Defined in tensorflow/contrib/checkpoint/__init__.py.
Tools for working with object-based checkpoints.
Visualization and inspection:
Managing dependencies:
Checkpointable data structures:
Checkpoint management:
Saving and restoring Python state:
Classes
class CheckpointManager: Deletes old checkpoints.
class Checkpointable: Manages dependencies on other objects.
class CheckpointableBase: Base class for Checkpointable objects without automatic dependencies.
class CheckpointableObjectGraph: A ProtocolMessage
class List: An append-only sequence type which is checkpointable.
class Mapping: An append-only checkpointable mapping data structure with string keys.
class NoDependency: Allows attribute assignment to Checkpointable objects with no dependency.
class NumpyState: A checkpointable object whose NumPy array attributes are saved/restored.
class PythonStateWrapper: Wraps a Python object for storage in an object-based checkpoint.
class UniqueNameTracker: Adds dependencies on checkpointable objects with name hints.
Functions
capture_dependencies(...): Capture variables created within this scope as Template dependencies.
dot_graph_from_checkpoint(...): Visualizes an object-based checkpoint (from tf.train.Checkpoint).
list_objects(...): Traverse the object graph and list all accessible objects.
object_metadata(...): Retrieves information about the objects in a checkpoint.
split_dependency(...): Creates multiple dependencies with a synchronized save/restore.