numba | 易学教程

numba eager compilation? Whats the pattern?

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问题 I looked into eager compilation on numba's website and couldnt figure out, how to specify the types: The example they use is this: from numba import jit, int32 @jit(int32(int32, int32)) def f(x, y): # A somewhat trivial example return x + y # source: http://numba.pydata.org/numba-doc/latest/user/jit.html#eager-compilation as you can see it gets 2 variables as input and returns one single variable. all of them should be int32. One way to understand the decorator is that @jit(int32(int32, int32

numba示例

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# -*- coding: utf-8 -*- import os import numba import numpy as np import cv2 from numba import jit @jit def aaa(ccc): py_list = [0,1] number_list = numba.typed.List([0]) cnts = numba.typed.Dict.empty( key_type=numba.int64, value_type=numba.int64 ) # a_dict=numba.typed.Dict(asdfsdf) for i in ccc: number_list.append(i) cnts[i] = i # print(i[0]) return number_list,cnts if __name__ == '__main__': ccc=[0,1] number_list,cnts=aaa(numba.typed.List(ccc)) print(number_list) print(cnts) 来源： oschina 链接： https://my.oschina.net/u/4393788/blog/4939705

Optimizing Calculations with numpy and numba Python

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问题 I am trying to make python run standard deviation functions faster with numba and numpy. However the problem is the for loop is very slow and I need alternatives so that I could make the code much faster. I iterated numba to the already existing numpy version however there is not much of a gain in performance. My original list_ has million of values within it thus it is taking a very long time to compute the standard deviation function. The list_ function down below is a very short numpy

numba vstack doesn't work on list of arrays

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问题 It's weird for me vstack doesn't work with Numba when input is a list of array, it only works when the input is a tuple of array. Example code: @nb.jit(nopython=True) def stack(items): return np.vstack(items) stack((np.array([1,2,3]), np.array([4,5,6]))) returns array([[1, 2, 3], [4, 5, 6]]) but stack([np.array([1,2,3]), np.array([4,5,6])]) Throws an error TypingError: No implementation of function Function(<function vstack at 0x0000027271963488>) found for signature: >>>vstack(reflected list

Multiprocessing an array in chunks

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问题 I have broken my standard deviation function into small chunks of std_1, std_2, std_3 etc. to optimize my code to make it run faster. Since I have over 2 million arrays on my main numpy array PC_list . I have used the numba, numpy arrays and multi processing to make the code run faster however I do not see any performance difference in the way even doe the code is broken into pieces from the main function. It takes about 57 seconds for the main function to process and the divided function to

Numba: How to Suppress

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问题 I keep on getting this error in my numba code: Warning 101:0: Unused argument 'self' My numba code is below. How do I suppress the error message? @autojit def initialise_output_data(self, input_data, output_data, params ): # Unpack Params #omega = params['omega'] #beta = params['beta'] #gamma = params['gamma'] psi = params['psi'] # Unpack Output Data mu = output_data['mu'] s2 = output_data['sigma2'] res = output_data['residuals'] res2 = output_data['residuals2'] # Initialise Garch Variables

Python Numba Value in Array

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问题 I am trying to check if a number is in NumPy array of int8 s. I tried this, but it does not work. from numba import njit import numpy as np @njit def c(b): return 9 in b a = np.array((9, 10, 11), 'int8') print(c(a)) The error I get is Invalid use of Function(<built-in function contains>) with argument(s) of type(s): (array(int8, 1d, C), Literal[int](9)) * parameterized In definition 0: All templates rejected with literals. In definition 1: All templates rejected without literals. In

Bounds checking is not supported for CUDA

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问题 I am trying to use Numba and access the GPU in order to accelerate the code, but I get the following error: in jit raise NotImplementedError("bounds checking is not supported for CUDA") NotImplementedError: bounds checking is not supported for CUDA I saw that another question was raised, but not completely specified nor answered here. I implemented the 2-for loops when I saw that the vectorized code ( y = corr*x + np.sqrt(1.-corr**2)*z ) did not work (same error). I also tried to play around

How to clear cache (or force recompilation) in numba

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问题 I have a fairly large codebase written in numba, and I have noticed that when the cache is enabled for a function calling another numba compiled function in another file, changes in the called function are not picked up when the called function is changed. The situation occurs when I have two files: testfile2: import numba @numba.njit(cache=True) def function1(x): return x * 10 testfile: import numba from tests import file1 @numba.njit(cache=True) def function2(x, y): return y + file1

How to clear cache (or force recompilation) in numba

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