Translations:TensorFlow/146/fr

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Créer des points de contrôle

Whether or not you expect your code to run for long time periods, it is a good habit to create Checkpoints during training. A checkpoint is a snapshot of your model at a given point during the training process (after a certain number of iterations or after a number of epochs) that is saved to disk and can be loaded at a later time. It is a handy way of breaking jobs that are expected to run for a very long time, into multiple shorter jobs that may get allocated on the cluster more quickly. It is also a good way of avoiding losing progress in case of unexpected errors in your code or node failures.