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Details of sci-ml/pytorch:
Description: Tensors and Dynamic neural networks in Python with strong GPU acceleration
Homepage: https://pytorch.org/
available versions:
| releases | alpha | amd64 | arm | hppa | ia64 | mips | ppc | ppc64 | ppc macos | s390 | sh | sparc | x86 | USE-Flags | dependencies | ebuild warnings |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pytorch-2.13.0-r3 | - | ~ | - | - | - | - | - | - | - | - | - | - | - | cuda cusparselt distributed fbgemm flash gloo kineto memefficient mimalloc mkl mpi nccl nnpack +numpy onednn openblas opencl openmp qnnpack rocm xnnpack | show | WARNING: pytorch is being built with its default CUDA compute capabilities: 3.5 and 7.0. These may not be optimal for your GPU. To configure pytorch with the CUDA compute capability that is optimal for your GPU, set TORCH_CUDA_ARCH_LIST in your make.conf, and re-emerge pytorch. For example, to use CUDA capability 7.5 & 3.5, add: TORCH_CUDA_ARCH_LIST=7.5 3.5 For a Maxwell model GPU, an example value would be: TORCH_CUDA_ARCH_LIST=Maxwell You can look up your GPU's CUDA compute capability at https://developer.nvidia.com/cuda-gpus or by running /opt/cuda/extras/demo_suite/deviceQuery | grep 'CUDA Capability' |
| pytorch-2.12.0 | - | ~ | - | - | - | - | - | - | - | - | - | - | - | none | show | show |
+ stable
~ testing
- not available
some ebuild warning depend on specific use-flags or architectures, all ebuild-warnings are shown.