keystoneml.workflow.AutoCacheRule

GreedyCache

case class GreedyCache(maxMem: Option[Long] = scala.None, partitionScales: Seq[Long] = ..., numProfileTrials: Int = 1) extends CachingStrategy with Product with Serializable

Greedy caching strategy given a memory budget.

If no maxMem is provided, the memory budget defaults to 75% of the remaining memory on the cluster (given whatever RDDs are already cached)

maxMem

The memory budget (bytes)

partitionScales

The scales to sample at (average number of desired data points per partition)

numProfileTrials

The number of profiling samples to take per scale

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Serializable, Serializable, Product, Equals, CachingStrategy, AnyRef, Any
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Instance Constructors

  1. new GreedyCache(maxMem: Option[Long] = scala.None, partitionScales: Seq[Long] = ..., numProfileTrials: Int = 1)

    maxMem

    The memory budget (bytes)

    partitionScales

    The scales to sample at (average number of desired data points per partition)

    numProfileTrials

    The number of profiling samples to take per scale

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 clone(): AnyRef

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

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  9. def finalize(): Unit

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

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  11. final def isInstanceOf[T0]: Boolean

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  12. val maxMem: Option[Long]

    The memory budget (bytes)

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

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. val numProfileTrials: Int

    The number of profiling samples to take per scale

  17. val partitionScales: Seq[Long]

    The scales to sample at (average number of desired data points per partition)

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

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  19. final def wait(): Unit

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

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

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