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import tensorflow as tf

tf.enable_eager_execution()

db = tf.data.Dataset.range(10)

db = db.cache('/tmp/range')

for v in db:

    print(v)

# /tmp/range.data-00000-of-00001  /tmp/range.index files are created



# later, you could restore the dataset from a method like this:

new_db = tf.data.Dataset.from_cache('/tmp/range')

import tensorflow as tf

tf.enable_eager_execution()

db = tf.data.Dataset.range(10)

db = db.cache('/tmp/range')

for v in db:

    print(v)

# /tmp/range.data-00000-of-00001  /tmp/range.index files are created



# later, you could restore the dataset from a method like this:

new_db = tf.data.Dataset.from_cache('/tmp/range')

import tensorflow as tf

tf.enable_eager_execution()

db = tf.data.Dataset.range(10)

db = db.cache('/tmp/range')

for v in db:

    print(v)

# /tmp/range.data-00000-of-00001  /tmp/range.index files are created



# later, you could restore the dataset from a method like this:

new_db = tf.data.Dataset.from_cache('/tmp/range')

import tensorflow as tf

tf.enable_eager_execution()

db = tf.data.Dataset.range(10)

db = db.cache('/tmp/range')

for v in db:

    print(v)

# /tmp/range.data-00000-of-00001  /tmp/range.index files are created



# later, you could restore the dataset from a method like this:

new_db = tf.data.Dataset.from_cache('/tmp/range')

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