How to convert python to cython and speed up 100x
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converting python code to cython can significantly speed up your python code, especially if it involves loops and numerical computations. cython is a superset of python that allows you to add static types to your code, which can lead to performance improvements. in this tutorial, we will go through the steps to convert a simple python function to cython and demonstrate how to compile it for improved performance.
step 1: install cython
before you start, ensure you have cython installed. you can install it via pip:
step 2: create a simple python example
let's start with a simple python function that calculates the sum of squares of a list of numbers:
step 3: convert python code to cython
now, let's convert the above python function to cython. create a new file called `sum_of_squares.pyx`:
here, we have added type declarations for the parameters and variables:
- `int n` specifies that `n` is an integer.
- `cdef int total` and `cdef int i` declare variables with c-style integer types.
step 4: create a setup file
to compile the cython code, we need a setup script. create a file named `setup.py`:
step 5: compile the cython code
now, open a terminal and run the following command to compile the cython code:
this command compiles the `sum_of_squares.pyx` file and creates a shared object file (e.g., `sum_of_squares.cpython-version-platform.so`) that can be imported in python.
step 6: test performance
now we can test the performance of both the original python function and the cython function. create a new file named `test_performance.py`:
step 7: run the performance test
run the performance test script:
you should see that the cython implementation runs significantly faster than the pure python version.
conclusion
you've successfully converted a python function to cython and compiled it for performance. here are some key takeaways:
- adding static type declarations can significantly improve performance.
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