sealed trait Device extends AnyRef
Represents a device where tensors are stored and tensor operations are executed
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- Device
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Abstract Value Members
- abstract def measureTime[A](f: ⇒ A): (A, Long)
- abstract def options[S](precision: FloatingPointPrecision)(implicit arg0: Sc[S]): STenOptions
- abstract def setSeed(seed: Long): Unit
- abstract def to[S](t: STenOptions)(implicit arg0: Sc[S]): STenOptions
- abstract def to(t: Tensor): Tensor
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abstract
def
withOtherStreamThenSync[A](synchronizeBefore: Boolean)(f: ⇒ A): A
Executes f on a new stream f must not switch to other threads Restores the stream to the default stream
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!=(arg0: Any): Boolean
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def
##(): Int
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def
==(arg0: Any): Boolean
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- def allocateBuffers[S](size: Long, options: STenOptions)(implicit arg0: Sc[S]): BufferPair
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final
def
asInstanceOf[T0]: T0
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clone(): AnyRef
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eq(arg0: AnyRef): Boolean
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isInstanceOf[T0]: Boolean
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ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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def
notifyAll(): Unit
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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- def to[S](t: STen)(implicit arg0: Sc[S]): STen
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def
toBatched[S](tensors: Seq[STen], buffers: BufferPair)(implicit arg0: Sc[S]): Seq[STen]
Copies tensors to this device in a single cross device copy.
Copies tensors to this device in a single cross device copy. srcBuffer is supposed to be on the source device. dstBuffer has to be on
this
device. Tensors are first copied to the srcBuffer, then the srcBuffer is copied to dstBuffer, then the dstBuffer is split into views.All tensors must have the same data type.
Might make sense to pin the srcBuffer.
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def
toString(): String
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final
def
wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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wait(): Unit
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finalize(): Unit
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