paddle

README

paddle

Go Inference Engine for PaddlePaddle.

Installation

1. Install paddle

   $ go get -u github.com/go-aie/paddle
   

2. Install Paddle Inference Go API

Documentation

Check out the documentation.

Known Issues

BLAS error

When using a high maxConcurrency (e.g. running benchmarks), sometimes you will get a BLAS error:

BLAS : Program is Terminated. Because you tried to allocate too many memory regions.

Solution

Set OpenBLAS to use a single thread:

export OPENBLAS_NUM_THREADS=1
export GOTO_NUM_THREADS=1
export OMP_NUM_THREADS=1

See also:

• https://github.com/autogluon/autogluon/issues/1020
• https://groups.google.com/g/cmu-openface/c/CwVFyKJPWP4

License

MIT

Documentation

Index

• type Engine
  • func NewEngine(model, params string, maxConcurrency int) *Engine
  • func (e *Engine) Infer(inputs []Tensor) (outputs []Tensor)
• type Matrix
  • func NewMatrix[E xslices.Number](r, c int, data []E) *Matrix[E]
  • func NewMatrixFromTensor[E xslices.Number](t Tensor) *Matrix[E]
  • func (m *Matrix[E]) Col(j int) []E
  • func (m *Matrix[E]) Cols() [][]E
  • func (m *Matrix[E]) Dims() (r, c int)
  • func (m *Matrix[E]) Equal(other *Matrix[E]) bool
  • func (m *Matrix[E]) Norm() *Matrix[E]
  • func (m *Matrix[E]) Pad(r, c int, v E) *Matrix[E]
  • func (m *Matrix[E]) RawData() []E
  • func (m *Matrix[E]) Row(i int) []E
  • func (m *Matrix[E]) Rows() [][]E
  • func (m *Matrix[E]) Set(rowStart, rowEnd, colStart, colEnd int, value E) *Matrix[E]
  • func (m *Matrix[E]) SetAll(value E) *Matrix[E]
  • func (m *Matrix[E]) SetAllFunc(f func(E) E) *Matrix[E]
  • func (m *Matrix[E]) SetFunc(rowStart, rowEnd, colStart, colEnd int, f func(E) E) *Matrix[E]
• type PredictorPool
  • func NewPredictorPool(config *paddle.Config, size int) *PredictorPool
  • func (p *PredictorPool) Get() (predictor *paddle.Predictor, put func())
• type Tensor
  • func NewTensorFromFourDimSlice[E any](slice [][][][]E) Tensor
  • func NewTensorFromOneDimSlice[E any](slice []E) Tensor
  • func NewTensorFromThreeDimSlice[E any](slice [][][]E) Tensor
  • func NewTensorFromTwoDimSlice[E any](slice [][]E) Tensor
• type TypedTensor
  • func NewTypedTensor[E xslices.Number](t Tensor) TypedTensor[E]

Types

type Engine

type Engine struct {
	// contains filtered or unexported fields
}

Engine is an inference engine.

func NewEngine

func NewEngine(model, params string, maxConcurrency int) *Engine

func (*Engine) Infer

func (e *Engine) Infer(inputs []Tensor) (outputs []Tensor)

type Matrix

type Matrix[E xslices.Number] struct {
	// contains filtered or unexported fields
}

func NewMatrix

func NewMatrix[E xslices.Number](r, c int, data []E) *Matrix[E]

func NewMatrixFromTensor

func NewMatrixFromTensor[E xslices.Number](t Tensor) *Matrix[E]

func (*Matrix[E]) Col

func (m *Matrix[E]) Col(j int) []E

func (*Matrix[E]) Cols

func (m *Matrix[E]) Cols() [][]E

func (*Matrix[E]) Dims

func (m *Matrix[E]) Dims() (r, c int)

func (*Matrix[E]) Equal

func (m *Matrix[E]) Equal(other *Matrix[E]) bool

Equal implements the Equal method which will be used by go-cmp to determine equality.

func (*Matrix[E]) Norm

func (m *Matrix[E]) Norm() *Matrix[E]

func (*Matrix[E]) Pad

func (m *Matrix[E]) Pad(r, c int, v E) *Matrix[E]

Pad creates a new matrix, which expands the receiver m by r rows and c columns, and sets each expanded element to v.

func (*Matrix[E]) RawData

func (m *Matrix[E]) RawData() []E

func (*Matrix[E]) Row

func (m *Matrix[E]) Row(i int) []E

func (*Matrix[E]) Rows

func (m *Matrix[E]) Rows() [][]E

func (*Matrix[E]) Set

func (m *Matrix[E]) Set(rowStart, rowEnd, colStart, colEnd int, value E) *Matrix[E]

func (*Matrix[E]) SetAll

func (m *Matrix[E]) SetAll(value E) *Matrix[E]

func (*Matrix[E]) SetAllFunc

func (m *Matrix[E]) SetAllFunc(f func(E) E) *Matrix[E]

func (*Matrix[E]) SetFunc

func (m *Matrix[E]) SetFunc(rowStart, rowEnd, colStart, colEnd int, f func(E) E) *Matrix[E]

type PredictorPool

type PredictorPool struct {
	// contains filtered or unexported fields
}

PredictorPool is a predictor pool for concurrent inferences.

See also: - https://github.com/PaddlePaddle/Paddle/issues/17288 - https://www.paddlepaddle.org.cn/inference/master/guides/performance_tuning/multi_thread.html

func NewPredictorPool

func NewPredictorPool(config *paddle.Config, size int) *PredictorPool

func (*PredictorPool) Get

func (p *PredictorPool) Get() (predictor *paddle.Predictor, put func())

type Tensor

type Tensor struct {
	Shape []int32
	Data  interface{}
}

func NewTensorFromFourDimSlice

func NewTensorFromFourDimSlice[E any](slice [][][][]E) Tensor

func NewTensorFromOneDimSlice

func NewTensorFromOneDimSlice[E any](slice []E) Tensor

func NewTensorFromThreeDimSlice

func NewTensorFromThreeDimSlice[E any](slice [][][]E) Tensor

func NewTensorFromTwoDimSlice

func NewTensorFromTwoDimSlice[E any](slice [][]E) Tensor

type TypedTensor

type TypedTensor[E xslices.Number] struct {
	Shape []int32
	Data  []E
}

func NewTypedTensor

func NewTypedTensor[E xslices.Number](t Tensor) TypedTensor[E]