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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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Closing in favor of #327. |
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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:
cli.pywhere the CLI-speific logic will live.