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[in progress] argparse approach #6
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All the relevant changes here are in main.py (https://github.com/dronedeploy/dd-ml-segmentation-benchmark/pull/6/files#diff-5bc02cefb3ea9e27f1a6776eabd1935d) -- please ignore the rest.
This is how I wrap my benchmarks for convenience: defaults/hyperparameters are defined in one file and parsed as args (so I can easily modify them from the command line when I run a bunch of experiments, and see them in the run overview--especially for frequently changed params like the model name, the number of epochs, the learning rate...). This config is then passed on to other relevant training/inference steps.
Let me know what you think! I realize this may not be ideal for your setup since you have several different scripts in libs/ that may need different config. I find this strategy the only way to stay sane when running lots of experiments and don't know how other people work without it.
We could extend this approach to cover all the params in libs/training.py at least and do the same for main_keras.py (though then we'd have to consider merging the two main_*.py into one file imo).
Or we could just leave a few of these as an example, so participants can easily create their own command line flags if they choose.