Packages

  • package root
    Definition Classes
    root
  • 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
  • CPU
  • CudaDevice
  • Device
  • DoublePrecision
  • FloatingPointPrecision
  • HalfPrecision
  • Movable
  • STen
  • STenOptions
  • Scope
  • SinglePrecision
  • TensorHelpers
o

lamp

TensorHelpers

object TensorHelpers

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. TensorHelpers
  2. AnyRef
  3. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. Protected

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 clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. def device(t: Tensor): Product with Device with Serializable
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  9. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  10. def fromDoubleArray(arr: Array[Double], dim: Seq[Long], device: Device, precision: FloatingPointPrecision): Tensor
  11. def fromFloatArray(arr: Array[Float], dim: Seq[Long], device: Device): Tensor
  12. def fromFloatMat(m: Mat[Float], device: Device): Tensor
  13. def fromLongArray(arr: Array[Long], dim: Seq[Long], device: Device): Tensor
  14. def fromLongMat(m: Mat[Long], device: Device): Tensor
  15. def fromLongMat(m: Mat[Long], cuda: Boolean = false): Tensor
  16. def fromLongVec(m: Vec[Long], device: Device): Tensor
  17. def fromLongVec(m: Vec[Long], cuda: Boolean = false): Tensor
  18. def fromMat(m: Mat[Double], device: Device, precision: FloatingPointPrecision): Tensor
  19. def fromMat(m: Mat[Double], cuda: Boolean = false): Tensor
  20. def fromMatList(m: Seq[Mat[Double]], device: Device, precision: FloatingPointPrecision): Tensor
  21. def fromMatList(m: Seq[Mat[Double]], cuda: Boolean = false): Tensor
  22. def fromVec(m: Vec[Double], device: Device, precision: FloatingPointPrecision): Tensor
  23. def fromVec(m: Vec[Double], cuda: Boolean = false): Tensor
  24. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  25. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  26. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  27. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  28. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  29. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  30. def precision(t: Tensor): Option[Product with FloatingPointPrecision with Serializable]
  31. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  32. def toFloatMat(t0: Tensor): Mat[Float]
  33. def toFloatVec(t0: Tensor): Vec[Float]
  34. def toLongMat(t0: Tensor): Mat[Long]
  35. def toLongVec(t0: Tensor): Vec[Long]
  36. def toMat(t0: Tensor): Mat[Double]
  37. def toString(): String
    Definition Classes
    AnyRef → Any
  38. def toVec(t0: Tensor): Vec[Double]
  39. def unbroadcast(p: Tensor, desiredShape: List[Long]): Option[Tensor]
  40. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  41. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  42. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()

Inherited from AnyRef

Inherited from Any

Ungrouped