Parents (Total: 26)

The searched file hash is included in 26 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	314760
MD5	7BF0E78717DA26E6338F7BD64DFF900B
PackageDescription	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.
PackageMaintainer	Debian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageName	python3-pycuda
PackageSection	contrib/python
PackageVersion	2018.1.1-3
SHA-1	194237D7D664B504399F5E9BEB56DA0152F10DE1
SHA-256	0FEACB25B38F54A02704784F4224BB36C6531EEC2F66E69507FBA85570A04353

Key	Value
FileSize	466804
MD5	DA783AD042E8BC1B24DD7F017C98259F
PackageDescription	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.
PackageMaintainer	Debian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageName	python3-pycuda
PackageSection	contrib/python
PackageVersion	2018.1.1-4+b1
SHA-1	216F72108B66361221097E375E4CD57619E47A32
SHA-256	7A9AE4FC9D93F552529DD8A9DA555A4C27DAC0151B8581BE57D7E041E349A221

Key	Value
FileSize	315996
MD5	9DAD2B920C5F38AE16418FC825678E96
PackageDescription	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.
PackageMaintainer	Debian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageName	python3-pycuda
PackageSection	contrib/python
PackageVersion	2018.1.1-3+b1
SHA-1	2412174B9D481D80C4933275CF9CBD46E43E7B01
SHA-256	60FF2DA05EC2E8335D75BECDCFC5995F3A6FBAA1B7CE00713F30312582B9A4E4

Key	Value
FileSize	315604
MD5	6D7EDAD4A85900295C8656C15C55898B
PackageDescription	Python 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.
PackageMaintainer	Debian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageName	python-pycuda
PackageSection	contrib/python
PackageVersion	2018.1.1-3
SHA-1	4EF4ADCC6E4220EFE21307798BDCFF45E7722ABD
SHA-256	A84B96264AAFE4114348062D6CA1C01661D2B1C3079FA0EBFD6981F3A0794D5A

Key	Value
MD5	BA6ED9E2057F04E33B7203B93019EE5A
PackageArch	x86_64
PackageDescription	PyCUDA lets you access NVIDIA's CUDA parallel computation API from Python 3.
PackageMaintainer	daviddavid <daviddavid>
PackageName	python3-pycuda
PackageRelease	2.mga7.nonfree
PackageVersion	2018.1.1
SHA-1	514707E56A9270BFCE760C39DEDB47B5DDBBA4A9
SHA-256	FF83A7F9CDD3C11E94B62A7E8657B497D1181F7C4C4201B9BE53E3E2C3122A99

Key	Value
MD5	24A50AC218C6D868AEA50CE43DC85755
PackageArch	x86_64
PackageDescription	PyCUDA lets you access NVIDIA's CUDA parallel computation API from Python 3.
PackageMaintainer	wally <wally>
PackageName	python3-pycuda
PackageRelease	7.mga8.nonfree
PackageVersion	2019.1.2
SHA-1	5AD469F0EB4A115315A610C8B8E461D0D340F3CA
SHA-256	0F5936855090EA7EFFAA70C80127B3BE578896F254929658D3ED970F1E179FBC

Key	Value
FileSize	319636
MD5	12B0F72FA0DD4D4F5917C93FDDB849FD
PackageDescription	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.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python3-pycuda
PackageSection	contrib/python
PackageVersion	2018.1.1-4build3
SHA-1	5B6B1750FCBE9A57B1F6EB243119376976F12806
SHA-256	825FD1875E492CEDCD406277A8604BECFE0D7918A2B860445F76084BF5F6DD38

Key	Value
FileSize	324516
MD5	243CCEF4C9E154C6A489BFBE0BD7DE2F
PackageDescription	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.
PackageMaintainer	Debian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageName	python3-pycuda
PackageSection	contrib/python
PackageVersion	2018.1.1-4+b3
SHA-1	5CB7C1DC9306BF10D8A8AB8F486876259448B62A
SHA-256	3FBC78D464995A76A71EA5F6B5E4A9E8B74BA4F3FBCA33B29236C8520C78A1F2

Key	Value
FileSize	324172
MD5	95571647075E84C6DEAE2BF9F998EAE1
PackageDescription	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.
PackageMaintainer	Debian NVIDIA Maintainers <pkg-nvidia-devel@lists.alioth.debian.org>
PackageName	python3-pycuda
PackageSection	contrib/python
PackageVersion	2020.1~dfsg1-1
SHA-1	6D7B76D4A0C3460CDB396C06BCAF2B7BE741D316
SHA-256	EE0DD23354FC91FF5644058F857FA3492331625C53180DE417FE4863AABD9556

Key	Value
FileSize	308540
MD5	673F6253F066B09703945114347D9A4D
PackageDescription	Python 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.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python-pycuda
PackageSection	contrib/python
PackageVersion	2017.1.1-2
SHA-1	7392023424C20835E5B3E622CAB5582789A99E8D
SHA-256	BB372B58FA1DD3AEB11E70BCB920A2018A1C93FBF533D4703BB9123DE1D818B1