# Remote Operator

*author: shiyu*

<br />

## Desription
Remote triton server.

<br />



## Code Example

 *Run with ops:*

```python
from towhee.dc2 import ops

c = ops.triton.client('<your-ip>:<your-port>')
res = c('<your-data>')
```

*Run with pipeline:*

```python
from towhee.dc2 import ops, pipe

p = (pipe.input('data')
        .map('data', 'res', ops.triton.client('<your-ip>:<your-port>'))
        .output('res'))
        
p('<your-data>').get()
```


<br />



## Factory Constructor

Create the operator via the following factory method:



***towhee.remote(uri, mode='infer', model_name='pipeline', protocol='grpc')***



**Parameters:**

***url:*** *str*

IP address and port for the triton server, such as '<host>:<port>' and  '127.0.0.1:8001'.

***model_name:*** *str*

The name of the model to run inference, defaults to 'pipline'.



<br />



## Interface

**Parameters:**

***data:***

The data to your triton server.



**Returns:**

Return the results in triton.


# More Resources

- [Multimodal RAG with Milvus and GPT-4o](https://zilliz.com/event/multimodal-rag-with-milvus-and-gpt-4o): Join us for a webinar for a demo of multimodal RAG with Milvus and GPT-4o
- [The Journey to Optimizing Billion-scale Image Search (2/2) - Zilliz blog](https://zilliz.com/blog/optimizing-billion-scale-image-search-milvus-part-2): A case study with UPYUN, part II
- [Multimodal RAG with Milvus and GPT-4o](https://zilliz.com/event/multimodal-rag-with-milvus-and-gpt-4o/success): Join us for a webinar for a demo of multimodal RAG with Milvus and GPT-4o
- [The GUI for Milvus - Attu](https://zilliz.com/attu): Attu is an all-in-one Milvus administration tool, enabling you to dramatically reduce the DevOps cost of managing Milvus.