How to read tensorflow dataset caches without building the dataset again












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I have created a dataset (tf.data.Dataset) with fairly complicated transformations and I have cached it to a file. My question is how can I read the content of that dataset again without reconstructing the dataset object again. For example:



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')


The idea is to build the dataset in another context and use it without building the complicated input pipeline that I had before.










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    0














    I have created a dataset (tf.data.Dataset) with fairly complicated transformations and I have cached it to a file. My question is how can I read the content of that dataset again without reconstructing the dataset object again. For example:



    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')


    The idea is to build the dataset in another context and use it without building the complicated input pipeline that I had before.










    share|improve this question

























      0












      0








      0







      I have created a dataset (tf.data.Dataset) with fairly complicated transformations and I have cached it to a file. My question is how can I read the content of that dataset again without reconstructing the dataset object again. For example:



      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')


      The idea is to build the dataset in another context and use it without building the complicated input pipeline that I had before.










      share|improve this question













      I have created a dataset (tf.data.Dataset) with fairly complicated transformations and I have cached it to a file. My question is how can I read the content of that dataset again without reconstructing the dataset object again. For example:



      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')


      The idea is to build the dataset in another context and use it without building the complicated input pipeline that I had before.







      python tensorflow






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 13 '18 at 9:39









      183.amir183.amir

      250214




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