"Checkpoints capture the exact value of all parameters (tf.Variable objects) used by a model but do not contain any description of the computation defined by the model."
"The SavedModel format includes a serialized description of the computation defined by the model in addition to the parameter values, making models independent of the source code."
Checkpoints in TensorFlow save the parameters of a model but lack a description of the computations involved. The SavedModel format addresses this by containing both the parameter values and a serialized version of the computation, making it usable across various programming languages. The guide provides instructions on saving and restoring TensorFlow models, including writing checkpoints and using high-level APIs to manage model variables. It emphasizes that tracking parameters can be efficiently done by attaching them to Python object attributes.
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