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Details of sci-libs/caffe2:

Description: A deep learning framework
Homepage:
https://pytorch.org/

available versions:

releasesalphaamd64armhppaia64mipsppcppc64ppc macoss390shsparcx86USE-Flagsdependenciesebuild warnings
caffe2-2.2.2 -~-----------cuda
distributed
fbgemm
ffmpeg
gloo
mkl
mpi
nnpack
+numpy
onednn
openblas
opencl
opencv
openmp
qnnpack
rocm
xnnpack
show
WARNING: caffe2 is being built with its default CUDA compute capabilities: 3.5 and 7.0.
These may not be optimal for your GPU.

To configure caffe2 with the CUDA compute capability that is optimal for your GPU,
set TORCH_CUDA_ARCH_LIST in your make.conf, and re-emerge caffe2.
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'
show
caffe2-2.1.2-r7 -~-----------cuda
distributed
fbgemm
ffmpeg
gloo
mkl
mpi
nnpack
+numpy
onednn
openblas
opencl
opencv
openmp
qnnpack
rocm
tensorpipe
xnnpack
show
WARNING: caffe2 is being built with its default CUDA compute capabilities: 3.5 and 7.0.
These may not be optimal for your GPU.

To configure caffe2 with the CUDA compute capability that is optimal for your GPU,
set TORCH_CUDA_ARCH_LIST in your make.conf, and re-emerge caffe2.
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'
show
Legend:
+ stable
~ testing
- not available
some ebuild warning depend on specific use-flags or architectures, all ebuild-warnings are shown.

Esitmated compile time


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