|
- Numba documentation — Numba 0. 52. 0. dev0+274. g626b40e-py3. 7-linux-x86_64 . . .
Numba documentation ¶ This is the Numba documentation Unless you are already acquainted with Numba, we suggest you start with the User manual
- Supported Python features — Numba 0. 52. 0. dev0+274. g626b40e . . . - PyData
Improving the string performance is an ongoing task, but the speed of CPython is unlikely to be surpassed for basic string operation in isolation Numba is most successfully used for larger algorithms that happen to involve strings, where basic string operations are not the bottleneck
- Supported NumPy features — Numba 0. 52. 0. dev0+274. g626b40e-py3. 7-linux . . .
Numba excels at generating code that executes on top of NumPy arrays NumPy support in Numba comes in many forms: Numba understands calls to NumPy ufuncs and is able to generate equivalent native code for many of them NumPy arrays are directly supported in Numba
- Parallel Range — numba 0. 11. 0 documentation - PyData
Numba implements the ability to run loops in parallel, similar to OpenMP parallel for loops and Cython’s prange The loops body is scheduled in seperate threads, and they execute in a nopython numba context prange automatically takes care of data privatization and reductions:
- Installation — Numba 0. 52. 0. dev0+274. g626b40e-py3. 7-linux-x86_64. egg . . .
We are now uploading packages to the numba channel on Anaconda Cloud for 32-bit little-endian, ARMv7-based boards, which currently includes the Raspberry Pi 2 and 3, but not the Pi 1 or Zero
- NumPy and numba — numba 0. 12. 0 documentation - PyData
Numba generated code will evaluate the full expression in one go, for each element The numba approach approach avoids having temporal intermmediate arrays built, as well as avoiding revisiting operands that are being used more than once in a expression
|
|
|