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Manages saving/restoring trackable values to disk, for DTensor. (deprecated)
Inherits From: Checkpoint
tf.experimental.dtensor.DTensorCheckpoint(
mesh: tf.experimental.dtensor.Mesh
,
root=None,
**kwargs
)
Attributes | |
---|---|
save_counter
|
An integer variable which starts at zero and is incremented on save.
Used to number checkpoints. |
Methods
read
read(
save_path, options=None
)
Reads a training checkpoint written with write
.
Reads this Checkpoint
and any objects it depends on.
This method is just like restore()
but does not expect the save_counter
variable in the checkpoint. It only restores the objects that the checkpoint
already depends on.
The method is primarily intended for use by higher level checkpoint
management utilities that use write()
instead of save()
and have their
own mechanisms to number and track checkpoints.
Example usage:
# Create a checkpoint with write()
ckpt = tf.train.Checkpoint(v=tf.Variable(1.))
path = ckpt.write('/tmp/my_checkpoint')
# Later, load the checkpoint with read()
# With restore() assert_consumed() would have failed.
checkpoint.read(path).assert_consumed()
# You can also pass options to read(). For example this
# runs the IO ops on the localhost:
options = tf.train.CheckpointOptions(
experimental_io_device="/job:localhost")
checkpoint.read(path, options=options)
Args | |
---|---|
save_path
|
The path to the checkpoint as returned by write .
|
options
|
Optional tf.train.CheckpointOptions object.
|
Returns | |
---|---|
A load status object, which can be used to make assertions about the
status of a checkpoint restoration. See restore for details.
|
restore
restore(
save_path, options=None
)
Restores a training checkpoint.
Restores this Checkpoint
and any objects it depends on.
This method is intended to be used to load checkpoints created by save()
.
For checkpoints created by write()
use the read()
method which does not
expect the save_counter
variable added by save()
.
restore()
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).
checkpoint = tf.train.Checkpoint( ... )
checkpoint.restore(path)
# You can additionally pass options to restore():
options = tf.CheckpointOptions(experimental_io_device="/job:localhost")
checkpoint.restore(path, options=options)
To ensure that loading is complete and no more deferred restorations will
take place, use the assert_consumed()
method of the status object returned
by restore()
:
checkpoint.restore(path, options=options).assert_consumed()
The assert will raise an error 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.
Loading from SavedModel checkpoints
To load values from a SavedModel, just pass the SavedModel directory to checkpoint.restore:
model = tf.keras.Model(...)
tf.saved_model.save(model, path) # or model.save(path, save_format='tf')
checkpoint = tf.train.Checkpoint(model)
checkpoint.restore(path).expect_partial()
This example calls expect_partial()
on the loaded status, since
SavedModels saved from Keras often generates extra keys in the checkpoint.
Otherwise, the program prints a lot of warnings about unused keys at exit
time.
Args | |
---|---|
save_path
|
The path to the checkpoint, as returned by save or
tf.train.latest_checkpoint . If the checkpoint was written by the
name-based tf.compat.v1.train.Saver , names are used to match
variables. This path may also be a SavedModel directory.
|
options
|
Optional tf.train.CheckpointOptions object.
|
Returns | |
---|---|
A load status object, which can be used to make assertions about the
status of a checkpoint restoration.
The returned status object has the following methods:
|
Raises | |
---|---|
NotFoundError
|
if the a checkpoint or SavedModel cannot be found at
save_path .
|
save
save(
file_prefix, options=None
)
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
and read
.
(tf.train.CheckpointManager
for example).
step = tf.Variable(0, name="step")
checkpoint = tf.train.Checkpoint(step=step)
checkpoint.save("/tmp/ckpt")
# Later, read the checkpoint with restore()
checkpoint.restore("/tmp/ckpt-1")
# You can also pass options to save() and restore(). For example this
# runs the IO ops on the localhost:
options = tf.train.CheckpointOptions(experimental_io_device="/job:localhost")
checkpoint.save("/tmp/ckpt", options=options)
# Later, read the checkpoint with restore()
checkpoint.restore("/tmp/ckpt-1", options=options)
Args | |
---|---|
file_prefix
|
A prefix to use for the checkpoint filenames
(/path/to/directory/and_a_prefix). Names are generated based on this
prefix and Checkpoint.save_counter .
|
options
|
Optional tf.train.CheckpointOptions object.
|
Returns | |
---|---|
The full path to the checkpoint. |
sync
sync()
Wait for any outstanding save or restore operations.
write
write(
file_prefix, options=None
)
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.
Checkpoints written with write
must be read with read
.
Example usage:
step = tf.Variable(0, name="step")
checkpoint = tf.Checkpoint(step=step)
checkpoint.write("/tmp/ckpt")
# Later, read the checkpoint with read()
checkpoint.read("/tmp/ckpt")
# You can also pass options to write() and read(). For example this
# runs the IO ops on the localhost:
options = tf.CheckpointOptions(experimental_io_device="/job:localhost")
checkpoint.write("/tmp/ckpt", options=options)
# Later, read the checkpoint with read()
checkpoint.read("/tmp/ckpt", options=options)
Args | |
---|---|
file_prefix
|
A prefix to use for the checkpoint filenames (/path/to/directory/and_a_prefix). |
options
|
Optional tf.train.CheckpointOptions object.
|
Returns | |
---|---|
The full path to the checkpoint (i.e. file_prefix ).
|