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Source: pytorch-cuda
Section: contrib/science
Homepage: https://pytorch.org/
Priority: optional
Standards-Version: 4.7.0
Vcs-Git: https://salsa.debian.org/deeplearning-team/pytorch.git
Vcs-Browser: https://salsa.debian.org/deeplearning-team/pytorch
Maintainer: Debian Deep Learning Team <debian-ai@lists.debian.org>
Uploaders: Mo Zhou <lumin@debian.org>
Rules-Requires-Root: no
Build-Depends: cmake,
debhelper-compat (= 13),
dh-exec,
dh-python,
googletest,
glslc,
libblas-dev,
libbenchmark-dev,
libcpuinfo-dev (>= 0.0~git20230113.6481e8b~),
libcpp-httplib-dev,
libdnnl-dev (>= 3.6.0~) [amd64 arm64 ppc64el],
libeigen3-dev,
libfmt-dev,
libfp16-dev (>= 0.0~git20200514.4dfe081~),
libflatbuffers-dev,
flatbuffers-compiler-dev,
libfxdiv-dev (>= 0.0~git20200417.b408327~),
libgloo-cuda-dev (>= 0.0~git20230519.597accf~) [amd64 arm64 ppc64el],
libideep-dev (>= 0.0~git20230825.6f4d653~) [amd64 arm64 ppc64el],
liblapack-dev,
llvm-19,
llvm-19-dev,
libnuma-dev,
libonnx-dev (>= 1.14.1~),
libprotobuf-dev,
libprotoc-dev,
libpsimd-dev (>= 0.0~git20200517.072586a~),
libpthreadpool-dev (>= 0.0~git20210507.1787867~),
libsleef-dev (>= 3.6.1-1~),
libsnappy-dev,
libtensorpipe-cuda-dev (>= 0.0~git20220513.bb1473a~),
libzstd-dev,
libvulkan-dev,
libvulkan-memory-allocator-dev,
libxnnpack-dev (>= 0.0~git20241108.4ea82e5~) [amd64 arm64],
nlohmann-json3-dev,
ninja-build,
protobuf-compiler,
pybind11-dev,
python3,
python3-dev,
python3-cffi,
python3-expecttest,
python3-numpy,
python3-onnx,
python3-pybind11,
python3-setuptools,
python3-yaml,
patchelf,
nvidia-cuda-toolkit,
nvidia-cuda-toolkit-gcc,
nvidia-cudnn (>= 8.7.0.84~cuda11.8),
libcudnn-frontend-dev (>= 0.9.2~),
libcutlass-dev (>= 3.4.1+ds-2),
libcub-dev,
libnccl-dev,
libcupti-dev,
lld,
Package: python3-torch-cuda
Section: contrib/python
Architecture: amd64 arm64 ppc64el
Depends: libtorch-cuda-2.6 (= ${binary:Version}),
${misc:Depends},
${python3:Depends},
${shlibs:Depends},
libtorch-cuda-test (= ${binary:Version}),
# PyTorch's JIT (C++ Extension) functionality needs development files/tools.
Recommends: libtorch-cuda-dev (= ${binary:Version}),
build-essential,
ninja-build,
pybind11-dev,
Suggests: python3-hypothesis, python3-pytest
Provides: ${python3:Provides}, python3-torch-api-2.6
Conflicts: python3-torch
Replaces: python3-torch
Description: Tensors and Dynamic neural networks in Python (Python Interface)
PyTorch is a Python package that provides two high-level features:
.
(1) Tensor computation (like NumPy) with strong GPU acceleration
(2) Deep neural networks built on a tape-based autograd system
.
You can reuse your favorite Python packages such as NumPy, SciPy and Cython
to extend PyTorch when needed.
.
This is the CUDA version of PyTorch (Python interface).
Package: libtorch-cuda-dev
Section: contrib/libdevel
Architecture: amd64 arm64 ppc64el
Depends: libtorch-cuda-2.6 (= ${binary:Version}),
python3-dev,
libprotobuf-dev,
${misc:Depends}
Conflicts: libtorch-dev
Replaces: libtorch-dev
Description: Tensors and Dynamic neural networks in Python (Development Files)
PyTorch is a Python package that provides two high-level features:
.
(1) Tensor computation (like NumPy) with strong GPU acceleration
(2) Deep neural networks built on a tape-based autograd system
.
You can reuse your favorite Python packages such as NumPy, SciPy and Cython
to extend PyTorch when needed.
.
This is the CUDA version of PyTorch (Development files).
Package: libtorch-cuda-2.6
Section: contrib/libs
Architecture: amd64 arm64 ppc64el
Multi-Arch: same
Depends: ${misc:Depends}, ${shlibs:Depends},
Recommends: libopenblas0 | libblis3 | libatlas3-base | libmkl-rt | libblas3,
Conflicts: libtorch1.13, libtorch-cuda-1.13,
libtorch2.0, libtorch-cuda-2.0,
libtorch2.1, libtorch-cuda-2.1,
libtorch2.4, libtorch-cuda-2.4,
libtorch2.5, libtorch-cuda-2.5,
libtorch2.6
Replaces: libtorch1.13, libtorch-cuda-1.13,
libtorch2.0, libtorch-cuda-2.0,
libtorch2.1, libtorch-cuda-2.1,
libtorch2.4, libtorch-cuda-2.4,
libtorch2.5, libtorch-cuda-2.5,
libtorch2.6
Description: Tensors and Dynamic neural networks in Python (Shared Objects)
PyTorch is a Python package that provides two high-level features:
.
(1) Tensor computation (like NumPy) with strong GPU acceleration
(2) Deep neural networks built on a tape-based autograd system
.
You can reuse your favorite Python packages such as NumPy, SciPy and Cython
to extend PyTorch when needed.
.
This is the CUDA version of PyTorch (Shared Objects).
Package: libtorch-cuda-test
Section: contrib/libs
Architecture: amd64 arm64 ppc64el
Depends: libtorch-cuda-2.6 (= ${binary:Version}), ${misc:Depends}, ${shlibs:Depends},
Conflicts: libtorch-test
Replaces: libtorch-test
Description: Tensors and Dynamic neural networks in Python (Test Binaries)
PyTorch is a Python package that provides two high-level features:
.
(1) Tensor computation (like NumPy) with strong GPU acceleration
(2) Deep neural networks built on a tape-based autograd system
.
You can reuse your favorite Python packages such as NumPy, SciPy and Cython
to extend PyTorch when needed.
.
This is the CUDA version of PyTorch (Test Binaries).
|
