luke-who / federated-learning-project Goto Github PK

Hello! Our static bug checker has found a performance issue in
tff_tutorials/custom_federated_algorithms,_part_2_implementing_federated_averaging.py: batch_train is repeatedly called in a for loop, but there is a tf.function decorated function _train_on_batch defined and called in batch_train.

In that case, when batch_train is called in a loop, the function `` will create a new graph every time, and that can trigger tf.function retracing warning.

Here is the tensorflow document to support it.

Briefly, for better efficiency, it's better to use:

@tf.function
def inner():
    pass

def outer():
    inner()  

than:

def outer():
    @tf.function
    def inner():
        pass
    inner()

Looking forward to your reply.