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A class which needs a TensorFlow session for some of its operations.

A SessionUser is a class which can be instantiated outside of a TensorFlow session, but which needs to have access to a session-like object for most of its operations.

A session-like object is an object on which we can call run to execute some TensorFlow ops (e.g. tf.Session() and tf.train.(Singular)MonitoredSession).

There are 2 ways of providing a session to a SessionUser:

  • within a TensorFlow session context manager (e.g. within with tf.Session() as session:), the session will be automatically retrieved. Be aware that a tf.train.(Singular)MonitoredSession does not enter a session context manager.
  • if the session is constructed outside of a context manager, it must be provided using the session setter.

The session can then be accessed using the session property.

The usual way to use a SessionUser is the following.

class MySessionUserClass(SessionUser):

  def __init__(self):
    self(MySessionUserClass, self).__init__()
    self.op = tf.constant(0)

  def run_some_op(self):

my_session_owner = MySessionUserClass()
with tf.Session() as session:

Since both tf.train.SingularMonitoredSession and tf.train.MonitoredSession do not create a Session context manager, one will need to set the session manually.

with tf.train.(Singular)MonitoredSession(...) as session:
  my_session_owner.session = session

For tf.train.SingularMonitoredSession, since one can access the underlying raw session, one can also open a Session context manager.

with tf.train.SingularMonitoredSession(...) as mon_sess:
  with mon_sess.raw_session().as_default():
     while not mon_sess.should_stop():

Advanced usage:

One can override the session setter by using the following code.

class MyClass(session_utils.SessionUser):

  # This is overriding the `session` setter from `session_utils.SessionUser`.
  def session(self, session):
    # This calls the setter of the `session_utils.SessionUser` class.
    session_utils.SessionUser.session.fset(self, session)
    # Then you can do other things such as setting the session of internal
    # objects.

session Returns the TensorFlow session-like object used by this object.