ThArrays.jl

A Julia interface for PyTorch's C++ backend, focusing on Tensor, AD, and JIT
Author TuringLang
Popularity
22 Stars
Updated Last
1 Year Ago
Started In
February 2020

ThArrays

A Julia interface for PyTorch's C++ backend.

Unit Testing

Features

  • ThArrays.Tensor: PyTorch Tensor as an Array-like data type in Julia
  • ThArrays.ThAD: AD using PyTorch C++ backend
  • ThArrays.TrackerAD: AD using Tracker.jl and PyTorch C++ backend mixed, on your choice
  • ThArrays.ThJIT: using TorchScript in Julia

Getting Started

  1. Install the package: ] add ThArrays

  2. Read the docs here, or

  3. Experiment in the Julia REPL directly:

     julia> using ThArrays
    
     julia> t = Tensor( -rand(3, 3) )
     PyTorch.Tensor{Float64, 2}:
     -0.1428 -0.7099 -0.1446
     -0.3447 -0.0686 -0.8287
     -0.2692 -0.0501 -0.2092
     [ CPUDoubleType{3,3} ]
    
     julia> sin(t)^2 + cos(t)^2
     PyTorch.Tensor{Float64, 2}:
      1.0000  1.0000  1.0000
      1.0000  1.0000  1.0000
      1.0000  1.0000  1.0000
     [ CPUDoubleType{3,3} ]
    
     julia> ThAD.gradient(x->sum(sin(x)+x^2), rand(3,3))
     (PyTorch.Tensor{Float64, 2}:
      2.3776  1.5465  2.0206
      1.2542  1.2081  2.1156
      2.1034  1.1568  2.2599
     [ CPUDoubleType{3,3} ]
     ,)
    
     julia>
    

    You can find more examples under the test directory.

Required Packages

Used By Packages

No packages found.