A checkpoint is a snapshot of a machine learning model’s parameters at a specific point during training. It allows you to save the model’s progress and resume training from that point later. Checking the version of a checkpoint is important to ensure that you are using the correct version for your training task.
The importance of checking checkpoint versions is multifaceted. Firstly, it ensures compatibility between the checkpoint and the training code. Secondly, it helps track the progress of the training process and identify any potential issues. Thirdly, it allows for the comparison of different checkpoints to determine the best performing model.
