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On this page
  • Highlights - 1.0.0-beta3 Release
  • Deeplearning4J
  • Deeplearning4J: New Features
  • Deeplearning4J: Bug Fixes and Optimizations
  • Deeplearning4J: API Changes (Transition Guide): 1.0.0-beta2 to 1.0.0-beta3
  • Deeplearning4J: Known issues: 1.0.0-beta3
  • Deeplearning4J: Keras Import
  • ND4J
  • ND4J: New Features
  • ND4J: Bug Fixes and Optimizations
  • ND4J: API Changes (Transition Guide): 1.0.0-beta2 to 1.0.0-beta3
  • ND4J: Known issues: 1.0.0-beta3
  • DataVec
  • DataVec: New Features
  • DataVec: Optimizations and Bug Fixes
  • Arbiter
  • Arbiter: Fixes
  • ND4S

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  1. Release Notes

1.0.0-beta3

Previous1.0.0-beta4Next1.0.0-beta2

Last updated 3 years ago

Was this helpful?

Highlights - 1.0.0-beta3 Release

  • ND4J/Deeplearning4j: Added support for CUDA 10.0. Dropped support for CUDA 8.0. (1.0.0-beta3 release has CUDA 9.0, 9.2 and 10.0 support)

  • SameDiff now supports training and evaluation from DataSetIterator and MultiDataSetIterator. Evaluation classes have been moved to ND4J.

  • DL4J Spark training (gradient sharing) is now fully fault tolerant, and has improvements for threshold adaption (potentially more robust convergence). Ports can now be easily configured independently on master/workers.

Deeplearning4J

Deeplearning4J: New Features

  • Added OutputAdapter interface and MultiLayerNetwork/ComputationGraph.output method overloads using OutputAdapter (avoids allocating off-heap memory that needs to be cleaned up by GC) , ,

  • Added ComputationGraph/MultiLayerNetwork rnnTimeStep overload with user-specified workspace.

  • Added Cnn3DLossLayer

  • ParallelInference: Instances can now update the model in real-time (without re-init)

  • ParallelInferenc: Added ParallelInference INPLACE mode

  • Added validation for incompatible loss/activation function combinations (such as softmax+nOut=1, or sigmoid+mcxent). New validation can be disabled using outputValidation(false)

  • Spark training: Added full fault tolerance (robust failure recovery) for gradient sharing implementation

  • Spark training now supports configuring ports more flexibly (and differently for different workers) using PortSupplier

  • Spark training: overhauled gradient sharing threshold adaption algorithms; made it possible to customize threshold settings, plus made defaults more robust to initial threshold configuration improving convergence speed in some cases.

  • Spark training: implemented chunked messaging to reduce memory requirements (and insufficient buffer length issues) for large messages

  • Spark training: Added MeshBuildMode configuration for improved scalability for large clusters

  • Spark network data pipelines: added FileBatch, FileBatchRecordReader etc for "small files" (images etc) distributed training use cases

  • Added FailureTestingListener for fault tolerance/debugging purposes

  • Upgraded Apache Lucene/Solr to version 7.5.0 (from 7.4.0)

  • Added system properties (org.deeplearning4j.tempdir and org.nd4j.tempdir) to allow overriding of the temporary directories ND4J and DL4J use

  • Mode MultiLayerNetwork/ComputationGraph.clearLayerStates methods public (was protected)

  • AbstactLayer.layerConf() method is now public

  • ParallelWrapper module now no longer has a Scala version suffix for artifact id; new artifact id is deeplearning4j-parallel-wrapper

  • Improved validation and error mesages for invalid inputs/labels in Yolo2OutputLayer

  • Spark training: added SharedTrainingMaster.Builder.workerTogglePeriodicGC and .workerPeriodicGCFrequency to easily configure the ND4J garbage collection configuration on workers. Set default GC to 5 seconds on workers

  • Spark training: added threshold encoding debug mode (logs current threshold and encoding statistics on each worker during training). Enable using SharedTrainingConfiguration.builder.encodingDebugMode(true). Note this operation has computational overhead.

