DistriOptimizer

Type Members

abstract class Cache[T] extends AnyRef

Optimizer cache some metadata on each executor
Optimizer cache some metadata on each executor
T
Tensor element type
case class CacheV1[T](localModels: Array[Module[T]], modelWeights: Array[Tensor[T]], modelGradients: Array[Tensor[T]], localCriterions: Array[Criterion[T]], localStates: Array[Table], moduleTimeList: Array[Long] = null, localMethods: Array[Option[Array[ValidationMethod[T]]]], optimMethods: Map[String, OptimMethod[T]], parameterSynchronizer: DistriParameterSynchronizer[T] = null) extends Cache[T] with Product with Serializable

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
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
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
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
parameters
AllReduceParameter
trainingModel
the model is trained by optimizer
returns
trained model

Attributes
protected
Definition Classes
DistriOptimizer → AbstractOptimizer
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val logger: Logger
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
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
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
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
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: class DistriOptimizer | package optim

object DistriOptimizer extends AbstractOptimizer

Type Members

abstract class Cache[T] extends AnyRef

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

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

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

val logger: Logger

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from AbstractOptimizer

Inherited from AnyRef

Inherited from Any

Ungrouped