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CheckpointIO takes care of the Booster.save and Booster.load logic to allow for model saving/resuming/loading. It should be noted that CheckpointIO is often used in pair with the Plugin as a Plugin can possibly require a specific saving/loading strategy. However, we should propose general ones for normal pytorch model and a DTensor-based model. As the DTensor is under development, we should focus on the native PyTorch implementation first.
Wanna track the development progress? Take a look at
Overview
CheckpointIOtakes care of theBooster.saveandBooster.loadlogic to allow for model saving/resuming/loading. It should be noted thatCheckpointIOis often used in pair with thePluginas aPlugincan possibly require a specific saving/loading strategy. However, we should propose general ones for normal pytorch model and a DTensor-based model. As the DTensor is under development, we should focus on the native PyTorch implementation first.Wanna track the development progress? Take a look at
proposal: #3046
project kanban: API Refactoring
Goal
The CheckpointIO should allow the user to save/load the native PyTorch model/optimizer/lr schduler.