## How to save a servable model of Paddle Serving?
- Currently, paddle serving provides a save_model interface for users to access, the interface is similar with `save_inference_model` of Paddle.
``` python
import paddle_serving_client.io as serving_io
serving_io.save_model("imdb_model", "imdb_client_conf",
                      {"words": data}, {"prediction": prediction},
                      fluid.default_main_program())
```
`imdb_model` is the server side model with serving configurations. `imdb_client_conf` is the client rpc configurations. Serving has a 
dictionary for `Feed` and `Fetch` variables for client to assign. In the example, `{"words": data}` is the feed dict that specify the input of saved inference model. `{"prediction": prediction}` is the fetch dic that specify the output of saved inference model. An alias name can be defined for feed and fetch variables. An example of how to use alias name
 is as follows:
 ``` python
 from paddle_serving_client import Client
import sys

client = Client()
client.load_client_config(sys.argv[1])
client.connect(["127.0.0.1:9393"])

for line in sys.stdin:
    group = line.strip().split()
    words = [int(x) for x in group[1:int(group[0]) + 1]]
    label = [int(group[-1])]
    feed = {"words": words, "label": label}
    fetch = ["acc", "cost", "prediction"]
    fetch_map = client.predict(feed=feed, fetch=fetch)
    print("{} {}".format(fetch_map["prediction"][1], label[0]))
 ```
