portage-ng --info
>>> Printing information for: portage://sci-ml/caffe2-2.12.0-r3
----------------------------------------------------------------------------------------------------------------------------------------------------------------
description :
A deep learning framework
homepage :
https://pytorch.org/
maintainer :
tupone@gentoo.org
license :
BSD
eapi :
8
slot :
0
subslot :
[not set]
----------------------------------------------------------------------------------------------------------------------------------------------------------------
iuse :
cuda
cusparselt
distributed
fbgemm
flash
gloo
kineto
memefficient
mimalloc
mkl
mpi
nccl
nnpack
+numpy
onednn
openblas
opencl
openmp
qnnpack
rocm
xnnpack
python_single_target_python3_12
python_single_target_python3_13
python_single_target_python3_14
+amdgpu_targets_gfx908
+amdgpu_targets_gfx90a
+amdgpu_targets_gfx942
+amdgpu_targets_gfx1030
+amdgpu_targets_gfx1100
+amdgpu_targets_gfx1101
+amdgpu_targets_gfx1200
+amdgpu_targets_gfx1201
amdgpu_targets_gfx803
amdgpu_targets_gfx900
amdgpu_targets_gfx906
amdgpu_targets_gfx940
amdgpu_targets_gfx941
amdgpu_targets_gfx1010
amdgpu_targets_gfx1011
amdgpu_targets_gfx1012
amdgpu_targets_gfx1031
amdgpu_targets_gfx1102
amdgpu_targets_gfx1103
amdgpu_targets_gfx1150
amdgpu_targets_gfx1151
required_use :
one
python_single_target_python3_12
python_single_target_python3_13
python_single_target_python3_14
use mpi
distributed
use gloo
distributed
one[one]
cuda
rocm
use rocm
any
any
amdgpu_targets_gfx908
amdgpu_targets_gfx90a
amdgpu_targets_gfx942
amdgpu_targets_gfx1030
amdgpu_targets_gfx1100
amdgpu_targets_gfx1101
amdgpu_targets_gfx1200
amdgpu_targets_gfx1201
amdgpu_targets_gfx803
amdgpu_targets_gfx900
amdgpu_targets_gfx906
amdgpu_targets_gfx940
amdgpu_targets_gfx941
amdgpu_targets_gfx1010
amdgpu_targets_gfx1011
amdgpu_targets_gfx1012
amdgpu_targets_gfx1031
amdgpu_targets_gfx1102
amdgpu_targets_gfx1103
amdgpu_targets_gfx1150
amdgpu_targets_gfx1151
use memefficient
flash
use cusparselt
any
cuda
rocm
use flash
any
cuda
rocm
use memefficient
any
cuda
rocm
use nccl
rocm
keywords :
unstable(amd64)
unstable(arm64)
properties :
[not set]
eclasses :
eapi9-pipestatus
multiprocessing
toolchain-funcs
python-utils-r1
python-single-r1
flag-o-matic
ninja-utils
xdg-utils
cmake
cuda
prefix
rocm
defined_phases :
compile
configure
install
prepare
setup
test
installed :
[not set]
----------------------------------------------------------------------------------------------------------------------------------------------------------------
src_uri :
pytorch-2.12.0.tar.gz
use rocm
composable_kernel-7fe50dc3.tar.gz
use cuda
use flash
flash-attention-2.7.4.gh.tar.gz
use memefficient
flash-attention-2.7.4.gh.tar.gz
