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edhu2017

seems great for my reinforcement learning models. instead of parsing my hyperparameters through the tensorflow cli API and editing the training file a line at a time to take in an additional hyperparameter, I can just directly set them through the cli with fire.

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but then how will you keep track of which parameters worked well? I've been essentially storing my kwargs in json and not felt a need to control anything directly from the CLI.

Good question, for me I programatically generate a folder with the hyperparameter key - values in the name and store the checkpoints under it. As you can imagine, it can get out of control quickly if not managed well, but it works for 1-3 person projects. For anything more large scale or organized, I would recommend looking into Comet ML which lets you query and filter your experiments by hyperparameter ranges instead of manually looking at folder names.

I did that until I hit the Linux directory name length limit, lol. Now I hash the hyperparameters dict to get the directory name, and store a json file within. Totally ad hoc and I'm sure a better solution exists.

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