Packages

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    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
  • Movable
  • STen
  • STenOptions
  • Scope
  • SinglePrecision
  • TensorHelpers
t

lamp

FloatingPointPrecision

sealed trait FloatingPointPrecision extends AnyRef

Linear Supertypes
AnyRef, Any
Known Subclasses
DoublePrecision, SinglePrecision
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Abstract Value Members

  1. abstract def convertOption[S](t: STenOptions)(implicit arg0: Sc[S]): STenOptions
  2. abstract def convertTensor(t: Tensor): Tensor

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
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