> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/roboflow/roboflow-hi/deploy/dedicated-deployments/make-requests-to-a-dedicated-deployment.md).

# Dedicated Deployment को Requests भेजें

### Python SDK का उपयोग करें

कृपया हमारे Python SDK का नवीनतम संस्करण इंस्टॉल करें [inference\_sdk](https://pypi.org/project/inference-sdk/) के साथ `pip install --upgrade inference-sdk`.

जब आपका dedicated deployment तैयार हो जाए, तो उसका URL कॉपी करें:

<figure><img src="/files/3932df1c38e72ca652de5a07227c2f63e0ad782a" alt=""><figcaption><p>जब आपका dedicated deployment तैयार हो जाए, तो उसका URL कॉपी करें</p></figcaption></figure>

और इसे parameter में पेस्ट करें `api_url` जब initialize करें `InferenceHTTPClient` और बस इतना ही!

मॉडल inference चलाने का एक उदाहरण यहाँ है, आप अधिक विवरण इसमें पा सकते हैं [inference\_sdk की documentation](https://inference.roboflow.com/inference_helpers/inference_sdk/).

```
from inference_sdk import InferenceHTTPClient

CLIENT = InferenceHTTPClient(
    api_url="https://dev-testing.roboflow.cloud",
    api_key="ROBOFLOW_API_KEY"
)

image_url = "https://source.roboflow.com/pwYAXv9BTpqLyFfgQoPZ/u48G0UpWfk8giSw7wrU8/original.jpg"
result = CLIENT.infer(image_url, model_id="soccer-players-5fuqs/1")
```

### HTTP API का उपयोग करें

आप यह भी एक्सेस कर सकते हैं [HTTP APIs](https://inference.roboflow.com/api/) जो यहाँ सूचीबद्ध हैं `/docs`, उदाहरण के लिए,, `https://dev-testing.roboflow.cloud/docs` .

कृपया अपना workspace संलग्न करें `api_key` इन endpoints को एक्सेस करते समय query parameter के रूप में।

HTTP API का उपयोग करके ऊपर जैसा ही request बनाने का एक उदाहरण यहाँ है:

```
import requests
import json

api_url = "https://dev-testing.roboflow.cloud"
model_id = "soccer-players-5fuqs/1"
image_url = "https://source.roboflow.com/pwYAXv9BTpqLyFfgQoPZ/u48G0UpWfk8giSw7wrU8/original.jpg"

resp = requests.get(f"{api_url}/{model_id}", params = {"api_key": "ROBOFLOW_API_KEY", "image": image_url})
result = json.loads(resp.content)
```

### Workflow UI का उपयोग करें

एक dedicated deployment का उपयोग चलाने के लिए backend server के रूप में भी किया जा सकता है [Roboflow Workflows](https://roboflow.com/workflows/build). Roboflow Workflows एक low-code, web-based application builder है, जिसका उपयोग computer vision applications बनाने के लिए किया जाता है।

अपना workflow बनाने के बाद, **Running on Hosted API** लिंक पर ऊपरी बाएँ कोने में क्लिक करें:

<figure><img src="https://blog.roboflow.com/content/images/2024/09/Screenshot-2024-09-03-at-18.26.29.png" alt="" height="102" width="354"><figcaption><p>वह backend बदलना जहाँ workflow execute होगा।</p></figcaption></figure>

क्लिक करें **Dedicated Deployments** अपने dedicated deployments की सूची देखने के लिए, target deployment चुनें, फिर क्लिक करें **Connect**:

<figure><img src="/files/b3b53efc8bf7f4f252f7bee3838e3068495317fa" alt=""><figcaption><p>workflow execution के लिए backend server के रूप में एक target dedicated deployment चुनें।</p></figcaption></figure>

अब आप workflow editor में अपने dedicated deployment का उपयोग करने के लिए तैयार हैं।


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