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
  • BufferPair
  • CPU
  • CudaDevice
  • Device
  • DoublePrecision
  • EmptyMovable
  • FloatingPointPrecision
  • HalfPrecision
  • MPS
  • Movable
  • NcclUniqueId
  • STen
  • STenOptions
  • Scope
  • SinglePrecision
  • TensorHelpers

case object MPS extends Device with Product with Serializable

Linear Supertypes
Serializable, Product, Equals, Device, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. MPS
  2. Serializable
  3. Product
  4. Equals
  5. Device
  6. AnyRef
  7. Any
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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. def allocateBuffers[S](size: Long, options: STenOptions)(implicit arg0: Sc[S]): BufferPair
    Definition Classes
    Device
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  9. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  10. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  11. def measureTime[A](f: => A): (A, Long)
    Definition Classes
    MPS → Device
  12. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  14. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  15. def options[S](precision: FloatingPointPrecision)(implicit arg0: Sc[S]): STenOptions
    Definition Classes
    MPS → Device
  16. def productElementName(n: Int): String
    Definition Classes
    Product
  17. def productElementNames: Iterator[String]
    Definition Classes
    Product
  18. def setSeed(seed: Long): Unit
    Definition Classes
    MPS → Device
  19. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  20. def to(t: Tensor): Tensor
    Definition Classes
    MPS → Device
  21. def to[S](t: STenOptions)(implicit arg0: Sc[S]): STenOptions
    Definition Classes
    MPS → Device
  22. def to[S](t: STen)(implicit arg0: Sc[S]): STen
    Definition Classes
    Device
  23. 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. Data is copied via a buffer pair which consists of a source and a destinatin buffer. 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.

    Definition Classes
    Device
  24. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  25. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  26. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  27. def withOtherStream[A](synchronizeBefore: Boolean, synchronizeAfter: Boolean)(f: => A): A

    Executes f on a new stream

    Executes f on a new stream

    f must not switch to other threads

    Restores the stream to the original stream Optionally synchronizes the host before and/or after f

    Definition Classes
    Device
    Annotations
    @nowarn()

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Device

Inherited from AnyRef

Inherited from Any

Ungrouped