Options for constructing a Checkpoint.
tf.train.CheckpointOptions(
experimental_io_device=None,
experimental_enable_async_checkpoint=False,
enable_async=False
)
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)
Args |
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.
|
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.
|
Attributes |
enable_async
|
|
experimental_enable_async_checkpoint
|
|
experimental_io_device
|
|