com.intel.analytics.bigdl.models.utils

ModelBroadcast

class ModelBroadcast[T] extends Serializable

ModelBroadcast is used to broadcast model.

Note: If you want to use this to broadcast training model, please use value(true) to get the model. And before broadcasting please make sure the model's parameter is compacted.

T

data type

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Instance Constructors

  1. new ModelBroadcast(applyProtoBuffer: Boolean = false)(implicit arg0: ClassTag[T], ev: TensorNumeric[T])

    applyProtoBuffer

    it will use proto buffer serialization for broadcasting if set true

Value Members

  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 broadcast(sc: SparkContext, model: Module[T]): ModelBroadcast.this.type

    broadcast the model first get and clear Const values from the model then get and clear the weight and bias parameters from the model finally broadcast Const values, the parameters and model(without parameters) separately

    broadcast the model first get and clear Const values from the model then get and clear the weight and bias parameters from the model finally broadcast Const values, the parameters and model(without parameters) separately

    sc

    SparkContext

    model

    model to broadcast

    returns

    this

  8. def clone(): AnyRef

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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

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  19. def toString(): String

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  20. def value(initGradient: Boolean = false): Module[T]

    get the broadcast model put the weight and bias back to the model

    get the broadcast model put the weight and bias back to the model

    initGradient

    if init gradParameter.

    returns

    model

  21. final def wait(): Unit

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

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

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