def build_ops():
init_step = int(_get_init_step())
print('init_step is %d' % (init_step))
step = counter(init_step)
schedule = step_based_schedule(config, step)
ckpt_tensor = tf.as_string(step + 1)
resize_op = resize_cluster(ckpt_tensor, schedule)
return init_step, resize_op
def test_step_based_scheduler():
sizes = [1, 2, 4, 8]
n_step = 3
config = ','.join('%d:%d' % (size, n_step) for size in sizes)
expected_sizes = [1, 1, 1, 2, 2, 2, 4, 4, 4, 8, 8, 8]
schedule = step_based_schedule(config)
with tf.Session() as sess:
for i in range(12):
size = sess.run(schedule)
assert (size == expected_sizes[i])
def build_ops():
step_place = tf.placeholder(dtype=tf.int32, shape=())
new_step_op = step_based_schedule(config, step_place)
resize_op = resize_cluster_from_url()
return step_place, resize_op, new_step_op
def _build_resize_op(self, config, init_step):
step = counter(init_step)
new_size = step_based_schedule(config, step)
ckpt_tensor = tf.as_string(step + 1)
resize_op = resize_cluster(ckpt_tensor, new_size)
return resize_op
def _build_resize_op(self, config, step):
new_size_op = step_based_schedule(config, step)
resize_op = resize_cluster_from_url()
return resize_op, new_size_op