## Background
PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.

The data structure to describe the compile time graph should be able to be serialized for distributed training. So we use proto message to describe the graph: OpDesc to describe computation and VarDesc to describe data.

PaddlePaddle will generate these data structure according to user's description and do some optimization, such as:

1. InferShape. Infer the Output size according to Input size and set them into VarDesc.
1. memory optimise and reuse. Scan all the memory that will be used and reuse some memory that is allocated before but will not be used anymore to reduce memory.

VarDesc is used to describe different kinds of Variable value, such as Tensor, scalar, and scope:

## Definition of VarDesc in Proto

```
message LoDTensorDesc {
  enum Type {
    INT16 = 1;
    INT32 = 2;
    INT64 = 3;
    FP16 = 4;
    FP32 = 5;
    DOUBLE = 6
    BOOL = 7;
  }

  Type element_type = 1;
  repeated int dims = 2; // [UNK, UNK, 6000] is saved as [-1, -1, 6000]
  optional int lod_level [default=0] = 3;
  repeated int32 int16_val = 4 [packed = true]; // INT16
  repeated int32 int32_val = 5 [packed = true]; // INT32
  repeated int64 int64_val = 6 [packed = true]; // INT64
  repeated float float_val = 7 [packed = true]; // FP32
  repeated double double_val = 8 [packed = true]; // DOUBLE
  repeated bool bool_val = 9 [packed = true];  // BOOL
}

message VarDesc {
  enum Type {
    INT = 0;
    FLOAT = 1;
    STRING = 2;
    INTS = 3;
    FLOATS = 4;
    STRINGS = 5;
    LOD_TENSOR = 6;
  }

  message Value {
    optional int32 i = 1;
    optional float f = 2;
    optional string s = 3;
    repeated int32 ints = 4;
    repeated float floats = 5;
    repeated string strings = 6;
    optional LodTesnorDesc lod_tensor = 7; // when type==LOD_TENSOR
  }

  required string name = 1;
  required Type type = 2;
  required Value value = 3;
}

```

## Definition of Variable in Python

There is a class `Variable` in python to help create Variable.

```python
class Variable(object):
  def __init__(self,
               name=None,
               data_type=None,
               shape=None,
               value=None,
               trainable=True):
```

create a variable with a tensor value.

```python
a = Variable("X", shape=[784, 10], data_type=pd.INT32, value=0)
```

or create a Variable with a string value

```python
a = Variable("X", data_type=pd.STRING, value="aa")
```