[TensorFlow] Python ⇔ Protokollpuffer ⇔ GPU / Distributed Computing
Einführung
TensorFlow, ein Framework für Deep Learning, reduziert das Umschalten zwischen Python und GPU / verteilten Computern und optimiert Berechnungen, indem verschiedene Prozesse über Protokollpuffer ausgelagert werden.
Protokollpuffer ist eine Technologie, die das verteilte Computing von Google unterstützt und ein Mechanismus zum sprachunabhängigen und plattformunabhängigen Serialisieren von Datenstrukturen ist. Derzeit werden C ++, C #, GO, Java und Python unterstützt.
TensorFlow ist ein Mechanismus, der ein Diagramm mit der Verarbeitung als Knoten erstellt und alles auf einmal berechnet. Grafikbeispiel
Im Folgenden möchte ich sehen, wie das TensorFlow-Diagramm im Protokollpufferformat serialisiert wird.
Beispielprogramm
Fügen wir mit TensorFlow hinzu. Add 1 wird dreimal mit dem Anfangswert 0 ausgeführt.
add.py
import tensorflow as tf
state = tf.Variable(0, name="counter")
one = tf.constant(1)
new_value = tf.add(state, one)
update = tf.assign(state, new_value)
init_op = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init_op)
print(sess.run(state))
for _ in range(3):
sess.run(update)
print(sess.run(state))
TensorBoard-Diagramm
** Grafik mit TensorBoard anzeigen **
Protocol Buffers Im Folgenden sehen wir uns an, was passiert, wenn ein Python-Programm, das TensorFlow verwendet, in einen Protokollpufferknoten konvertiert wird.
Variablendefinition
- Python
state = tf.Variable(0, name="counter")
- Protokollpuffer: Variablen mit dem Namen counter
name: "counter"
op: "Variable"
attr {
key: "container"
value {
s: ""
}
}
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "shape"
value {
shape {
}
}
}
attr {
key: "shared_name"
value {
s: ""
}
}
- Protokollpuffer: Definition des Anfangswertes 0 (konstant)
name: "counter/initial_value"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
}
int_val: 0
}
}
}
- Protokollpuffer: Weisen Sie dem variablen Zähler den Anfangswert 0 zu
name: "counter/Assign"
op: "Assign"
input: "counter"
input: "counter/initial_value"
attr {
key: "T"
value {
type: DT_INT32
}
}
attr {
key: "_class"
value {
list {
s: "loc:@counter"
}
}
}
attr {
key: "use_locking"
value {
b: true
}
}
attr {
key: "validate_shape"
value {
b: true
}
}
- Protokollpuffer: Variablenzähler lesen
name: "counter/read"
op: "Identity"
input: "counter"
attr {
key: "T"
value {
type: DT_INT32
}
}
attr {
key: "_class"
value {
list {
s: "loc:@counter"
}
}
}
Definition der Konstante (Zusatzwert)
- Python
one = tf.constant(1)
- Protokollpuffer: Definition der Konstante 1
name: "Const"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
}
int_val: 1
}
}
}
Definition der Additionsoperation
- Python
one = tf.constant(1)
- Protokollpuffer: Definieren Sie Additionsoperationen
name: "Add"
op: "Add"
input: "counter/read"
input: "Const"
attr {
key: "T"
value {
type: DT_INT32
}
}
Setzen Sie den Wert des Additionsergebnisses in die Variable ein
- Python
update = tf.assign(state, new_value)
- Protokollpuffer: Ersetzen Sie die Additionsergebnisse durch Werte in Variablen
name: "Assign"
op: "Assign"
input: "counter"
input: "Add"
attr {
key: "T"
value {
type: DT_INT32
}
}
attr {
key: "_class"
value {
list {
s: "loc:@counter"
}
}
}
attr {
key: "use_locking"
value {
b: true
}
}
attr {
key: "validate_shape"
value {
b: true
}
}
abschließend
Wenn Sie verstehen, dass TensorFlow hinter den Kulissen eines Python-Programms Protokollpuffer verwendet, werden Sie verstehen, warum die TensorFlow-Verarbeitung an diesem Speicherort angeschlossen ist.
