Class Checkpointable

Defined in tensorflow/python/training/

Manages dependencies on other objects.

Checkpointable objects may have dependencies: other Checkpointable objects which should be saved if the object declaring the dependency is saved. A correctly saveable program has a dependency graph such that if changing a global variable affects an object (e.g. changes the behavior of any of its methods) then there is a chain of dependencies from the influenced object to the variable.

Dependency edges have names, and are created implicitly when a Checkpointable object is assigned to an attribute of another Checkpointable object. For example:

obj = Checkpointable()
obj.v = ResourceVariable(0.)

The Checkpointable object obj now has a dependency named "v" on a variable.

Checkpointable objects may specify Tensors to be saved and restored directly (e.g. a Variable indicating how to save itself) rather than through dependencies on other objects. See Checkpointable._gather_saveables_for_checkpoint for details.




Support = checkpointable syntax.