libtorchv2.13.0
- Created
- Updated
Tensors and Dynamic neural networks in Python with strong GPU acceleration
#License
#Supported Platforms
(windows & !static) | osx | linux
- ✅linux
- ✅osx
- ✅windows-dynamic
- ❌
uwp - ❌
windows-static
#Features
Default feature: blas
#blasdefault
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
Supports: (windows & x64 & !staticcrt) | (linux & x64)
- ✅linux
- ✅windows-x64-dynamic
- ❌
osx - ❌
windows-arm64-dynamic - ❌
windows-x86-dynamic
Dependencies:
#dist
Use distributed training/inference (Gloo, MPI, libuv, TensorPipe)
Supports: (linux & x64) | windows
- ✅linux
- ✅windows-dynamic
- ❌
osx
Dependencies:
#gflags
Build with gflags
#glog
Build with glog
#llvm
Build with LLVM
#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.
#vulkan
#Dependencies
and 18 transitive dependencies:
#Host Dependencies
#Dependents
No dependents.
#Host Dependents
No dependents.
#Contributors
PARK DongHa
jim wang
Lily Wang
MonicaLiu
Ómar Högni Guðmarsson
Alexander Neumann
Jia Yue Hua
Billy O'Neal
miyanyan
JoergAtGithub
Stefano Sinigardi
Mengna-Li
Steve Brain