Here, `client.predict` function has two arguments. `feed` is a `python dict` with model input variable alias name and values. `fetch` assigns the prediction variables to be returned from servers. In the example, the name of `"x"` and `"price"` are assigned when the servable model is saved during training.
<h2align="center">Some Key Features of Paddle Serving</h2>
- Integrate with Paddle training pipeline seamlessly, most paddle models can be deployed **with one line command**.
-**Industrial serving features** supported, such as models management, online loading, online A/B testing etc.
-**Distributed Key-Value indexing** supported which is especially useful for large scale sparse features as model inputs.
-**Highly concurrent and efficient communication** between clients and servers supported.
-**Multiple programming languages** supported on client side, such as Golang, C++ and python.
### WEB service
...
...
@@ -189,6 +182,14 @@ the response is
{"result":{"price":[[18.901151657104492]]}}
```
<h2align="center">Some Key Features of Paddle Serving</h2>
- Integrate with Paddle training pipeline seamlessly, most paddle models can be deployed **with one line command**.
-**Industrial serving features** supported, such as models management, online loading, online A/B testing etc.
-**Distributed Key-Value indexing** supported which is especially useful for large scale sparse features as model inputs.
-**Highly concurrent and efficient communication** between clients and servers supported.
-**Multiple programming languages** supported on client side, such as Golang, C++ and python.