Accelerate Python Functions with Numba
Numba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN.
You don’t need to replace the Python interpreter, run a separate compilation step, or even have a C/C++ compiler installed. Just apply one of the Numba decorators to your Python function, and Numba does the rest.
from numba import jit import numpy as np import time x = np.random.rand(10000000) @jit def sum_sq(a): result = 0 N = len(a) for i in range(N): result+= a[i] return result s_t = (time.time()) sum_sq.py_func(x) s_t_1 = time.time() sum_sq(x) s_t_2 = time.time() print(s_t_1 - s_t, s_t_2 - s_t_1)
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