Deeplearning4J: Bug Fixes and Optimizations

    • Note that learning rates may need to be decreased for some updaters (such as Adam) to account for this change vs. earlier versions. Some other updaters (such as SGD, NoOp, etc) should be unaffected.

    • Note that deserialized (loaded) configurations/networks saved in 1.0.0-beta2 or earlier will default to old behaviour for backward compatibility. All new networks (created in 1.0.0-beta3) will default to the new behaviour.

Deeplearning4J: API Changes (Transition Guide): 1.0.0-beta2 to 1.0.0-beta3

  • IEvaluation classes in DL4J have been deprecated and moved to ND4J so they are available for SameDiff training. Functionality and APIs are unchanged

Deeplearning4J: Known issues: 1.0.0-beta3

Deeplearning4J: Keras Import

ND4J

ND4J: New Features

  • Libnd4j new ops:

ND4J: Bug Fixes and Optimizations

  • Libnd4j native op fixes:

ND4J: API Changes (Transition Guide): 1.0.0-beta2 to 1.0.0-beta3

  • CUDA 8.0 support has been removed. CUDA 9.0, 9.2 and 10.0 support is available in 1.0.0-beta3

ND4J: Known issues: 1.0.0-beta3

  • Android users may need to manually exclude the (now deprecated) module nd4j-base64. This is due to org.nd4j.serde.base64.Nd4jBase64 class being present in both nd4j-api and nd4j-base64 modules. Both versions have identical content. Use exclude group: 'org.nd4j', module: 'nd4j-base64' to exclude.

DataVec

DataVec: New Features

DataVec: Optimizations and Bug Fixes

Arbiter

Arbiter: Fixes

ND4S

Fixed an issue where L1/L2 and updaters (Adam, Nesterov, etc) were applied before dividing gradients by minibatch to obtain average gradient. To maintain old behaviour, use NeuralNetConfiguration.Builder.legacyBatchScaledL2(true) .

Fixed an issue where EarlyStoppingScoreCalculator would not correctly handle "maximize score" cases instead of minimizing

Fixed order (BGR vs. RGB) for VGG16ImagePreProcessor channel offset values

Fixed bug with variational autoencoders using weight noise

Fixed issue with BaseDataSetIterator not respecting the 'maximum examples' configuration

Optimization: A workspace is now used for ComputationGraph/MultiLayerNetwork evaluation methods (avoids allocating off-heap memory during evaluation that must be cleaned up by garbage collector)

Fixed an issue where shuffling combined with a subset for MnistDataSetIterator would not maintain the same subset between resets

Fixed issue with StackVertex.getOutputType

Fix issue with CNN to/from RNN preprocessors handling of mask arrays

Fixed issue with VGG16 non-pretrained configuration in model zoo

Fixed issue with TransferLearning nOutReplace where multiple layers in a row are modified

Fixed issue with CuDNN workspaces where backpropagation is performed outside of a standard fit call

Fixed an issue with dropout masks being cleared prematurely on output layers in ComputationGraph

RecordReaderMultiDataSetIterator now supports 5D arrays (for 3D CNNs)

Fixed bug in multi input/output ComputationGraphs with TBPTT combined with both masking and different number of input/output arrays

Improved input validation/exceptions for batch normalization layer

Fixed bug with TransferLearning GraphBuilder nOutReplace when combined with subsampling layers

SimpleRnnParamInitializer now properly respects bias initialization configuration

Fixed SqueezeNet zoo model non-pretrained configuration

Fixed Xception zoo model non-pretrained configuration

Fixed an issue with some evaluation signatures for multi-output ComputationGraphs

Improved MultiLayerNetwork/ComputationGraph summary method formatting for large nets

