Tool for benchmarking the saving and loading speed of numpy arrays
usage: npabench.py [-h] [-s FILE | --max-size MAX_SIZE] [--no-browser]
[--save-html-file FILE] [--standalone-html] [--notebook]
Benchmark load/save speeds and summarize results graphically. A results file
is generated and saved. The tool can be also run just to read a results file
and show the results graphically using the --summarize-file argument.
optional arguments:
-h, --help show this help message and exit
-s FILE, --summarize-file FILE
Only generate summary graphs based on provided results
file. This will not run the benchmark.
--max-size MAX_SIZE Maximum file size (must be a power of 2) to benchmark
(e.g, 8MB, 4GB, 128KB). Default: 1MB. Caution: large
sizes can take a very long time or run out of memory
or disk space. 16GB takes about 90 minutes on a fast
computer.
--no-browser Do not launch a browser tab to display the results.
--save-html-file FILE
If desired, provide filename so that html report gets
saved to it.
--standalone-html By default the html will reference generated png files
for the figures. But if desired this option can encode
the pngs directly in the html (making it larger but
standalone).
--notebook Plot results in Jupyter Notebook.
pip install -r requirements.txt
If using conda:
(normally requirements.txt can also be used here as well but tables has a different name on conda (pytables); hence, the yml file)
conda activate base
conda env create --name npabench python=3.9 --file environment.yml
conda activate npabench
python npabench.py --max-size 16mb
https://omasoud.github.io/np-array-io-bench
Copyright (c) 2022 O. Masoud