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توضیحات

Speed up Python code that has well layed out type hints (works by converting the function to typed cython). Find more info at https://github.com/smpurkis/autocompile

ویژگی	مقدار
سیستم عامل	-
نام فایل	autocompile-0.3.0
نام	autocompile
نسخه کتابخانه	0.3.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Sam Purkis
ایمیل نویسنده	sam.purkis@hotmail.co.uk
آدرس صفحه اصلی	https://github.com/smpurkis/autocompile
آدرس اینترنتی	https://pypi.org/project/autocompile/
مجوز	-

# AutoCompile TLDR; Speed up Python code that is marked with type hints (by converting it to Cython) This is a package born slightly out of surprise when I found out that type hints don't speed up Python code at all, when all the information is there to be able to speed it up. So I decided to write this short package, that analyzes the code of any function marked with `@autocompile` and converts it into a Cython inline function. For example, ```python def do_maths(x: float): i: int for i in range(10000000): x += (i + x) ** 0.1 return x ``` will be converted to: ```cython def do_maths(double x): cdef long i for i in range(10000000): x += (i + x) ** 0.1 return x ``` ## Documentation `@autocompile` has the following arguments: ``` mode: "inline" or "file", type: str, default: "inline" "inline": uses Cython inline as a backend, works with all imported libraries "file": moves code to a tmp file and cythonizes it using subprocess, doesn't work with any imported libraries infer_types: True or False, type: Bool, default: False Enable Cython infer type option checks_on: True or False, type: Bool, default: False Enable Cython boundary and wrapping checking required_imports: {} or globals(), type: Dict, default: {} This is required for access to the globals of the calling module. As Python in its infinite wisdom doesn't allow access without explicitly passing them. Example: @autocompile(required_imports=globals()) def foo(bar: int): x = np.arange(bar) return x Without passing globals, Cython inline conversion will error, as it doesn't know what np (numpy) is. debug: True or False, type: Bool, default: False Shows the created function code to be used in place of the original force_memview: True or False, type: Bool, default: False (currently disabled) Forces all declared numpy arrays to be treated at cython memview. Can be unsafe, as addition of memviews in cython is not supported while for numpy arrays it is. ``` ## Benchmark Here are a few benchmarks of speed improvements (all code is in `tests` folder): ``` tests/test_main.py::test_mixed_maths maths_py took: 1.263 seconds maths_nb took: 0.498 seconds func_cy took: 2.489 seconds maths_ac took: 0.486 seconds PASSED tests/test_main.py::test_list_type lists_py took: 0.091 seconds lists_nb took: 0.042 seconds func_cy took: 0.049 seconds lists_ac took: 0.053 seconds PASSED tests/test_main.py::test_mixed_types mixed_py took: 0.513 seconds mixed_nb took: 0.292 seconds func_cy took: 0.199 seconds mixed_ac took: 0.064 seconds PASSED tests/test_main.py::test_np_arr_in_body np_array_in_body_py took: 0.494 seconds np_array_in_body_nb took: 0.017 seconds func_cy took: 0.45 seconds np_array_in_body_ac took: 0.467 seconds PASSED tests/test_main.py::test_np_arr_in_args np_array_in_args_py took: 0.443 seconds np_array_in_args_nb took: 0.011 seconds np_array_in_args_np took: 0.01 seconds np_array_in_args_ac took: 0.016 seconds PASSED tests/test_main.py::test_strings string_py took: 0.049 seconds string_nb took: 0.795 seconds func_cy took: 0.721 seconds string_ac took: 0.038 seconds PASSED ``` notes: - The `test_strings` is using cython version `3.0a6`, using `<3.0` yields results similar to numba. - This is using `cython.compile`, to compare against, as it is the closest function to `autocompile` (`ac`). As can be seen, `ac` is best at a mixture of base Python types, lists, dicts, numbers. It offers selective speed up for arrays at the moment (via numpy array inputs as arguments) Potential improvements: - Add support for return types (relatively straightforward) - Add a backend like Nim or Julia (a lot of work)

نیازمندی

مقدار	نام
-	cython
-	numba
-	numpy
-	pytest

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl autocompile-0.3.0:

pip install autocompile-0.3.0.whl

نصب پکیج tar.gz autocompile-0.3.0:

pip install autocompile-0.3.0.tar.gz