TensorFlow bietet verschiedene grundlegende Prozesse (API).
- Grafik
- Variable
- Tensol
- Mathematik
- String
- Steuerungssyntax
- Behauptung
- Bild
- Video
- Eingabe / Ausgabe
- Neuronales Netz
- Betriebsübersicht
- Unit Test
Es ist auch interessant zu versuchen, TensorFlow als GPU-Computing / Distributed-Computing-Framework und nicht als Deep-Learning-Framework zu verwenden.
(Referenz)
Grafikanzeigeprogramm
graph = tf.get_default_graph()
summary_writer = tf.train.SummaryWriter('log_valiable', graph)
operations = graph.get_operations()
for operation in operations:
print("======================")
print("=== name ===")
print(operation.name)
print("=== type ===")
print(operation.type)
print("=== inputs ===")
for input in operation.inputs:
print(input)
print("=== control_inputs ===")
for control_input in operation.control_inputs:
print(control_input)
print("=== outputs ===")
for output in operation.outputs:
print(output)
print("=== node_def ===")
print(operation.node_def)
print("=== op_def ===")
print(operation.op_def)
print("=== traceback ===")
print(operation.traceback)
print("")
Konsolenausgabe
0
1
2
3
======================
=== name ===
counter/initial_value
=== type ===
Const
=== inputs ===
=== control_inputs ===
=== outputs ===
Tensor("counter/initial_value:0", shape=(), dtype=int32)
=== node_def ===
name: "counter/initial_value"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
}
int_val: 0
}
}
}
=== op_def ===
None
=== traceback ===
[('./valiable.py', 5, '<module>', 'state = tf.Variable(0, name="counter")'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py', 215, '__init__', 'dtype=dtype)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py', 293, '_init_from_args', 'initial_value, name="initial_value", dtype=dtype)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 657, 'convert_to_tensor', 'ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py', 180, '_constant_tensor_conversion_function', 'return constant(v, dtype=dtype, name=name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py', 167, 'constant', 'attrs={"value": tensor_value, "dtype": dtype_value}, name=name).outputs[0]'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 2380, 'create_op', 'original_op=self._default_original_op, op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 1298, '__init__', 'self._traceback = _extract_stack()')]
======================
=== name ===
counter
=== type ===
Variable
=== inputs ===
=== control_inputs ===
=== outputs ===
Tensor("counter:0", shape=(), dtype=int32_ref)
=== node_def ===
name: "counter"
op: "Variable"
attr {
key: "container"
value {
s: ""
}
}
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "shape"
value {
shape {
}
}
}
attr {
key: "shared_name"
value {
s: ""
}
}
=== op_def ===
name: "Variable"
output_arg {
name: "ref"
type_attr: "dtype"
is_ref: true
}
attr {
name: "shape"
type: "shape"
}
attr {
name: "dtype"
type: "type"
}
attr {
name: "container"
type: "string"
default_value {
s: ""
}
}
attr {
name: "shared_name"
type: "string"
default_value {
s: ""
}
}
is_stateful: true
=== traceback ===
[('./valiable.py', 5, '<module>', 'state = tf.Variable(0, name="counter")'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py', 215, '__init__', 'dtype=dtype)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py', 300, '_init_from_args', 'name=name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/state_ops.py', 146, 'variable_op', 'container=container, shared_name=shared_name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_state_ops.py', 490, '_variable', 'name=name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py', 749, 'apply_op', 'op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 2380, 'create_op', 'original_op=self._default_original_op, op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 1298, '__init__', 'self._traceback = _extract_stack()')]
======================
=== name ===
counter/Assign
=== type ===
Assign
=== inputs ===
Tensor("counter:0", shape=(), dtype=int32_ref)
