Make data processing optional in run_training() #220
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Thanks for the review @JamesKunstle! Would it be helpful to open another PR to update the pylint config and make it a little less strict? Looks like it mainly comes down to this value being too low. Line 283 in b37c8ce |
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Thanks for adding this @MichaelClifford! I would just rebase this on main (might fix some linting issues for you for free), and then for the rest you can check via |
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This pull request has merge conflicts that must be resolved before it can be |
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Thanks for the review @Maxusmusti and @JamesKunstle ! Sorry for the delay on the rebase. Should be good now, and looks like all the tests are passing :) |
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@MichaelClifford Looks good! Just a couple of quick comments, but nothing blocking really
README.md
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| If the machine's above have shared storage, users can preprocess the training dataset a single time so that it can then distributed to each machine with the following update: |
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machine's -> machines
then distributed -> then be distributed
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Thanks 😄 done.
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@MichaelClifford Awesome, thanks! Oleg is going to give this a spin, and then we'll get this merged today ✅ |
src/instructlab/training/__init__.py
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| # defer import of main_ds | |||
| def run_training(torch_args: TorchrunArgs, train_args: TrainingArgs) -> None: | |||
| def run_training( | |||
| torch_args: TorchrunArgs, train_args: TrainingArgs, process_data: bool = True | |||
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I would move the process_data arg out to live under train_args, unless there's a compelling reason to not do this. Keeping this function simple allows other consuming libraries to have a straightforward interface into our main training loop.
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I tested locally and it seems to work. Please address the comment about process_data as an arg - we should keep this either as something that's a part of train_args or we need to make a really good case for why it shouldn't be. Presently we are trying to make our interface be very simple to make it easy for the CLI & other tools to consume.
Co-authored-by: Michael Clifford <[email protected]> Co-authored-by: Shreyanand <[email protected]> Signed-off-by: Michael Clifford <[email protected]>
Signed-off-by: Michael Clifford <[email protected]>
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Signed-off-by: Michael Clifford <[email protected]>
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This PR makes running
data_process.main()optional inrun_training(). This change is needed since it is not always desirable to process the data inside of the training function. Particularly in distributed training cases where its beneficial to processes the data once prior to training and then distribute the processed data along with the training function to each node.The changes here have been made so that data processing inside of
run_training()is still the default behavior.I've updated the README.md to reflect how to run data processing independent of
run_training().