableParams config/field from SameDiff TrainingConfig (no longer required) (Link)

  • Improvements and cleanup to ND4J Javadoc (Link, Link, Link, Link)

  • Fixed an issue with Cholesky Lapack op on CUDA (Link, Link)

  • Fixed an issue where [1,N] and [N,1] arrays were not considered a matrix (rank 2 array) according to INDArray.isMatrix() (Link)

  • Fixed RegressionEvaluation for 4D arrays (CNNs / segmentation) (Link, Link)

  • Fixed issue with INDArray.median(int... dimension) (Link)

  • Fixed NPE that could occur when executing gather operation backprop (Link)

  • Fixed issue with LogSumExp operation Java/C++ mapping (Link)

  • Added header validation when reading Numpy .npy files, to ensure file is valid (Link)

  • Fixed a possible issue with reading Numpy .npy files on CUDA (Link)

  • Fixed an issue when reading Numpy .npy boolean files (Link)

  • Various fixes for TensorFlow import (Link)

  • Fixed an issue with a small number of Nd4j.create methods not creating arrays corresponding to the java primitive (Link)

  • Improved shape validation for some Nd4j.create methods (Link)

  • Cleaned up unmaintained Nd4j.createSparse methods (Link)

  • Fixed a CUDA issue for CUDA GPUs with CC 3.0 (Link)

  • Fixed some possible integer overflows in c++ code (Link)

  • Removed deprecated methods: Nd4j.trueScalar and Nd4j.trueVector (Link, Link)

  • Fixed an issue where some JVMs could warn about "Illegal reflective access" due to a (now removed) SameDiff dependency (Link)

  • SDVariable now no longer extends DifferentialFunction (Link)

  • Moved numerous operation calculateOutputShape instances from Java to C++ (Link)

  • Fixed an issue where maxpool2d_bp could throw an exception when NaN values are present (Link)

  • Fixed an issue with concatenation of empty shapes (with zeros) (Link)

  • Removed INDArray.javaTensorAlongDimension (Link)

  • LayerNorm operation now properly supports axis arg, NCHW format data (Link)

  • libnd4j: cuBLAS hgemm (FP16 gemm) wil only be called for devices with compute capability >= 5.3 due to cuBLAS limitations (Link)

  • Nd4j.readNumpy optimized (Link)

  • Added configurable alpha parameter to ELU and lrelu_bp operations in c++ (Link)

  • Cleaned up SameDiff SDCNN/SDRNN (SameDiff.cnn, .rnn) API/methods (Link, Link)

ND4J: Transition Guide, 1.0.0-beta4 to 1.0.0-beta5

  • OldAddOp, OldSubOp, etc removed: Replace with AddOp, SubOp, etc

  • Nd4j.trueScalar and trueVector removed; use Nd4j.scalar and Nd4j.createFromArray methods

  • INDArray.javaTensorAlongDimension removed; use INDArray.tensorAlongDimension instead

  • INDArray.lengthLong() removed; use INDArray.length() instead

ND4J: 1.0.0-beta5 Known Issues

  • nd4j-native on some OSX systems can fail with Symbol not found: ___emutls_get_address - See this link

  • SBT 1.3.0 can fail with an Illegal character in path error; SBT 1.2.8 is OK. This is an SBT issue, not an ND4J issue. See this link for details


DataVec: Features and Enhancements

  • ImageRecordReader: Support for 16-bit TIFF added (Link)

  • Added SequenceTrimToLengthTransform (Link)

DataVec: Bug Fixes and Optimizations

  • Fixed an issue with AnalyzeSpark and String columns (Link)

  • Fixed an issue with URL scheme detection in NumberedFileInputScheme (Link)

  • Fixed an issue with RandomPathFilter sampling being biased (Link, Link)


RL4J: Features and Enhancements

RL4J: Bug Fixes and Optimizations

  • Fixed issue with compression for HistoryProcessor (Link)


Bug Fixes and Optimizations

  • Updated EvaluationScoreFunction to use ND4J Evaluation class metrics (Link)

  • Fixed incorrect search size in GridSearchCandidateGenerator (Link)

Arbiter: Known Issues

  • The Jackson version upgrade necessitated a change to how generic object serialization was performed; Arbiter JSON data stored in 1.0.0-beta4 or earlier format may not be readable in 1.0.0-beta5 (Link)


ND4S Features and Enhancements

  • Added full data type support to ND4S as per ND4J (Link)

  • Added syntactic sugar for SameDiff (implicits, operator overloads) (Link)

Last updated