pycompile 0.2.2

A CLI tool for compiling python

pycompile

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"""

A CLI tool for compiling python source code using Cython or Nuitka.

Latest docs 📝

here

Table of contents

1. installation 🔨
2. compile
3. benchmark
4. dry run
5. Local-development 💻🏭

Installation

pip install pycompile

Compile

Syntax	Description
--input-path PATH	by default it excludes any test and __init__.py files
--clean-source	Deletes the sources files.
--keep-builds	Keeps the temp build files.
--clean-executables	Deletes the shared objects (.so) files.
--engine	Can be cython or nuitka.
--exclude-glob-paths	Glob file patterns for excluding specific files.
--verbose	Increase log messages.

pycompile -i your_python_files --clean-source --engine nuitka 

By default, the Cython is being used as the default compiler.

[!TIP] For compiling the examples use the following command

pycompile -i input_path --engine cython 

which by default, deletes any temp build files and keeps the source files.

or

pycompile -i input_path --engine nuitka

After the compilation the input dir will have the following structure.

examples
    ├── fib.py.py                           
    ├── fib.cpython-310-darwin.so                      
    ├── test_fib.py                   

Benchmark

Syntax	Description
--input-path PATH	by default it excludes any test and __init__.py files
--engine	Can be cython, nuitka, all or none.
--type	Can be memory , cpy, or both
--verbose	Increase log messages.
--profile_func_pattern TEXT	function name pattern for profiling defaults to benchmark

For running a benchmark on the input-path use the following command:

pycompile benchmark -i src/examples -vvv

which by default will start a memory and a cpu benchmark, starting with python and then with cython and nuitka

[!IMPORTANT] The python package must have a test_module.py because both benchmark types are invoked with pytest runs

[!NOTE] For memory profiling the script will decorate all the functions in benchmark.py with the profile decorator from memory-profiler. This is not optimal memory profiling, because we don't actually profile the function itself, instead we profile the caller but it's necessary if we want to profile also the compiled code. Use the profile_func_pattern to specify the function to be profiled in different module for example if main is the entrypoint under main.py use --profile_func_pattern main.

Hence, the following structure are required for the benchmark subcommand.

 module
    ├── sample_funcs.py                        # implementation
    ├── main.py                                # entrypoint with a `main` function, during compilation will be excluded
    ├── test_sample_funcs.py                   # test cases

Memory benchmark using:3.10.9 (main, Feb 2 2023, 12:59:36) [Clang 14.0.0 (clang-1400.0.29.202)

Line #    Mem usage    Increment  Occurrences   Line Contents
=============================================================
     7     49.4 MiB     49.4 MiB           1   @profile
     8                                         def samples_benchmark():
     9    127.7 MiB     78.4 MiB           1       sum_of_squares()
    10    166.0 MiB     38.3 MiB           1       harmonic_mean()
    11    166.0 MiB      0.0 MiB           1       fibonacci(30)
    12    204.2 MiB     38.2 MiB           1       sum_numbers()
    13     57.7 MiB   -146.5 MiB           1       sum_strings()

46.03s call     test_examples.py::test_examples

[!NOTE] For cpu profiling the same approached is being used, but instead of decorating the calling functions it decorates the test cases with the benchmark from pytest-benchmark.

CPU benchmark using:3.10.9 (main, Feb 2 2023, 12:59:36) [Clang 14.0.0 (clang-1400.0.29.202)]

------------------------------------------- benchmark: 1 tests ------------------------------------------
Name (time in s)        Min     Max    Mean  StdDev  Median     IQR  Outliers     OPS  Rounds  Iterations
---------------------------------------------------------------------------------------------------------
test_examples        3.9257  4.0640  3.9731  0.0605  3.9387  0.0917       1;0  0.2517       5           1
---------------------------------------------------------------------------------------------------------

Legend:
  Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
  OPS: Operations Per Second, computed as 1 / Mean
=================================================================================================================
29.40s call     test_examples.py::test_examples

Dry run

Syntax	Description
--input-path PATH	by default it excludes any test and __init__.py files
--exclude-glob-paths	Glob file patterns for excluding specific files.
--verbose	Increase log messages.

pycompile dry_run -i ./src

Local-development

For local development run the following command

make setup-local-dev

All available make commands

make help