prefigure 0.0.5

Run configuration management utils: combines configparser, argparse, and wandb.API

prefigure

Run-configuration management utils: combines configparser, argparse, and wandb.API

Capabilities for archiving run settings and pulling configurations from previous runs. With just 3 lines of code 😎 : the import, the arg setup, & the wandb push.

Combines argparse, configparser, and wandb.API

Install:

pip install prefigure

Instructions:

All your usual command line args (with the exception of --name and --training-dir) are now to be specified in a defaults.ini file -- see examples/ for an example.
A different .ini file can be specified via --config-file.

The option --wandb-config <url> pulls previous runs' configs off wandb, where <url> is the url of any one of your runs to override those defaults: e.g. --wandb-config='https://wandb.ai/drscotthawley/delete-me/runs/1m2gh3o1?workspace=user-drscotthawley'` (i.e., whatever URL you grab from your browser window when looking at an individual run.)

NOTE: the --wandb-config thing can only pull from WandB runs that used prefigure, i.e. that have logged a "wandb config push".

Any command line args you specify will override any settings from WandB and/or the .ini file.

The order of precedence is "command line args override WandB, which overrides the .ini file".

1st line to add

In your run/training code, add this near the top:

from prefigure.prefigure import get_all_args, push_wandb_config

2nd line to add

Near the top of your main(), add this:

args = get_all_args()

Further down in your code, comment-out (or delete) all your command-line arguments. If you want different command-line arguments, then add or change them in defaults.ini. The 'help' string for these is provided via comment in the line preceding your variable. (see defaults.ini for examples)

3rd line to add

and then right after you define the wandb logger, run

push_wandb_config(wandb_logger, args)

Sample usage (code_):

from prefigure import get_all_args, push_wandb_config


def main():

    # Config setup. Order of preference will be:
    #   1. Default settings are in defaults.ini file or whatever you specify via --config-file
    #   2. if --wandb-config is given, pull config from wandb to override defaults
    #   3. Any new command-line arguments override whatever was set earlier
    args = get_all_args()

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    torch.manual_seed(args.seed)

    train_set = SampleDataset([args.training_dir], args)
    train_dl = data.DataLoader(train_set, args.batch_size, shuffle=True,
                               num_workers=args.num_workers, persistent_workers=True, pin_memory=True)
    wandb_logger = pl.loggers.WandbLogger(project=args.name)

    # push config to wandb for archiving, but don't push --training-dir value to WandB
    push_wandb_config(wandb_logger, args, omit=['training_dir']) 

    demo_dl = data.DataLoader(train_set, args.num_demos, shuffle=True)