----------------------------------------------------------------------------------------------------------------------------------------------------------------
bdepend :
app-alternatives/ninja
>=dev-build/cmake-3.28.5
depend :
use python_single_target_python3_12
dev-lang/python:3.12
use python_single_target_python3_13
dev-lang/python:3.13
use python_single_target_python3_14
dev-lang/python:3.14
dev-cpp/abseil-cpp:=
dev-cpp/gflags:=
>=dev-cpp/glog-0.5.0:=
>=dev-libs/cpuinfo-2025.11.14
dev-libs/libfmt:=
dev-libs/protobuf:=
dev-libs/sleef
sci-ml/onnx
virtual/lapack
use cuda
dev-libs/cudnn
>=sci-ml/cudnn-frontend-1.12.0:=
>=dev-util/nvidia-cuda-toolkit-12.9:= [ profiler ]
use cusparselt
dev-libs/cusparselt
use fbgemm
wildcardsci-ml/FBGEMM-1.4*
use gloo
>=sci-ml/gloo-2025.06.04 [ cuda? rocm? ]
use kineto
~sci-ml/kineto-0.4.0_p20260323
use mimalloc
dev-libs/mimalloc
use mpi
virtual/mpi
use nnpack
sci-ml/NNPACK
dev-libs/pthreadpool
use numpy
use python_single_target_python3_12
dev-python/numpy [ python_targets_python3_12(-) ]
use python_single_target_python3_13
dev-python/numpy [ python_targets_python3_13(-) ]
use python_single_target_python3_14
dev-python/numpy [ python_targets_python3_14(-) ]
use onednn
sci-ml/oneDNN
use opencl
virtual/opencl
use qnnpack
[weak block] sci-libs/QNNPACK
sci-ml/gemmlowp
dev-libs/pthreadpool
use rocm
use nccl
>=dev-libs/rccl-6.3:=
<dev-libs/rccl-7.3:=
>=dev-util/hip-6.3:=
<dev-util/hip-7.3:=
>=dev-util/roctracer-6.3:=
<dev-util/roctracer-7.3:=
>=sci-libs/hipBLAS-6.3:=
<sci-libs/hipBLAS-7.3:= [ rocsolver(+) ]
>=sci-libs/hipBLASLt-6.3:=
<sci-libs/hipBLASLt-7.3:=
>=sci-libs/hipFFT-6.3:=
<sci-libs/hipFFT-7.3:=
>=sci-libs/hipRAND-6.3:=
<sci-libs/hipRAND-7.3:=
>=sci-libs/hipSOLVER-6.3:=
<sci-libs/hipSOLVER-7.3:=
>=sci-libs/hipSPARSE-6.3:=
<sci-libs/hipSPARSE-7.3:=
>=sci-libs/miopen-6.3:=
<sci-libs/miopen-7.3:=
>=sci-libs/rocBLAS-6.3:=
<sci-libs/rocBLAS-7.3:=
>=sci-libs/rocRAND-6.3:=
<sci-libs/rocRAND-7.3:=
>=sci-libs/rocSOLVER-6.3:=
<sci-libs/rocSOLVER-7.3:=
use memefficient
wildcardsci-libs/aotriton-bin-0.11*:=
use distributed
>=dev-util/rocm-smi-6.3:=
<dev-util/rocm-smi-7.3:=
>=dev-util/amdsmi-6.3:=
<dev-util/amdsmi-7.3:=
use cusparselt
>=sci-libs/hipsparselt-6.3:=
<sci-libs/hipsparselt-7.3:=
use distributed
use -rocm
sci-ml/tensorpipe [ cuda? ]
dev-cpp/cpp-httplib:=
use xnnpack
>=sci-ml/XNNPACK-2024.11
dev-libs/pthreadpool
use mkl
sci-libs/mkl
use openblas
sci-libs/openblas
dev-cpp/nlohmann_json
dev-libs/flatbuffers
dev-libs/FXdiv
dev-libs/pocketfft
dev-libs/psimd
sci-ml/FP16
use python_single_target_python3_12
<dev-python/pybind11-3.0.5 [ python_targets_python3_12(-) ]
dev-python/pyyaml [ python_targets_python3_12(-) ]
dev-python/typing-extensions [ python_targets_python3_12(-) ]
use python_single_target_python3_13
<dev-python/pybind11-3.0.5 [ python_targets_python3_13(-) ]
dev-python/pyyaml [ python_targets_python3_13(-) ]
