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Tools for working with object-based checkpoints.
Visualization and inspection:
Managing dependencies:
Trackable 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 Trackable objects without automatic dependencies.
class CheckpointableObjectGraph: A ProtocolMessage
class List: An append-only sequence type which is trackable.
class Mapping: An append-only trackable mapping data structure with string keys.
class NoDependency: Allows attribute assignment to Trackable objects with no dependency.
class NumpyState: A trackable object whose NumPy array attributes are saved/restored.
class PythonStateWrapper: A mixin for putting Python state in an object-based checkpoint.
class UniqueNameTracker: Adds dependencies on trackable 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.