|View source on GitHub|
API to persist the output of the input dataset. (deprecated)
Compat aliases for migration
See Migration guide for more details.
tf.data.experimental.snapshot( path, compression='AUTO', reader_func=None, shard_func=None )
Used in the notebooks
|Used in the tutorials|
The snapshot API allows users to transparently persist the output of their preprocessing pipeline to disk, and materialize the pre-processed data on a different training run.
This API enables repeated preprocessing steps to be consolidated, and allows re-use of already processed data, trading off disk storage and network bandwidth for freeing up more valuable CPU resources and accelerator compute time.
https://github.com/tensorflow/community/blob/master/rfcs/20200107-tf-data-snapshot.md has detailed design documentation of this feature.
Users can specify various options to control the behavior of snapshot,
including how snapshots are read from and written to by passing in
user-defined functions to the
shard_func is a user specified function that maps input elements to snapshot
Users may want to specify this function to control how snapshot files should be written to disk. Below is an example of how a potential shard_func could be written.
dataset = ... dataset = dataset.enumerate() dataset = dataset.apply(tf.data.experimental.snapshot("/path/to/snapshot/dir", shard_func=lambda x, y: x % NUM_SHARDS, ...)) dataset = dataset.map(lambda x, y: y)
reader_func is a user specified function that accepts a single argument:
(1) a Dataset of Datasets, each representing a "split" of elements of the
original dataset. The cardinality of the input dataset matches the
number of the shards specified in the
shard_func (see above). The function
should return a Dataset of elements of the original dataset.
Users may want specify this function to control how snapshot files should be read from disk, including the amount of shuffling and parallelism.
Here is an example of a standard reader function a user can define. This function enables both dataset shuffling and parallel reading of datasets:
def user_reader_func(datasets): # shuffle the datasets splits datasets = datasets.shuffle(NUM_CORES) # read datasets in parallel and interleave their elements return datasets.interleave(lambda x: x, num_parallel_calls=AUTOTUNE) dataset = dataset.apply(tf.data.experimental.snapshot("/path/to/snapshot/dir", reader_func=user_reader_func))
By default, snapshot parallelizes reads by the number of cores available on the system, but will not attempt to shuffle the data.
||Required. A directory to use for storing / loading the snapshot to / from.|
Optional. The type of compression to apply to the snapshot
written to disk. Supported options are
||Optional. A function to control how to read data from snapshot shards.|
||Optional. A function to control how to shard data when writing a snapshot.|