libtorch 2.12.0

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Dependencies

platform: x64 & !uwp

platform: arm64

platform: windows | (arm64 & !osx)

platform: linux | osx

vcpkg-cmake-config

vcpkg-get-python-packages

Used By

Not used by any other package

Features

blas - BLAS/LAPACK backend (openblas on linux/windows, Accelerate on macOS). On by default; disable with [core,...] when using [mkl] instead.

Dependencies

cuda - Build with CUDA GPU backend

Dependencies

features: [cuda]

platform: linux

platform: linux

features: [cuda]

platform: linux

dist - Use distributed training/inference (Gloo, MPI, libuv, TensorPipe)

Dependencies

platform: linux

platform: windows | osx

platform: linux

platform: linux

gflags - Build with gflags

Dependencies

glog - Build with glog

Dependencies

llvm - Build with LLVM

Dependencies

mkl - Intel performance backend: use oneMKL for BLAS/LAPACK and enable oneDNN (MKLDNN) CPU acceleration via ideep. Mutually exclusive with [blas] (the openblas default-feature) — install as libtorch[core,mkl,...] to skip the openblas backend, otherwise both backends get built and only MKL is linked.

Dependencies

vulkan - Build with Vulkan GPU backend

Dependencies

vulkan-memory-allocator

Available Versions

2.12.0#0
2.7.1#0
2.1.2#12
2.1.2#11
2.1.2#10
2.1.2#9
2.1.2#8
2.1.2#7
2.1.2#6
2.1.2#5
2.1.2#4
2.1.2#3
2.1.2#2
2.1.2#1
2.1.2#0
1.12.1#5
1.12.1#4
1.12.1#3
1.12.1#2
1.12.1#1
1.12.1#0

Port Content

This package supports the following triplets:

(windows & !static) | osx | linux

In classic mode, run the following vcpkg command:

 vcpkg install libtorch

In manifest mode, run the following vcpkg command in your project directory:

 vcpkg add port libtorch