The module in this repository compares execution time and maximum memory usage for different methods of converting a color image to black-and-white.

The conversion methods are:

• using Numpy
• using Numba to jit the Numpy method
• using a for loop in Numba

This project is derived from and replicates the results from Itamar Turner-Trauring's blog post The wrong way to speed up your code with Numba. For a more thorough and detailed explanation, please see his post.

The execution times and maximum memory usage of the conversion methods are below. The results from a first run of all three methods are printed on the first three lines; the second set of three lines shows results for the second run with a different color image as input. The second run shows the optimized execution times. Please note that the exact execution times will stochastically vary from run to run, though the maximum memory usage should be consistent for a given image.

The results from the second run above replicate the primary theme of Mr. Turner-Trauring's blog post: trying to optimize the Numpy code by merely jitting it with Numba is far inferior to rewriting the code with a for loop to take advantage of how Numba optimizes code. The for loop Numba code is much faster than the jitted written-for-Numpy code, and it requires much less memory.

Note that if one wants a more careful quantification of the code speed-up and reduced memory usage, one should statistically summarize results from multiple profiling runs on images that are representative of the ones that will be encountered in production.

From the top-level project directory, you can create and activate the Anaconda environment by running these commands:

conda env create -f environment.yml

conda activate numba01

Then you can run the code and save the results with this command:

python src/convert_bw.py > output/profiling_results.txt

The commands to run with Poetry in the top-level directory are:

poetry install

poetry run python src/convert_bw.py