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Source: pytorch
Section: science
Homepage: https://pytorch.org/
Priority: optional
Standards-Version: 4.6.0.1
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 (= 12),
dh-exec,
dh-python,
googletest,
libasio-dev,
libavcodec-dev,
libblas-dev,
libbenchmark-dev,
libcpuinfo-dev,
libdnnl-dev [amd64 arm64 ppc64el],
libeigen3-dev,
libfmt-dev,
libfp16-dev,
libflatbuffers-dev,
flatbuffers-compiler-dev,
libfxdiv-dev,
libgflags-dev,
libgloo-dev [amd64 arm64 ppc64el mips64el s390x],
libgoogle-glog-dev,
libideep-dev (>=0.0~git20220817.77d662b-1~) [amd64 arm64 ppc64el],
liblapack-dev,
libleveldb-dev,
liblmdb-dev,
libnop-dev,
libnuma-dev,
libonnx-dev (>= 1.7.0+dfsg-3),
libopencv-dev,
libprotobuf-dev,
libprotoc-dev,
libpsimd-dev,
libpthreadpool-dev,
libsleef-dev,
libsnappy-dev,
libtensorpipe-dev,
libxnnpack-dev [amd64 arm64],
libzmq3-dev,
libzstd-dev,
ninja-build,
ocl-icd-opencl-dev,
protobuf-compiler,
pybind11-dev,
python3,
python3,
python3-dev,
python3-cffi,
python3-distutils,
python3-numpy,
python3-onnx,
python3-pybind11,
python3-setuptools,
python3-yaml
Package: python3-torch
Section: python
Architecture: amd64 arm64 mips64el ppc64el s390x kfreebsd-amd64 riscv64
Depends: libtorch1.13 (= ${binary:Version}),
${misc:Depends},
${python3:Depends},
${shlibs:Depends},
libtorch-test (= ${binary:Version}),
# PyTorch's JIT (C++ Extension) functionality needs development files/tools.
Recommends: libtorch-dev (= ${binary:Version}), build-essential, ninja-build,
pybind11-dev,
Suggests: python3-hypothesis, python3-pytest
Provides: ${python3:Provides}
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 CPU-only version of PyTorch and Caffe2 (Python interface).
Package: libtorch-dev
Section: libdevel
Architecture: amd64 arm64 mips64el ppc64el s390x kfreebsd-amd64 riscv64
Depends: libgflags-dev,
libgoogle-glog-dev,
libtorch1.13 (= ${binary:Version}),
python3-dev,
libprotobuf-dev,
${misc:Depends}
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 CPU-only version of PyTorch and Caffe2 (Development files).
Package: libtorch1.13
Section: libs
Architecture: amd64 arm64 mips64el ppc64el s390x kfreebsd-amd64 riscv64
Multi-Arch: same
Depends: ${misc:Depends}, ${shlibs:Depends},
Recommends: libopenblas0 | libblis3 | libatlas3-base | libmkl-rt | libblas3,
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 CPU-only version of PyTorch and Caffe2 (Shared Objects).
Package: libtorch-test
Architecture: amd64 arm64 mips64el ppc64el s390x kfreebsd-amd64 riscv64
Depends: libtorch1.13 (= ${binary:Version}), ${misc:Depends}, ${shlibs:Depends},
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 CPU-only version of PyTorch and Caffe2 (Test Binaries).
|
