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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.