tf.CriticalSection     View source on GitHub    Critical section.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.CriticalSection  
tf.CriticalSection(
    name=None, shared_name=None, critical_section_def=None, import_scope=None
)
 A CriticalSection object is a resource in the graph which executes subgraphs in serial order. A common example of a subgraph one may wish to run exclusively is the one given by the following function: v = resource_variable_ops.ResourceVariable(0.0, name="v")

def count():
  value = v.read_value()
  with tf.control_dependencies([value]):
    with tf.control_dependencies([v.assign_add(1)]):
      return tf.identity(value)
 Here, a snapshot of v is captured in value; and then v is updated. The snapshot value is returned. If multiple workers or threads all execute count in parallel, there is no guarantee that access to the variable v is atomic at any point within any thread's calculation of count. In fact, even implementing an atomic counter that guarantees that the user will see each value 0, 1, ..., is currently impossible. The solution is to ensure any access to the underlying resource v is only processed through a critical section: cs = CriticalSection()
f1 = cs.execute(count)
f2 = cs.execute(count)
output = f1 + f2
session.run(output)
 The functions f1 and f2 will be executed serially, and updates to v will be atomic. NOTES All resource objects, including the critical section and any captured variables of functions executed on that critical section, will be colocated to the same device (host and cpu/gpu). When using multiple critical sections on the same resources, there is no guarantee of exclusive access to those resources. This behavior is disallowed by default (but see the kwarg exclusive_resource_access). For example, running the same function in two separate critical sections will not ensure serial execution: v = tf.compat.v1.get_variable("v", initializer=0.0, use_resource=True)
def accumulate(up):
  x = v.read_value()
  with tf.control_dependencies([x]):
    with tf.control_dependencies([v.assign_add(up)]):
      return tf.identity(x)
ex1 = CriticalSection().execute(
  accumulate, 1.0, exclusive_resource_access=False)
ex2 = CriticalSection().execute(
  accumulate, 1.0, exclusive_resource_access=False)
bad_sum = ex1 + ex2
sess.run(v.initializer)
sess.run(bad_sum)  # May return 0.0

 


 Attributes
  name  
   Methods execute View source 
execute(
    fn, exclusive_resource_access=True, name=None
)
 Execute function fn() inside the critical section. fn should not accept any arguments. To add extra arguments to when calling fn in the critical section, create a lambda: critical_section.execute(lambda: fn(*my_args, **my_kwargs))

 


 Args
  fn   The function to execute. Must return at least one tensor.  
  exclusive_resource_access   Whether the resources required by fn should be exclusive to this CriticalSection. Default: True. You may want to set this to False if you will be accessing a resource in read-only mode in two different CriticalSections.  
  name   The name to use when creating the execute operation.   
 


 Returns   The tensors returned from fn().  

 


 Raises
  ValueError   If fn attempts to lock this CriticalSection in any nested or lazy way that may cause a deadlock.  
  ValueError   If exclusive_resource_access == True and another CriticalSection has an execution requesting the same resources as fn. Note, even ifexclusive_resource_accessisTrue, if another execution in anotherCriticalSectionwas created withoutexclusive_resource_access=True, aValueError` will be raised.