Added validation to the training script and exposed more of the settngs for network #113
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NickGeneva
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…ngs for network Signed-off-by: LimitingFactor <aswift0n3@gmail.com>
Updated the wandb initialisation Signed-off-by: LimitingFactor <aswift0n3@gmail.com>
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Hi @cfd1 , thanks for the PR! Is this ready for review? |
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@mnabian yes, it's ready for a review please |
mnabian
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Oct 19, 2023
| do_concat_trick: bool = False | ||
| num_processor_checkpoint_segments: int = 0 | ||
| # activation_fn: str = "relu" | ||
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| # performance configs | ||
| amp: bool = False | ||
| jit: bool = False | ||
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| # test & visualization configs |
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Change to "visualization configs"
mnabian
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Oct 19, 2023
| def get_options(): | ||
| parser = argparse.ArgumentParser() | ||
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| parser.add_argument("--entity", "-e", type=str, default=None) |
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Can we add these to the config file and not use argparse here?
mnabian
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Oct 19, 2023
| # initialize distributed manager | ||
| DistributedManager.initialize() | ||
| dist = DistributedManager() | ||
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| # initialize loggers | ||
| if wandb: |
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Is this if statement necessary? This can be done by changing the mode argument in initialize_wandb
mnabian
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Oct 19, 2023
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| if __name__ == "__main__": | ||
| @torch.no_grad() | ||
| def validation(self): |
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This is very similar to the predict method in `inference.py. Can we make the prediction code more modular to avoid code duplication?
mnabian
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Oct 19, 2023
| # Train the model | ||
| tmp_start = time.time() | ||
| loss_train_agg = 0 | ||
| for graph in tqdm(trainer.dataloader): |
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Does tqdm play nicely in the multi-gpu runs?
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