Fixed an issue where gradient normalization could result in NaNs if gradient is exactly 0.0 for all parameters in a layer

Fixed an issue where MultiLayerNetwork/ComputationGraph.setLearningRate could throw an exception for SGD and NoOp updaters

Fixed an issue with StackVertex plus masking in some rare cases

Fixed an issue with JSON deserialization of frozen layers in pre-1.0.0-alpha format

Fixed an issue where GraphBuilder.removeVertex can fail under some limited circumstances

Fixed a bug in CacheableExtractableDataSetFetcher

DL4J Spark training: Fixed issues with thread/device affinity for multi-GPU training + evaluation

DL4J Spark training: Made all Aeron threads daemon threads to prevent Aeron from stopping JVM shutdown when all other threads have completed

Added cudnnAllowFallback configuration for BatchNormalization layer (fallback to built-in implementation if CuDNN fails unexpectedly)

Fixed some rare concurrency issues with multi-worker (multi-GPU) nodes for Spark training

Fixed an issue with BatchNormalization layers that prevented the mean/variance estimates from being synced properly on each worker for GradientSharing training, causing convergence issues

Added a check to detect ZipSlip CVE attempts in ArchiveUtils

DL4J Spark training and evaluation: methods now use Hadoop Configuration from Spark context to ensure runtime-set configuration is available in Spark functions reading directly from remote storage (HDFS etc)

MultiLayerNetwork and ComputationGraph now properly support more than Integer.MAX_VALUE parameters

Added data validation for Nd4j.readTxt - now throws exception on invalid input instead of returning incorrect values

Fixed an issue with KNN implementation where a deadlock could occur if an invalid distance function (one returning "distances" less than 0) was utilized

Added synchronization to loading of Keras import models to avoid thread safety issues in the underlying HDFS library used for loading

Fixed rare issue for Async(Multi)DataSetIterator with large prefetch values

MultiLayerConfiguration/ComputationGraphConfiguration pretrain(boolean) and backprop(boolean) have been deprecated and are no longer used. Use fit and pretrain/pretrainLayer methods instead.

ParallelWrapper module now no longer has a Scala version suffix for artifact id; new artifact id is deeplearning4j-parallel-wrapper which should be used instead

deeplearning4j-nlp-korean module now has Scala version suffix due to scala dependencies; new artifact ID is deeplearning4j-nlp-korean_2.10 and deeplearning4j-nlp-korean_2.11

Running multiple Spark training jobs simultaneously on the one physical node (i.e., multiple JVMs from one or more Spark jobs) may cause problems with network communication. A workaround for this is to manually set a unique stream ID manually in the VoidConfiguration. Use a unique (or random) integer value for different jobs

Fixed import issue due to Keras JSON format changes for Keras 2.2.3+

Added Keras import for timeseries preprocessing

Elephas

Fixed issue with importing models with reshaping after an embedding layer

Added support for Keras masking layers

Fixed JSON deserialization issue with some layers/preprocessors, such as Permute

Fixed issue with Keras import of Nadam configuration

Added SameDiff training and evaluation: SameDiff instances can now be trained directly using DataSetIterator and MultiDataSetIterator, and evaluated using IEvaluation instances (that have been moved from ND4J to DL4J)

Added GraphServer implementation: c++ inference server for SameDiff (and Tensorflow, via TF import) with Java API

SameDiff instances can now be loaded from serialized FlatBuffers format (SameDiff.asFlatFile plus fromFlatFile)

Added MKL-DNN support for some operations (Conv2d, etc)

Upgraded ND4J (and DataVec) to Arrow 0.11.0 , which also fixes

Added Nd4j.where op method (same semantics as numpy.where)

Added Nd4j.stack op method (combine arrays + increase array rank by 1)

