Source: pycuda Section: contrib/python Priority: optional Maintainer: Debian NVIDIA Maintainers Uploaders: Tomasz Rybak , Andreas Beckmann , Build-Depends: debhelper-compat (= 13), dh-sequence-numpy3, dh-sequence-python3, libboost-python-dev, libboost-thread-dev, mesa-common-dev, nvidia-cuda-toolkit, pybind11-dev, python3-all-dbg, python3-all-dev, python3-numpy, python3-pybind11, python3-pytools, python3-setuptools, Build-Depends-Indep: dh-sequence-sphinxdoc , python-mako-doc , python-numpy-doc , python3-doc , python3-sphinx (>= 1.0.7+dfsg) , Standards-Version: 4.5.1 Rules-Requires-Root: no Homepage: http://mathema.tician.de/software/pycuda Vcs-Browser: https://salsa.debian.org/nvidia-team/python-pycuda Vcs-Git: https://salsa.debian.org/nvidia-team/python-pycuda.git Package: python3-pycuda Architecture: any Multi-Arch: no Depends: nvidia-cuda-toolkit, python3-appdirs (>= 1.4.0), python3-decorator (>= 3.2.0), python3-numpy, python3-pytools, ${misc:Depends}, ${python3:Depends}, ${shlibs:Depends}, Recommends: python-pycuda-doc , python3-mako, Suggests: python3-matplotlib, python3-opengl, python3-pycuda-dbg, python3-pytest, Description: Python 3 module to access Nvidia‘s CUDA parallel computation API PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains Python 3 modules. Package: python3-pycuda-dbg Section: contrib/debug Architecture: any Multi-Arch: no Depends: python3-dbg, python3-pycuda (= ${binary:Version}), ${misc:Depends}, ${python3:Depends}, ${shlibs:Depends}, Description: Python 3 module to access Nvidia‘s CUDA API (debug extensions) PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains debug extensions for the Python 3 debug interpreter. Package: python-pycuda-doc Section: contrib/doc Architecture: all Multi-Arch: foreign Build-Profiles: Depends: fonts-mathjax, libjs-mathjax, ${misc:Depends}, ${sphinxdoc:Depends}, Recommends: nvidia-cuda-doc, python-mako-doc, python-numpy-doc, python3-doc, Suggests: python3-pycuda, Description: module to access Nvidia‘s CUDA computation API (documentation) PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist–so what’s so special about PyCUDA? * Object cleanup tied to lifetime of objects. This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. PyCUDA knows about dependencies, too, so (for example) it won’t detach from a context before all memory allocated in it is also freed. * Convenience. Abstractions like pycuda.driver.SourceModule and pycuda.gpuarray.GPUArray make CUDA programming even more convenient than with Nvidia’s C-based runtime. * Completeness. PyCUDA puts the full power of CUDA’s driver API at your disposal, if you wish. * Automatic Error Checking. All CUDA errors are automatically translated into Python exceptions. * Speed. PyCUDA’s base layer is written in C++, so all the niceties above are virtually free. * Helpful Documentation. . This package contains HTML documentation and example scripts.