Python tensorflow.variable_axis_size_partitioner() Examples
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code examples of tensorflow.variable_axis_size_partitioner().
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Example #1
Source File: partitioned_variables_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testControlDepsNone(self): with self.test_session() as session: c = tf.constant(1.0) with tf.control_dependencies([c]): # d get the control dependency. d = tf.constant(2.0) # Partitioned variables do not. var_x = tf.get_variable( "x", initializer=tf.ones_initializer([2]), partitioner=tf.variable_axis_size_partitioner(4)) ops_before_read = session.graph.get_operations() var_x.as_tensor() # Caches the ops for subsequent reads. reading_ops = [op for op in session.graph.get_operations() if op not in ops_before_read] self.assertEqual([c.op], d.op.control_inputs) # Tests that no control dependencies are added to reading a partitioned # variable which is similar to reading a variable. for op in reading_ops: self.assertEqual([], op.control_inputs)
Example #2
Source File: partitioned_variables_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testConcat(self): with self.test_session() as session: var_x = tf.get_variable( "x", initializer=tf.constant([1., 2.]), partitioner=tf.variable_axis_size_partitioner(4)) c = tf.constant(1.0) with tf.control_dependencies([c]): ops_before_concat = session.graph.get_operations() value = var_x._concat() # pylint: disable=protected-access concat_ops = [op for op in session.graph.get_operations() if op not in ops_before_concat] concat_control_inputs = [ci for op in concat_ops for ci in op.control_inputs] self.assertTrue( c.op in concat_control_inputs, "var_x._concat() should get control dependencies from its scope.") tf.global_variables_initializer().run() self.assertAllClose(value.eval(), var_x.as_tensor().eval())
Example #3
Source File: partitioned_variables_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def _testVariableAxisSizePartitioner(self, name, axis, max_shard_bytes, expected_axis_shards, expected_partitions, max_shards=None): partitioner = tf.variable_axis_size_partitioner( axis=axis, max_shard_bytes=max_shard_bytes, max_shards=max_shards) with tf.variable_scope("root", partitioner=partitioner): v0 = tf.get_variable(name, dtype=tf.float32, shape=(4, 8, 16, 32)) v0_list = v0._get_variable_list() v0_part = v0._get_partitions() self.assertEqual(len(v0_list), expected_axis_shards) self.assertAllEqual(v0_part, expected_partitions)
Example #4
Source File: localhost_cluster_performance_test.py From deep_image_model with Apache License 2.0 | 5 votes |
def benchmark_create_1000_partitions_with_100_parameter_servers(self): workers, _ = create_local_cluster(num_workers=1, num_ps=100) worker_sessions = [tf.Session(w.target) for w in workers] worker = worker_sessions[0] partition_sizes = (1, 512, 1024*32, 1024*128) partitioned = [] for partition_size in partition_sizes: # max_shard_bytes is 4, shape is 1000*partition_size float32s which should # partition into 1000 shards, each containing partition_size float32s. print("Building partitioned variable with %d floats per partition" % partition_size) with tf.device(tf.train.replica_device_setter(ps_tasks=100)): partitioned_ix = tf.get_variable( "partitioned_%d" % partition_size, shape=[1000 * partition_size], dtype=tf.float32, # Each partition to have exactly N float32s partitioner=tf.variable_axis_size_partitioner( max_shard_bytes=4 * partition_size)) # Concatenates along axis 0 partitioned.append(tf.convert_to_tensor(partitioned_ix)) tf.global_variables_initializer().run(session=worker) for ix, partition_size in enumerate(partition_sizes): print("Running benchmark having partitions with %d floats" % partition_size) self.run_op_benchmark( worker, partitioned[ix], name=("read_concat_1000_partitions_from_" "100_parameter_servers_partsize_%d_floats" % partition_size))