data-efficient-ml / ThArrays.jl Licence: MIT license
A Julia interface for PyTorch's C++ backend, focusing on Tensor, AD, and JIT
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ThArrays
A Julia interface for PyTorch's C++ backend.
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
Install the package: ] add ThArrays
Read the docs here , or
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.
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