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Migrates to LightningCLI #265

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Migrates to LightningCLI #265

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kylebgorman
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Closes #60.
Closes #218.

LightningCLI removes our need to create separate training and prediction CLI programs, moving nearly all of that logic into the base model.

This commit in particular sets the stage:

  • Updates dependencies.
  • Increments minor version number.
  • Creates an empty cli.py where the CLI-speific logic will live.

kylebgorman and others added 19 commits November 20, 2024 17:14
Closes CUNY-CL#60.
Closes CUNY-CL#218.

LightningCLI removes our need to create separate training and prediction
CLI programs, moving nearly all of that logic into the base model.

This commit in particular sets the stage:

* Updates dependencies.
* Increments minor version number.
* Creates an empty `cli.py` where the CLI-speific logic will live.
This adds an extra flag to every command and for what? If you want to
keep model directories separate for different experiments in a way that
goes beyond already done with the automated versioning, you can just
append a subdirectory name to `--model_dir`, e.g., using `--model_dir
models/foo` instead of `--model_dir models --experiment foo`.
Borrowing a design element I used in UDTube, I decompose the dataset
object into two pieces:

* a `Mapper` interface which knows how to map between lists of strings
  and tensors (to decode and encode)
* `DataSet`, as before

There was no particular reason for the mapper functions to live inside
the dataset, and this commit simply makes this separation.

A subsequent commit will use this mapper object during prediction.
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Unpin Numpy Torch and Lightning 2.0.0
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