Tensor("counter/initial_value:0", shape=(), dtype=int32)
=== control_inputs ===
=== outputs ===
Tensor("counter/Assign:0", shape=(), dtype=int32_ref)
=== node_def ===
name: "counter/Assign"
op: "Assign"
input: "counter"
input: "counter/initial_value"
attr {
key: "T"
value {
type: DT_INT32
}
}
attr {
key: "_class"
value {
list {
s: "loc:@counter"
}
}
}
attr {
key: "use_locking"
value {
b: true
}
}
attr {
key: "validate_shape"
value {
b: true
}
}
=== op_def ===
name: "Assign"
input_arg {
name: "ref"
type_attr: "T"
is_ref: true
}
input_arg {
name: "value"
type_attr: "T"
}
output_arg {
name: "output_ref"
type_attr: "T"
is_ref: true
}
attr {
name: "T"
type: "type"
}
attr {
name: "validate_shape"
type: "bool"
default_value {
b: true
}
}
attr {
name: "use_locking"
type: "bool"
default_value {
b: true
}
}
allows_uninitialized_input: true
=== traceback ===
[('./valiable.py', 5, '<module>', 'state = tf.Variable(0, name="counter")'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py', 215, '__init__', 'dtype=dtype)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py', 317, '_init_from_args', 'validate_shape=validate_shape).op'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_state_ops.py', 45, 'assign', 'use_locking=use_locking, name=name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py', 749, 'apply_op', 'op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 2380, 'create_op', 'original_op=self._default_original_op, op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 1298, '__init__', 'self._traceback = _extract_stack()')]
======================
=== name ===
counter/read
=== type ===
Identity
=== inputs ===
Tensor("counter:0", shape=(), dtype=int32_ref)
=== control_inputs ===
=== outputs ===
Tensor("counter/read:0", shape=(), dtype=int32)
=== node_def ===
name: "counter/read"
op: "Identity"
input: "counter"
attr {
key: "T"
value {
type: DT_INT32
}
}
attr {
key: "_class"
value {
list {
s: "loc:@counter"
}
}
}
=== op_def ===
name: "Identity"
input_arg {
name: "input"
type_attr: "T"
}
output_arg {
name: "output"
type_attr: "T"
}
attr {
name: "T"
type: "type"
}
=== traceback ===
[('./valiable.py', 5, '<module>', 'state = tf.Variable(0, name="counter")'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py', 215, '__init__', 'dtype=dtype)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py', 327, '_init_from_args', 'self._snapshot = array_ops.identity(self._variable, name="read")'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py', 1128, 'identity', 'result = _op_def_lib.apply_op("Identity", input=input, name=name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py', 749, 'apply_op', 'op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 2380, 'create_op', 'original_op=self._default_original_op, op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 1298, '__init__', 'self._traceback = _extract_stack()')]
======================
=== name ===
Const
=== type ===
Const
=== inputs ===
=== control_inputs ===
=== outputs ===
Tensor("Const:0", shape=(), dtype=int32)
=== node_def ===
name: "Const"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
}
int_val: 1
}
}
}
=== op_def ===
None
=== traceback ===
[('./valiable.py', 7, '<module>', 'one = tf.constant(1)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py', 167, 'constant', 'attrs={"value": tensor_value, "dtype": dtype_value}, name=name).outputs[0]'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 2380, 'create_op', 'original_op=self._default_original_op, op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 1298, '__init__', 'self._traceback = _extract_stack()')]
======================
=== name ===
Add
=== type ===
Add
=== inputs ===
Tensor("counter/read:0", shape=(), dtype=int32)
Tensor("Const:0", shape=(), dtype=int32)
=== control_inputs ===
=== outputs ===
Tensor("Add:0", shape=(), dtype=int32)
=== node_def ===
name: "Add"
op: "Add"
input: "counter/read"
input: "Const"
attr {
key: "T"
value {
type: DT_INT32
}
}
=== op_def ===
name: "Add"
input_arg {
name: "x"
type_attr: "T"
}
input_arg {
