ParallelOptimizer

Value Members

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

   

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2. final def ##(): Int

   

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

   

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4. final def asInstanceOf[T0]: T0

   

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5. def checkpoint[T](cacheTrigger: Option[Trigger], cachePath: Option[String], isOverWrite: Boolean, wallClockTime: Long, models: RDD[Cache[T]], state: Table, parameters: 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

6. def clone(): AnyRef

   

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7. final def eq(arg0: AnyRef): Boolean

   

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8. def equals(arg0: Any): Boolean

   

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9. def finalize(): Unit

   

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10. final def getClass(): Class[_]

    

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11. def getModel[T](models: RDD[Cache[T]], parameters: 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

    ParallelOptimizer → AbstractOptimizer

12. def hashCode(): Int

    

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13. final def isInstanceOf[T0]: Boolean

    

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14. val logger: Logger

    

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

    

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16. final def notify(): Unit

    

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17. final def notifyAll(): Unit

    

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18. def saveSummary[T](trainSummary: TrainSummary, models: RDD[Cache[T]], driverState: Table, parameters: 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

19. final def synchronized[T0](arg0: ⇒ T0): T0

    

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20. def toString(): String

    

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21. 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: 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

22. final def wait(): Unit

    

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23. final def wait(arg0: Long, arg1: Int): Unit

    

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24. final def wait(arg0: Long): Unit

    

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