Matrix band part

Scatter ND, ND-add, ND-sub and ND-update ops

Sparse softmax cross entropy loss with logits

Histogram fixed width op

broadcast_to op

deconv3d op added

Unsorted segment ops added

Segment_X backprop ops added

batchnorm_new op added that supports multiple axes for mean/variance

GRU cell backprop added

Nd4j Preconditions class now has methods for formatting INDArray arguments ,

SameDiff loss functions: cleanup plus forward pass implementation

CudaGridExecutioner now warns that exception stack traces may be delayed to avoid confusion in debugging exceptions occuring during asynchronous execution of ops

JavaCPP and JavaCPP-presets have been upgraded to version 1.4.3

Improved Javadoc on SDVariable class

Fixes for android: Remove use of RawIndexer

Libnd4j custom ops: conv op weight layouts are now not dependent on the input format (NCHW/NHWC) - now always [kH, kW, inChannels, outChannels] for 2d CNNs, [kH, kW, kD, inChannels, outChannels] for 3d CNNs. ,

Dot operation backprop , determinant

Backprop op fix for the broadcast case for some pairwise transform custom op implementations

Fix for reverse custom op with rank 1 inputs

ATan2 op is now broadcastable

Boolean custom op broadcast fixes/additions

Scatter op edge case fixes

ArgMin shape function fix , negative axis fix

Unique op fix

Pad op fix

Fixed where op shape function

SVD rank 1 edge case fix

Range op

Split and space_to_batch fixes

Broadcast dynamic shape

embedding_lookup op now supports multiple input arrays

Matrix determinant op edge case (rank 0 result) shape fix

SameDiff TensorFlow import: fixes for multiple operations , , ,

SameDiff: Improved error handling for multiple outputs case

Fixed issue where INDArray.permute would not correctly throw an exception for invalid length case

Fixed issues with INDArray.get/put with SpecifiedIndex ,

Minor change to DataSet.merge - signature now accepts any DataSet subtypes

INDArray.transposei operation was not in-place

Fixed issues with INDArray.mmul with MMulTranspose

Added additional order validation for ND4J creation methods (create, rand, etc)

Fix for ND4J binary deserialization (BinarySerde) when deserializing from heap byte buffers

Fixed issue with Nd4j-common ClassPathResource path resolution in some IDEs

Fixed issue where INDArray.get(interval) on rank 1 array would return rank 2 array

Fixed a validation issue with Nd4j.gemm/mmuli on views

INDArray.assign(INDArray) no longer allows assigning different shape arrays (other than scalar/vector cases)

NDarrayStrings (and INDArray.toString()) now always uses US locale when formatting numbers

Fixed an issue with GaussianDistribution specific to V100 GPUs

Fixed an issue with bitmap compression/encoding specific to V100 GPUs

Transforms.softmax now throws an error on unsupported shapes instead of simply not applying operation

VersionCheck functionality: handle case where SimpleFileVisitor is not available on earlier versions of Android

SameDiff convolution layer configuration (Conv2dConfig/Conv3dConfig/Pooling3dConfig etc) have had parameter names aligned

nd4j-base64 module contents have been deprecated; use the equivalent classes in nd4j-api from now on

Some classes in nd4j-jackson module has been deprecated; use the equivalent classes in nd4j-api from now on

Added NativeImageLoader method overloads for org.opencv.core.Mat and String as filename

Fix for JDBCRecordReader handling of null values

Improved errors/validation for ObjectDetectionRecordReader for invalid input (where image object centers are outside of image bounds)

Fixed issue where FileSplit using methods that are unavailable on earlier versions of Android

Added SerializableHadoopConfiguration and BroadcastHadoopConfigHolder for cases where a Hadoop configuration is required in Spark functions

Fixed issue with JDBCRecordReader's handling of real-valued column result types

Added validation and useful exception for CSVRecordReader/LineRecordReader being used without initialization

Fixed some issues with dropout layers

Added conversion between org.nd4j.linalg.primitives.Pair/Triple and Scala Tuple

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