c

com.microsoft.azure.synapse.ml.lightgbm

BulkPartitionTask

class BulkPartitionTask extends BasePartitionTask

Class for handling the execution of bulk-based Tasks on workers for each partition.

Linear Supertypes
BasePartitionTask, Logging, Serializable, Serializable, AnyRef, Any
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Inherited
  1. BulkPartitionTask
  2. BasePartitionTask
  3. Logging
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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Visibility
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Instance Constructors

  1. new BulkPartitionTask()

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def cleanupInternal(ctx: PartitionTaskContext): Unit

    Cleanup the task

    Cleanup the task

    ctx

    The training context.

    Attributes
    protected
    Definition Classes
    BasePartitionTask
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  7. def determineMatrixType(ctx: PartitionTaskContext, inputRows: Iterator[Row]): PeekingIterator[Row]
    Attributes
    protected
    Definition Classes
    BasePartitionTask
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. def generateFinalDatasetInternal(ctx: PartitionTaskContext, dataState: PartitionDataState, forValidation: Boolean, referenceDataset: Option[LightGBMDataset]): LightGBMDataset

    Generate the final dataset for this task.

    Generate the final dataset for this task. Internal implementation for specific execution modes.

    ctx

    The training context.

    dataState

    Any intermediate data state (used mainly by bulk execution mode).

    forValidation

    Whether to generate the final training dataset or the validation dataset.

    referenceDataset

    A reference dataset to start with (used mainly for validation dataset).

    returns

    LightGBM dataset Java wrapper.

    Attributes
    protected
    Definition Classes
    BulkPartitionTaskBasePartitionTask
  12. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  13. def getTaskContext(trainingCtx: TrainingContext, partitionId: Int, taskId: Long, measures: TaskInstrumentationMeasures, networkTopologyInfo: NetworkTopologyInfo, shouldExecuteTraining: Boolean, isEmptyPartition: Boolean, shouldReturnBooster: Boolean): PartitionTaskContext

    Initialize and customize the context for the task.

    Initialize and customize the context for the task.

    trainingCtx

    The training context information.

    partitionId

    The task context information.

    taskId

    The task context information.

    measures

    The task instrumentation measures.

    networkTopologyInfo

    Information about the network.

    shouldExecuteTraining

    Whether this task should participate in LightGBM training.

    isEmptyPartition

    Whether the partition has rows.

    shouldReturnBooster

    Whether the task should return a booster.

    returns

    The updated context information for the task.

    Attributes
    protected
    Definition Classes
    BasePartitionTask
  14. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  15. def initializeInternal(ctx: TrainingContext, shouldExecuteTraining: Boolean, isEmptyPartition: Boolean): Unit

    Initialize and customize the context for the task.

    Initialize and customize the context for the task.

    ctx

    The task context information.

    returns

    The updated context information for the task.

    Attributes
    protected
    Definition Classes
    BulkPartitionTaskBasePartitionTask
  16. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  17. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  18. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  19. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  20. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  21. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  22. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  23. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  24. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  25. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  26. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  27. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  28. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  29. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  30. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  31. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  32. def mapPartitionTask(ctx: TrainingContext)(inputRows: Iterator[Row]): Iterator[PartitionResult]

    This method will be passed to Spark's mapPartition method and handle execution of training on the workers.

    This method will be passed to Spark's mapPartition method and handle execution of training on the workers. Main stages: (and each execution mode has an "Internal" version to perform mode-specific operations) initialize() preparePartitionData() finalizeDatasetAndTrain() cleanup()

    ctx

    The training context.

    inputRows

    The Spark rows as an iterator.

    returns

    result iterator (to comply with Spark mapPartition API).

    Definition Classes
    BasePartitionTask
  33. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  34. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  35. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  36. def preparePartitionDataInternal(ctx: PartitionTaskContext, inputRows: Iterator[Row]): PartitionDataState

    Prepare any data objects for this particular partition.

    Prepare any data objects for this particular partition. Implement for specific execution modes.

    ctx

    The task context information.

    inputRows

    The Spark rows for a partition as an iterator.

    returns

    Any intermediate data state (used mainly by bulk execution mode) to pass to future stages.

    Attributes
    protected
    Definition Classes
    BulkPartitionTaskBasePartitionTask
  37. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  38. def toString(): String
    Definition Classes
    AnyRef → Any
  39. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  40. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  41. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()

Inherited from BasePartitionTask

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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