Hokohoko¶
This is the base module for Hokohoko. Can be invoked either
programmatically by passing an appropriately configured
hokohoko.entities.Config
to hokohoko.Hokohoko.run()
, or
by invoking in a script:
python3 -m hokohoko.Hokohoko [options]
Several options are available however to ease testing and debugging during Predictor development, so feel free to explore the options and associated documentation. However, for benchmarking, the only ones you should need to change are:
predictor Should be set to your Predictor of choice (which
inherits hokohoko.entities.Predictor), along with
any required parameters.
process_count Tune for how many cores you are willing to use.
Hokohoko uses roughly 1GB of RAM, plus the
Predictor's internal state, per core.
and:
assessor The assessor class you wish to use, plus its parameters.
Multiple assessors can be defined.
Note
The default options have been tuned from analysis of over 1000 peer-reviewed papers’ settings. If you do feel the need to use custom settings, please make sure you record them with your results, so others can compare their work with yours.
- hokohoko.Hokohoko.run(config)¶
Runs the Hokohoko benchmark suite based on the provided config.
- Parameters
config (hokohoko.entities.Config) – The global configuration options.