tf.train.Checkpoint

TensorFlow 1 version

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Groups trackable objects, saving and restoring them.

tf.train.Checkpoint(
    **kwargs
)

Checkpoint's constructor accepts keyword arguments whose values are types that contain trackable state, such as tf.keras.optimizers.Optimizer implementations, tf.Variable, tf.keras.Layer implementations, or tf.keras.Model implementations. It saves these values with a checkpoint, and maintains a save_counter for numbering checkpoints.

Example usage:

import tensorflow as tf
import os

checkpoint_directory = "/tmp/training_checkpoints"
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")

checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
status = checkpoint.restore(tf.train.latest_checkpoint(checkpoint_directory))
for _ in range(num_training_steps):
  optimizer.minimize( ... )  # Variables will be restored on creation.
status.assert_consumed()  # Optional sanity checks.
checkpoint.save(file_prefix=checkpoint_prefix)

Checkpoint.save and Checkpoint.restore write and read object-based checkpoints, in contrast to TensorFlow 1.x's tf.compat.v1.train.Saver which writes and reads variable.name based checkpoints. Object-based checkpointing saves a graph of dependencies between Python objects (Layers, Optimizers, Variables, etc.) with named edges, and this graph is used to match variables when restoring a checkpoint. It can be more robust to changes in the Python program, and helps to support restore-on-create for variables.

Checkpoint objects have dependencies on the objects passed as keyword arguments to their constructors, and each dependency is given a name that is identical to the name of the keyword argument for which it was created. TensorFlow classes like Layers and Optimizers will automatically add dependencies on their variables (e.g. "kernel" and "bias" for tf.keras.layers.Dense). Inheriting from tf.keras.Model makes managing dependencies easy in user-defined classes, since Model hooks into attribute assignment. For example:

class Regress(tf.keras.Model):

  def __init__(self):
    super(Regress, self).__init__()
    self.input_transform = tf.keras.layers.Dense(10)
    # ...

  def call(self, inputs):
    x = self.input_transform(inputs)
    # ...

This Model has a dependency named "input_transform" on its Dense layer, which in turn depends on its variables. As a result, saving an instance of Regress using tf.train.Checkpoint will also save all the variables created by the Dense layer.

When variables are assigned to multiple workers, each worker writes its own section of the checkpoint. These sections are then merged/re-indexed to behave as a single checkpoint. This avoids copying all variables to one worker, but does require that all workers see a common filesystem.

While tf.keras.Model.save_weights and tf.train.Checkpoint.save save in the same format, note that the root of the resulting checkpoint is the object the save method is attached to. This means saving a tf.keras.Model using save_weights and loading into a tf.train.Checkpoint with a Model attached (or vice versa) will not match the Model's variables. See the guide to training checkpoints for details. Prefer tf.train.Checkpoint over tf.keras.Model.save_weights for training checkpoints.

Methods

restore

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restore(
    save_path
)

Restore a training checkpoint.

Restores this Checkpoint and any objects it depends on.

Either assigns values immediately if variables to restore have been created already, or defers restoration until the variables are created. Dependencies added after this call will be matched if they have a corresponding object in the checkpoint (the restore request will queue in any trackable object waiting for the expected dependency to be added).

To ensure that loading is complete and no more assignments will take place, use the assert_consumed() method of the status object returned by restore:

checkpoint = tf.train.Checkpoint( ... )
checkpoint.restore(path).assert_consumed()

An exception will be raised if any Python objects in the dependency graph were not found in the checkpoint, or if any checkpointed values do not have a matching Python object.

Name-based tf.compat.v1.train.Saver checkpoints from TensorFlow 1.x can be loaded using this method. Names are used to match variables. Re-encode name-based checkpoints using tf.train.Checkpoint.save as soon as possible.

save

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save(
    file_prefix
)

Saves a training checkpoint and provides basic checkpoint management.

The saved checkpoint includes variables created by this object and any trackable objects it depends on at the time Checkpoint.save() is called.

save is a basic convenience wrapper around the write method, sequentially numbering checkpoints using save_counter and updating the metadata used by tf.train.latest_checkpoint. More advanced checkpoint management, for example garbage collection and custom numbering, may be provided by other utilities which also wrap write (tf.contrib.checkpoint.CheckpointManager for example).

write

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write(
    file_prefix
)

Writes a training checkpoint.

The checkpoint includes variables created by this object and any trackable objects it depends on at the time Checkpoint.write() is called.

write does not number checkpoints, increment save_counter, or update the metadata used by tf.train.latest_checkpoint. It is primarily intended for use by higher level checkpoint management utilities. save provides a very basic implementation of these features.