def testCreateOnecloneWithPS(self):
g = tf.Graph()
with g.as_default():
tf.set_random_seed(0)
tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
tf_labels = tf.constant(self._labels, dtype=tf.float32)
model_fn = BatchNormClassifier
model_args = (tf_inputs, tf_labels)
deploy_config = model_deploy.DeploymentConfig(num_clones=1,
num_ps_tasks=1)
self.assertEqual(framework.get_variables(), [])
clones = model_deploy.create_clones(deploy_config, model_fn,
model_args)
self.assertEqual(len(framework.get_variables()), 5)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
self.assertEqual(len(update_ops), 2)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
total_loss, grads_and_vars = model_deploy.optimize_clones(
clones, optimizer)
self.assertEqual(len(grads_and_vars),
len(tf.trainable_variables()))
self.assertEqual(total_loss.op.name, 'total_loss')
for g, v in grads_and_vars:
self.assertDeviceEqual(g.device, '/job:worker/device:GPU:0')
self.assertDeviceEqual(v.device, '/job:ps/task:0/CPU:0')
def testCreateMulticloneWithPS(self):
g = tf.Graph()
with g.as_default():
tf.set_random_seed(0)
tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
tf_labels = tf.constant(self._labels, dtype=tf.float32)
model_fn = BatchNormClassifier
clone_args = (tf_inputs, tf_labels)
deploy_config = model_deploy.DeploymentConfig(num_clones=2,
num_ps_tasks=2)
self.assertEqual(framework.get_variables(), [])
clones = model_deploy.create_clones(deploy_config, model_fn,
clone_args)
self.assertEqual(len(framework.get_variables()), 5)
for i, v in enumerate(framework.get_variables()):
t = i % 2
self.assertDeviceEqual(v.device,
'/job:ps/task:%d/device:CPU:0' % t)
self.assertDeviceEqual(v.device, v.value().device)
self.assertEqual(len(clones), 2)
for i, clone in enumerate(clones):
self.assertEqual(
clone.outputs.op.name,
'clone_%d/BatchNormClassifier/fully_connected/Sigmoid' % i)
self.assertEqual(clone.scope, 'clone_%d/' % i)
self.assertDeviceEqual(clone.device,
'/job:worker/device:GPU:%d' % i)
def testCreateMulticlone(self):
g = tf.Graph()
with g.as_default():
tf.set_random_seed(0)
tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
tf_labels = tf.constant(self._labels, dtype=tf.float32)
model_fn = BatchNormClassifier
clone_args = (tf_inputs, tf_labels)
num_clones = 4
deploy_config = model_deploy.DeploymentConfig(
num_clones=num_clones)
self.assertEqual(framework.get_variables(), [])
clones = model_deploy.create_clones(deploy_config, model_fn,
clone_args)
self.assertEqual(len(framework.get_variables()), 5)
for v in framework.get_variables():
self.assertDeviceEqual(v.device, 'CPU:0')
self.assertDeviceEqual(v.value().device, 'CPU:0')
self.assertEqual(len(clones), num_clones)
for i, clone in enumerate(clones):
self.assertEqual(
clone.outputs.op.name,
'clone_%d/BatchNormClassifier/fully_connected/Sigmoid' % i)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
clone.scope)
self.assertEqual(len(update_ops), 2)
self.assertEqual(clone.scope, 'clone_%d/' % i)
self.assertDeviceEqual(clone.device, 'GPU:%d' % i)
def testCreateOnecloneWithPS(self):
g = tf.Graph()
with g.as_default():
tf.set_random_seed(0)
tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
tf_labels = tf.constant(self._labels, dtype=tf.float32)
model_fn = BatchNormClassifier
clone_args = (tf_inputs, tf_labels)
deploy_config = model_deploy.DeploymentConfig(num_clones=1,
num_ps_tasks=1)
self.assertEqual(framework.get_variables(), [])
clones = model_deploy.create_clones(deploy_config, model_fn,
clone_args)
self.assertEqual(len(clones), 1)
clone = clones[0]
self.assertEqual(clone.outputs.op.name,
'BatchNormClassifier/fully_connected/Sigmoid')
self.assertDeviceEqual(clone.device, '/job:worker/device:GPU:0')
self.assertEqual(clone.scope, '')
self.assertEqual(len(framework.get_variables()), 5)
for v in framework.get_variables():
self.assertDeviceEqual(v.device, '/job:ps/task:0/CPU:0')
self.assertDeviceEqual(v.device, v.value().device)
def testCreateLogisticClassifier(self):
g = tf.Graph()
with g.as_default():
tf.set_random_seed(0)
tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
tf_labels = tf.constant(self._labels, dtype=tf.float32)
model_fn = LogisticClassifier
clone_args = (tf_inputs, tf_labels)
deploy_config = model_deploy.DeploymentConfig(num_clones=1)
self.assertEqual(framework.get_variables(), [])
clones = model_deploy.create_clones(deploy_config, model_fn,
clone_args)
clone = clones[0]
self.assertEqual(len(framework.get_variables()), 2)
for v in framework.get_variables():
self.assertDeviceEqual(v.device, 'CPU:0')
self.assertDeviceEqual(v.value().device, 'CPU:0')
self.assertEqual(clone.outputs.op.name,
'LogisticClassifier/fully_connected/Sigmoid')
self.assertEqual(clone.scope, '')
self.assertDeviceEqual(clone.device, 'GPU:0')
self.assertEqual(len(tf.losses.get_losses()), 1)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
self.assertEqual(update_ops, [])