name: "y"
type_attr: "T"
}
output_arg {
name: "z"
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_HALF
type: DT_FLOAT
type: DT_DOUBLE
type: DT_UINT8
type: DT_INT8
type: DT_INT16
type: DT_INT32
type: DT_INT64
type: DT_COMPLEX64
type: DT_COMPLEX128
type: DT_STRING
}
}
}
=== traceback ===
[('./valiable.py', 8, '<module>', 'new_value = tf.add(state, one)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_math_ops.py', 71, 'add', 'result = _op_def_lib.apply_op("Add", x=x, y=y, name=name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py', 749, 'apply_op', 'op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 2380, 'create_op', 'original_op=self._default_original_op, op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 1298, '__init__', 'self._traceback = _extract_stack()')]
======================
=== name ===
Assign
=== type ===
Assign
=== inputs ===
Tensor("counter:0", shape=(), dtype=int32_ref)
Tensor("Add:0", shape=(), dtype=int32)
=== control_inputs ===
=== outputs ===
Tensor("Assign:0", shape=(), dtype=int32_ref)
=== node_def ===
name: "Assign"
op: "Assign"
input: "counter"
input: "Add"
attr {
key: "T"
value {
type: DT_INT32
}
}
attr {
key: "_class"
value {
list {
s: "loc:@counter"
}
}
}
attr {
key: "use_locking"
value {
b: true
}
}
attr {
key: "validate_shape"
value {
b: true
}
}
=== op_def ===
name: "Assign"
input_arg {
name: "ref"
type_attr: "T"
is_ref: true
}
input_arg {
name: "value"
type_attr: "T"
}
output_arg {
name: "output_ref"
type_attr: "T"
is_ref: true
}
attr {
name: "T"
type: "type"
}
attr {
name: "validate_shape"
type: "bool"
default_value {
b: true
}
}
attr {
name: "use_locking"
type: "bool"
default_value {
b: true
}
}
allows_uninitialized_input: true
=== traceback ===
[('./valiable.py', 9, '<module>', 'update = tf.assign(state, new_value)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_state_ops.py', 45, 'assign', 'use_locking=use_locking, name=name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py', 749, 'apply_op', 'op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 2380, 'create_op', 'original_op=self._default_original_op, op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 1298, '__init__', 'self._traceback = _extract_stack()')]
======================
=== name ===
init
=== type ===
NoOp
=== inputs ===
=== control_inputs ===
name: "counter/Assign"
op: "Assign"
input: "counter"
input: "counter/initial_value"
attr {
key: "T"
value {
type: DT_INT32
}
}
attr {
key: "_class"
value {
list {
s: "loc:@counter"
}
}
}
attr {
key: "use_locking"
value {
b: true
}
}
attr {
key: "validate_shape"
value {
b: true
}
}
=== outputs ===
=== node_def ===
name: "init"
op: "NoOp"
input: "^counter/Assign"
=== op_def ===
name: "NoOp"
=== traceback ===
[('./valiable.py', 11, '<module>', 'init_op = tf.initialize_all_variables()'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py', 1063, 'initialize_all_variables', 'return initialize_variables(all_variables())'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variables.py', 1051, 'initialize_variables', 'return control_flow_ops.group(*[v.initializer for v in var_list], name=name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/control_flow_ops.py', 2645, 'group', 'return _GroupControlDeps(dev, deps, name=name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/control_flow_ops.py', 2603, '_GroupControlDeps', 'return no_op(name=name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_control_flow_ops.py', 184, 'no_op', 'result = _op_def_lib.apply_op("NoOp", name=name)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py', 756, 'apply_op', 'op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 2380, 'create_op', 'original_op=self._default_original_op, op_def=op_def)'), ('/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py', 1298, '__init__', 'self._traceback = _extract_stack()')]
Benchmark für Protokollpuffer
Ich habe tatsächlich überprüft, ob die Protokollpuffer wirklich langsam sind @ 24.08.2016
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