tf.train.CheckpointOptions

Options for constructing a Checkpoint.

tf.train.CheckpointOptions(
    experimental_io_device=None,
    experimental_enable_async_checkpoint=False,
    experimental_write_callbacks=None,
    enable_async=False,
    experimental_skip_slot_variables=False,
    experimental_sharding_callback=None
)

Used as the options argument to either tf.train.Checkpoint.save() or tf.train.Checkpoint.restore() methods to adjust how variables are saved/restored.

Example: Run IO ops on "localhost" while saving a checkpoint:

step = tf.Variable(0, name="step")
checkpoint = tf.train.Checkpoint(step=step)
options = tf.train.CheckpointOptions(experimental_io_device="/job:localhost")
checkpoint.save("/tmp/ckpt", options=options)

experimental_io_device string. Applies in a distributed setting. Tensorflow device to use to access the filesystem. If None (default) then for each variable the filesystem is accessed from the CPU:0 device of the host where that variable is assigned. If specified, the filesystem is instead accessed from that device for all variables. This is for example useful if you want to save to a local directory, such as "/tmp" when running in a distributed setting. In that case pass a device for the host where the "/tmp" directory is accessible.
experimental_enable_async_checkpoint bool Type. Deprecated, please use the enable_async option.
experimental_write_callbacks List[Callable]. A list of callback functions that will be executed after each saving event finishes (i.e. after save() or write()). For async checkpoint, the callbacks will be executed only after the async thread finishes saving. The return values of the callback(s) will be ignored. The callback(s) can optionally take the save_path (the result of save() or write()) as an argument. The callbacks will be executed in the same order of this list after the checkpoint has been written.
enable_async bool Type. Indicates whether async checkpointing is enabled. Default is False, i.e., no async checkpoint. Async checkpoint moves the checkpoint file writing off the main thread, so that the model can continue to train while the checkpoing file writing runs in the background. Async checkpoint reduces TPU device idle cycles and speeds up model training process, while memory consumption may increase.
experimental_skip_slot_variables bool Type. If true, ignores slot variables during restore. Context: TPU Embedding layers for Serving do not properly restore slot variables. This option is a way to omit restoring slot variables which are not required for Serving usecase anyways.(b/315912101)
experimental_sharding_callback tf.train.experimental.ShardingCallback. A pre-made or custom callback that determines how checkpoints are sharded on disk. Pre-made callback options are tf.train.experimental.ShardByDevicePolicy and tf.train.experimental.MaxShardSizePolicy. You may also write a custom callback, see tf.train.experimental.ShardingCallback.

enable_async

experimental_enable_async_checkpoint

experimental_io_device

experimental_sharding_callback

experimental_skip_slot_variables

experimental_write_callbacks