This python module allows us to speed up applications with high-performance functions written directly in Python.
The numba python module works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically.
The code can be just-in-time compiled to native machine instructions, similar in performance to C, C++ and Fortran.
For the installation I used the pip tool:
C:\Python27>cd Scripts
C:\Python27\Scripts>pip install numba
Collecting numba
Downloading numba-0.35.0-cp27-cp27m-win32.whl (1.4MB)
100% |################################| 1.4MB 497kB/s
...
Installing collected packages: singledispatch, funcsigs, llvmlite, numba
Successfully installed funcsigs-1.0.2 llvmlite-0.20.0 numba-0.35.0 singledispatch-3.4.0.3
C:\Python27\Scripts>pip install numpy
Requirement already satisfied: numpy in c:\python27\lib\site-packages
The example source code is:
from numba import jit
from numpy import arange
# jit decorator tells Numba to compile this function.
# The argument types will be inferred by Numba when function is called.
@jit
def sum2d(arr):
M, N = arr.shape
result = 0.0
for i in range(M):
for j in range(N):
result += arr[i,j]
return result
a = arange(9).reshape(3,3)
print(sum2d(a))C:\Python27>python.exe numba_test_001.py
36.0import numba
from numba import jit
def fibonacci(n):
a, b = 1, 1
for i in range(n):
a, b = a+b, a
return a
print fibonacci(10)
fibonacci_jit = jit(fibonacci)
print fibonacci_jit(14)@jit
def fibonacci_jit(n):
a, b = 1, 1
for i in range(n):
a, b = a+b, a
return aFirst, compiling takes time, but will work especially for small functions.
The Numba python module tries to do its best by caching compilation as much as possible though.
Another note: not all code is compiled equally.
