Package spidr - Namespaces

• BayesianOptimization

  Functionality for Bayesian optimization, the data-efficient optimization of objective functions. Bayesian optimization recommends new points at which to query your objective by placing a probabilistic model over historic data then, typically, optimizing an _acquisition function_ which quantifies how useful it would be to evaluate the objective at any given set of points.
• BayesianOptimization.Acquisition

  Acquisition functionality for Bayesian optimization. Acquisition functions quantify the usefulness of points in a search domain, towards the task of finding a function optimum.
• Compiler.Enzyme.MLIR.Dialect.Dialect

  For internal spidr use only.
• Compiler.Enzyme.MLIR.Implementations.CoreDialectsAutoDiffImplementations

  For internal spidr use only.
• Compiler.Enzyme.MLIR.Passes.Passes

  For internal spidr use only.
• Compiler.EnzymeJAX.Src.EnzymeAD.JAX.Implementations.XLADerivatives

  For internal spidr use only.
• Compiler.EnzymeJAX.Src.EnzymeAD.JAX.Passes.Passes

  For internal spidr use only.
• Compiler.LLVM.Support.RawOStream

  For internal spidr use only.
• Compiler.MLIR.Dialect.Func.IR.FuncOps

  For internal spidr use only.
• Compiler.MLIR.IR

  For internal spidr use only.
• Compiler.MLIR.IR.Block

  For internal spidr use only.
• Compiler.MLIR.IR.Builders

  For internal spidr use only.
• Compiler.MLIR.IR.BuiltinAttributes

  For internal spidr use only.
• Compiler.MLIR.IR.BuiltinLocationAttributes

  For internal spidr use only.
• Compiler.MLIR.IR.BuiltinOps

  For internal spidr use only.
• Compiler.MLIR.IR.BuiltinTypeInterfaces

  For internal spidr use only.
• Compiler.MLIR.IR.BuiltinTypes

  For internal spidr use only.
• Compiler.MLIR.IR.DialectRegistry

  For internal spidr use only.
• Compiler.MLIR.IR.Location

  For internal spidr use only.
• Compiler.MLIR.IR.MLIRContext

  For internal spidr use only.
• Compiler.MLIR.IR.Operation

  For internal spidr use only.
• Compiler.MLIR.IR.SymbolTable

  For internal spidr use only.
• Compiler.MLIR.IR.TypeRange

  For internal spidr use only.
• Compiler.MLIR.IR.Types

  For internal spidr use only.
• Compiler.MLIR.IR.ValueRange

  For internal spidr use only.
• Compiler.MLIR.IR.Value

  For internal spidr use only.
• Compiler.MLIR.Pass.PassManager

  For internal spidr use only.
• Compiler.MLIR.Pass.PassRegistry

  For internal spidr use only.
• Compiler.MLIR.Transforms.Passes

  For internal spidr use only.
• Compiler.Stablehlo.Dialect.Register

  For internal spidr use only.
• Compiler.Stablehlo.Dialect.StablehloOps

  For internal spidr use only.
• Compiler.Xla.Client.ExecutableBuildOptions

  For internal spidr use only.
• Compiler.Xla.HLO.Builder.Lib.Arithmetic

  For internal spidr use only.
• Compiler.Xla.HLO.Builder.Lib.Constants

  For internal spidr use only.
• Compiler.Xla.HLO.Builder.Lib.Math

  For internal spidr use only.
• Compiler.Xla.HLO.Builder.Lib.Matrix

  For internal spidr use only.
• Compiler.Xla.HLO.Builder.Lib.PRNG

  For internal spidr use only.
• Compiler.Xla.HLO.Builder.XlaBuilder

  For internal spidr use only.
• Compiler.Xla.HLO.Builder.XlaComputation

  For internal spidr use only.
• Compiler.Xla.HLO.Translate.StableHLO

  For internal spidr use only.
• Compiler.Xla.MLIRHLO.MHLO.IR.Register

  For internal spidr use only.
• Compiler.Xla.PJRT.C.PjrtCApi

  For internal spidr use, and use by plugin developers. The Idris API for PJRT.
• Compiler.Xla.PJRT.PjrtExecutable

  For internal spidr use only.
• Compiler.Xla.Service.HloProto

  For internal spidr use only.
• Compiler.Xla.Literal

  For internal spidr use only.
• Compiler.Xla.Shape

  For internal spidr use only.
• Compiler.Xla.ShapeUtil

  For internal spidr use only.
• Compiler.Xla.XlaData

  For internal spidr use only.
• Compiler.Eval

  For internal spidr use only.
• Compiler.Expr

  For internal spidr use only.
• Compiler.FFI

  For internal spidr use only.
• Compiler.LiteralRW

  For internal spidr use only.
• Constants

  Common tensor constants.
• Data

  Definitions and utilities for datasets.
• Device

  Defines `Device`, which encapsulates the graph compiler and hardware support.
• Distribution

  Probability distributions.
• Literal

  Defines `Literal`, a single value or array of values with a specified shape. `Literal` is similar to `Tensor`, but differs in a number of important ways: * `Literal` offers a convenient syntax for constructing `Literal`s with boolean and numeric contents. For example, `True`, `1` and `[1, 2, 3]` are all valid `Literal`s. This makes it useful for constructing `Tensor`s. * Operations on `Literal` are *not* accelerated by a graph compiler, so operations on large `Literal`s, and large sequences of operations on any `Literal`, can be expected to be slower than they would on an equivalent `Tensor`. * `Literal` is implemented in pure Idris. As such, values can contain elements of any type, and implements a number of standard Idris interfaces. This, along with its convenient syntax, makes it particularly useful for testing operations on `Tensor`s.
• Model

  Functionality for probabilistic models.
• Model.GaussianProcess

  Gaussian process inference.
• Model.Kernel

  Kernel functions, particularly for use in Gaussian processes.
• Model.MeanFunction

  Mean functions, particularly for use in Gaussian processes.
• Optimize

  Function optimizers.
• Primitive

  Primitive backend data types, and their interaction with Idris. This module contains a number of interfaces (`Primitive.Num`, `Primitive.Eq` etc.). These indicate what operations can be performed on primitive data in the backend. They are entirely distinct from the Idris interfaces `Prelude.Num` etc. but carry largely the same meaning. For example, primitive types satisfying `Primitive.Ord` have a notion of ordering.
• Tensor

  Defines `Tensor`, an array of numbers or booleans, along with a number of functions operating on `Tensor`s. `Tensor` operations typically leverage hardware acceleration and graph compilation. spidr tracks tensor shape and data type in the types, so you can be sure that if your tensor code compiles, these are consistent. spidr achieves efficient reuse of tensor computations with `Tag`. See the tutorial _Nuisances in the Tensor API_ for a discussion of pitfalls to avoid when using `Tag`.
• Types

  Common library types.
• Util

  Generic library utilities.