TypeError: Variable to save is not a Variable

Question

I have the following code to do some simple arithmetic calculations in distributed tensorflow. A minimal reproducible example would be:-

import tensorflow as tf

global_step_tensor = tf.Variable(10, trainable=False, name='global_step')

cluster = tf.train.ClusterSpec({"local": ["localhost:2222", "localhost:2223","localhost:2224", "localhost:2225"]})
x = tf.constant(2)

with tf.device("/job:local/task:0"):
    y = x + 300

model = tf.global_variables_initializer()

saver = tf.train.Saver([y])

ChiefSessionCreator = tf.train.ChiefSessionCreator(scaffold=None, master='grpc://localhost:2222', config=None, checkpoint_dir='/home/chaitanya/tensorflow/codes/checkpoints')
saver_hook = tf.train.CheckpointSaverHook(checkpoint_dir='/home/chaitanya/tensorflow/codes/checkpoints', save_secs=10, save_steps=None, saver=y, checkpoint_basename='model.ckpt', scaffold=None)
summary_hook = tf.train.SummarySaverHook(save_steps=None, save_secs=10, output_dir='/home/chaitanya/tensorflow/codes/savepoints', summary_writer=None, scaffold=None, summary_op=y)

with tf.train.MonitoredTrainingSession(master='grpc://localhost:2222', is_chief=True, checkpoint_dir='/home/chaitanya/tensorflow/codes/checkpoints', 
    scaffold=None, hooks=[saver_hook, summary_hook], chief_only_hooks=None, save_checkpoint_secs=10, save_summaries_steps=None, config=None) as sess:

    while not sess.should_stop():
        sess.run(model)

    while not sess.should_stop():
        result = sess.run(y)
        print(result)

The following is the error:-

Traceback (most recent call last):
  File "add_1.py", line 13, in <module>
    saver = tf.train.Saver([y])
    raise TypeError("Variable to save is not a Variable: %s" % var)
TypeError: Variable to save is not a Variable: Tensor("add_3:0", shape=(), dtype=int32, device=/job:local/task:3)

Please help me figure out the correct way to use this function.

Answer 1

When you simply write x + 300, you are not creating a tf.Variable. You need to explicitly use tf.get_variable() or tf.Variable() to create a variable which can be saved.

y = tf.Variable(x + 300)