dev-python/typing-extensions [ python_targets_python3_13(-) ]
use python_single_target_python3_14
<dev-python/pybind11-3.0.5 [ python_targets_python3_14(-) ]
dev-python/pyyaml [ python_targets_python3_14(-) ]
dev-python/typing-extensions [ python_targets_python3_14(-) ]
use cuda
>=dev-libs/cutlass-3.9.2 [ tools(+) ]
use onednn
sci-ml/ideep
use rocm
>=sci-libs/hipCUB-6.3:=
<sci-libs/hipCUB-7.3:=
>=sci-libs/rocPRIM-6.3:=
<sci-libs/rocPRIM-7.3:=
>=sci-libs/rocThrust-6.3:=
<sci-libs/rocThrust-7.3:=
use qnnpack
dev-libs/clog
idepend :
[not set]
rdepend :
use python_single_target_python3_12
dev-lang/python:3.12
use python_single_target_python3_13
dev-lang/python:3.13
use python_single_target_python3_14
dev-lang/python:3.14
dev-cpp/abseil-cpp:=
dev-cpp/gflags:=
>=dev-cpp/glog-0.5.0:=
>=dev-libs/cpuinfo-2025.11.14
dev-libs/libfmt:=
dev-libs/protobuf:=
dev-libs/sleef
sci-ml/onnx
virtual/lapack
use cuda
dev-libs/cudnn
>=sci-ml/cudnn-frontend-1.12.0:=
>=dev-util/nvidia-cuda-toolkit-12.9:= [ profiler ]
use cusparselt
dev-libs/cusparselt
use fbgemm
wildcardsci-ml/FBGEMM-1.4*
use gloo
>=sci-ml/gloo-2025.06.04 [ cuda? rocm? ]
use kineto
~sci-ml/kineto-0.4.0_p20260323
use mimalloc
dev-libs/mimalloc
use mpi
virtual/mpi
use nnpack
sci-ml/NNPACK
dev-libs/pthreadpool
use numpy
use python_single_target_python3_12
dev-python/numpy [ python_targets_python3_12(-) ]
use python_single_target_python3_13
dev-python/numpy [ python_targets_python3_13(-) ]
use python_single_target_python3_14
dev-python/numpy [ python_targets_python3_14(-) ]
use onednn
sci-ml/oneDNN
use opencl
virtual/opencl
use qnnpack
[weak block] sci-libs/QNNPACK
sci-ml/gemmlowp
dev-libs/pthreadpool
use rocm
use nccl
>=dev-libs/rccl-6.3:=
<dev-libs/rccl-7.3:=
>=dev-util/hip-6.3:=
<dev-util/hip-7.3:=
>=dev-util/roctracer-6.3:=
<dev-util/roctracer-7.3:=
>=sci-libs/hipBLAS-6.3:=
<sci-libs/hipBLAS-7.3:= [ rocsolver(+) ]
>=sci-libs/hipBLASLt-6.3:=
<sci-libs/hipBLASLt-7.3:=
>=sci-libs/hipFFT-6.3:=
<sci-libs/hipFFT-7.3:=
>=sci-libs/hipRAND-6.3:=
<sci-libs/hipRAND-7.3:=
>=sci-libs/hipSOLVER-6.3:=
<sci-libs/hipSOLVER-7.3:=
>=sci-libs/hipSPARSE-6.3:=
<sci-libs/hipSPARSE-7.3:=
>=sci-libs/miopen-6.3:=
<sci-libs/miopen-7.3:=
>=sci-libs/rocBLAS-6.3:=
<sci-libs/rocBLAS-7.3:=
>=sci-libs/rocRAND-6.3:=
<sci-libs/rocRAND-7.3:=
>=sci-libs/rocSOLVER-6.3:=
<sci-libs/rocSOLVER-7.3:=
use memefficient
wildcardsci-libs/aotriton-bin-0.11*:=
use distributed
>=dev-util/rocm-smi-6.3:=
<dev-util/rocm-smi-7.3:=
>=dev-util/amdsmi-6.3:=
<dev-util/amdsmi-7.3:=
use cusparselt
>=sci-libs/hipsparselt-6.3:=
<sci-libs/hipsparselt-7.3:=
use distributed
use -rocm
sci-ml/tensorpipe [ cuda? ]
dev-cpp/cpp-httplib:=
use xnnpack
>=sci-ml/XNNPACK-2024.11
dev-libs/pthreadpool
use mkl
sci-libs/mkl
use openblas
sci-libs/openblas
pdepend :
[not set]