How to use a dedicated deployment

Send Inference Requests to Your Dedicated Deployment

Use Python SDK

Please install the latest version of our Python SDK inference_sdk with pip install --upgrade inference-sdk.

When your dedicated deployment is ready, copy its URL:

Copy URL of your dedicated deployment when it's ready

and paste it to the parameter api_url when initialise InferenceHTTPClient , and that's it!

Here is an example for running model inference, you can find more details in the documentation of 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")

Use HTTP API

You can also access the HTTP APIs which are listed under /docs, e.g,, https://dev-testing.roboflow.cloud/docs .

Please attach your workspace api_key as a query parameter when access these endpoints.

Here is an example for making the same request as above using HTTP API:

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)

Use Workflow UI

A dedicated deployment can also be used as the backend server for running Roboflow Workflows. Roboflow Workflows is a low-code, web-based application builder for creating computer vision applications.

After creating your workflow, click on the Running on Hosted API link in the top left corner:

Changing the backend where the workflow will execute.

Click Dedicated Deployments to see the list of your dedicated deployments, select the target deployment, then click Connect:

Select a target dedicated deployment as the backend server for workflow execution.

Now you are ready to use your dedicated deployment in the workflow editor.