com.intel.analytics.bigdl.optim

ParallelOptimizer

object ParallelOptimizer extends AbstractOptimizer

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AbstractOptimizer, AnyRef, Any
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  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def checkpoint[T](cacheTrigger: Option[Trigger], cachePath: Option[String], isOverWrite: Boolean, wallClockTime: Long, models: RDD[Cache[T]], state: Table, parameters: Map[String, AllReduceParameter[T]], optimMethods: Map[String, OptimMethod[T]], trainingModel: Module[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Unit

    * Create checkpoint.

    * Create checkpoint.

    cacheTrigger

    cache trigger

    cachePath

    cache path

    isOverWrite

    whether over write

    wallClockTime

    wall clock time

    models

    cached models

    state

    state table

    parameters

    all reduce parameters

    optimMethods

    all optim methods

    trainingModel

    training model

    Attributes
    protected
    Definition Classes
    AbstractOptimizer
  8. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )
  12. final def getClass(): Class[_]

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  13. def getModel[T](models: RDD[Cache[T]], parameters: Map[String, AllReduceParameter[T]], trainingModel: Module[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Module[T]

    Fetch current model parameters to driver, and copy to trainingModel.

    Fetch current model parameters to driver, and copy to trainingModel.

    models

    cached models

    trainingModel

    the model is trained by optimizer

    returns

    trained model

    Attributes
    protected
    Definition Classes
    ParallelOptimizerAbstractOptimizer
  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. val logger: Logger

  17. final def ne(arg0: AnyRef): Boolean

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. def saveSummary[T](trainSummary: TrainSummary, models: RDD[Cache[T]], driverState: Table, parameters: Map[String, AllReduceParameter[T]], trainingModel: Module[T])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Unit

    Save train summaries.

    Save train summaries.

    trainSummary

    train logger

    models

    cached models

    driverState

    driver state

    parameters

    AllReduceParameter

    Attributes
    protected
    Definition Classes
    AbstractOptimizer
  21. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
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  22. def toString(): String

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  23. def validate[T](validationTrigger: Option[Trigger], validationDataSet: Option[DataSet[MiniBatch[T]]], validationMethods: Option[Array[ValidationMethod[T]]], coresPerNode: Int, models: RDD[Cache[T]], state: Table, validationSummary: Option[ValidationSummary], header: String, parameters: Map[String, AllReduceParameter[T]] = null): Unit

    Validate current model and save the result.

    Validate current model and save the result.

    validationTrigger

    validation trigger

    validationDataSet

    validation dataset

    validationMethods

    validation methods

    coresPerNode

    cores per node

    models

    cached models

    state

    state table

    validationSummary

    validation logger.

    header

    log header string

    Attributes
    protected
    Definition Classes
    AbstractOptimizer
  24. final def wait(): Unit

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    @throws( ... )
  25. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  26. final def wait(arg0: Long): Unit

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Inherited from AbstractOptimizer

Inherited from AnyRef

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