Parents (Total: 10)

The searched file hash is included in 10 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	3B34138441C4DC8B67C553D0BB6C15F3
PackageArch	x86_64
PackageDescription	Numba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageName	python3-numba
PackageRelease	lp153.51.2
PackageVersion	0.53.0
SHA-1	1AF6D5089D4B71988629C4F977698EA631443119
SHA-256	C0DECFA83E87374DCBB381B5171E68F6759B8FEC440A7EC57DB81703EA3E12FF

Key	Value
MD5	FDF32091F5D9608DEF4E2A448F0A42BF
PackageArch	x86_64
PackageDescription	Numba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageName	python3-numba
PackageRelease	2.4
PackageVersion	0.53.0
SHA-1	FB8C7ED86516C7672D643ABFE57ABDDE095E3023
SHA-256	CCA0D6EE600DCC86211A9E5D1FF9746490E869CB5E1E28D1A54D9890480C4528

Key	Value
MD5	3F3BEF199A2278B2C7B6E42A041F24CE
PackageArch	x86_64
PackageDescription	Numba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageName	python3-numba
PackageRelease	51.5
PackageVersion	0.53.0
SHA-1	972B200DB42CAB92C8465B93430BF2A89B5D5CFB
SHA-256	17DB679828C6B8C69DD22DEFA2B46A86A7866A20A83589FAD93DDC6D6A2E4993

Key	Value
MD5	F1491B6928C228BB9E6F6CF484783A13
PackageArch	x86_64
PackageDescription	Numba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageName	python3-numba
PackageRelease	lp152.51.3
PackageVersion	0.53.0
SHA-1	C3E14DB8C1DD1D40AE7BEF4CE4D24D7B3976961E
SHA-256	EA16EEB05E8152AAAA0C469A21AC63DCC98A6198746D429330DF768412FB3C04

Key	Value
MD5	7B44B6B97973B7D0028A8ADE82549E80
PackageArch	x86_64
PackageDescription	Numba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageName	python3-numba
PackageRelease	2.4
PackageVersion	0.53.0
SHA-1	C87F54123B178BFF212EF80EA522C5DA695D1672
SHA-256	556288418380EC62E342B6ED7B05AB207DA3815CDC86E3611C405253F5FAEB1D

Key	Value
MD5	0D643032D24F18CEA37EC666F634607B
PackageArch	x86_64
PackageDescription	Numba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageName	python3-numba
PackageRelease	2.5
PackageVersion	0.53.0
SHA-1	883EC742C30659246F2BB61CA056D69CBF3B435D
SHA-256	0A0453492E56A2B0D14C300104CF0799A66734DA71534E400B32AEC380E111F6

Key	Value
MD5	D70EA79722238EADFB872F44B6A1E9C0
PackageArch	x86_64
PackageDescription	Numba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageName	python3-numba
PackageRelease	lp153.2.5
PackageVersion	0.53.0
SHA-1	3253B014B47FF0689926E07A511304DAB46336AD
SHA-256	747FF92B10647E7F5786C24596CA96587EEA8E13055C27E5748A8C8ABCA29107

Key	Value
MD5	DCBFC246B43931DC15CE79952EBB4407
PackageArch	x86_64
PackageDescription	Numba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageName	python3-numba
PackageRelease	51.3
PackageVersion	0.53.0
SHA-1	17B952067229CC3FCD88CAD669A603E60B4395B5
SHA-256	5A264DDF0E77D82DBFAF36A40B2AA9C28F93C0E130A8317FDBD533F827F8A0D0

Key	Value
MD5	A7446C10B9C2F58F4CA84A97D3CDCDD7
PackageArch	x86_64
PackageDescription	Numba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageName	python3-numba
PackageRelease	lp152.2.4
PackageVersion	0.53.0
SHA-1	C874D9A9E41E8FFC99786EDEA9664C37B7688D15
SHA-256	D857D6E5A3CB2EA99CFEBAB5B6D5FA1BED0F9BC03F9264128FE408FD5A85945E

Key	Value
MD5	AF765F3F63BFB27058677EE25F9B9E8B
PackageArch	x86_64
PackageDescription	Numba is a NumPy-aware optimizing compiler for Python. It uses the LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls, effectively removing the "interpreter", but not removing the dynamic indirection. Numba is also not a tracing JIT. It *compiles* your code before it gets run, either using run-time type information or type information you provide in the decorator. Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.
PackageName	python3-numba
PackageRelease	lp152.2.4
PackageVersion	0.53.0
SHA-1	49547D6D40D6BD13A779F81C9519BEA468CA201D
SHA-256	475F9880AA26015E5C571D947A2064B0D821A85D98F10C1A665ED924559569AF