Packages

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    Definition Classes
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  • package lamp

    Lamp provides utilities to build state of the art machine learning applications

    Lamp provides utilities to build state of the art machine learning applications

    Overview

    Notable types and packages:

    • lamp.STen is a memory managed wrapper around aten.ATen, an off the heap, native n-dimensionl array backed by libtorch.
    • lamp.autograd implements reverse mode automatic differentiation.
    • lamp.nn contains neural network building blocks, see e.g. lamp.nn.Linear.
    • lamp.data.IOLoops implements a training loop and other data related abstractions.
    • lamp.knn implements k-nearest neighbor search on the CPU and GPU
    • lamp.umap.Umap implements the UMAP dimension reduction algorithm
    • lamp.onnx implements serialization of computation graphs into ONNX format
    • lamp.io contains CSV and NPY readers
    How to get data into lamp

    Use one of the file readers in lamp.io or one of the factories in lamp.STen$.

    How to define a custom neural network layer

    See the documentation on lamp.nn.GenericModule

    How to compose neural network layers

    See the documentation on lamp.nn

    How to train models

    See the training loops in lamp.data.IOLoops

    Definition Classes
    root
  • package data
    Definition Classes
    lamp
  • TensorLogger

object TensorLogger extends Serializable

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Serializable, Serializable, AnyRef, Any
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  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  6. def detailAllTensorOptions(logger: (String) ⇒ Unit): Unit
  7. def detailAllTensors(logger: (String) ⇒ Unit): Unit
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean
    Definition Classes
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  10. def finalize(): Unit
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    protected[lang]
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    @throws( classOf[java.lang.Throwable] )
  11. def formatLine(data: TensorTraceData): String
  12. def formatStackTrace(data: TensorTraceData): String
  13. final def getClass(): Class[_]
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    @native()
  14. def hashCode(): Int
    Definition Classes
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    @native()
  15. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  16. def logTensorOptions(logger: (String) ⇒ Unit, filter: (TensorTraceData, Double) ⇒ Boolean, detailMinMs: Double, detailMaxMs: Double, detailNum: Int): Unit
  17. def logTensors(logger: (String) ⇒ Unit, filter: (TensorTraceData, Double) ⇒ Boolean, detailMinMs: Double, detailMaxMs: Double, detailNum: Int): Unit
  18. def makeLog(nanoTime: Long, data: Seq[TensorTraceData], filter: (TensorTraceData, Double) ⇒ Boolean, detailMinMs: Double, detailMaxMs: Double, detailNum: Int): String
  19. def makeStatistic(nanoTime: Long, actives: Seq[TensorTraceData])(filter: (TensorTraceData, Double) ⇒ Boolean): (List[(Double, Double, Int)], Seq[(TensorTraceData, Double)])
  20. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. final def notify(): Unit
    Definition Classes
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    @native()
  22. final def notifyAll(): Unit
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    @native()
  23. def queryActiveTensorOptions(): Vector[TensorTraceData]
  24. def queryActiveTensors(): Vector[TensorTraceData]
  25. def start(frequency: FiniteDuration = 5 seconds)(logger: (String) ⇒ Unit, filter: (TensorTraceData, Double) ⇒ Boolean, detailMinMs: Double, detailMaxMs: Double, detailNum: Int): TensorLogger
  26. final def synchronized[T0](arg0: ⇒ T0): T0
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  27. def toString(): String
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  28. final def wait(): Unit
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    @throws( ... )
  29. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  30. final def wait(arg0: Long): Unit
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