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On this page
  • Overview
  • How to Deploy a Model
  • 1. Run Serving App
  • 2. Deploy Model
  • How to Predict
  • Apply Model in Platform
  • NN Image Labeling
  • Inference via API

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  1. Neural Networks
  2. Inference & Deployment

Supervisely Serving Apps

PreviousInference & DeploymentNextDeploy & Predict with Supervisely SDK

Last updated 2 months ago

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Overview

This section covers the deployment and inference of models using Supervisely Serving Apps. This is a simple way to deploy a model on the Supervisely Platform with a convenient web UI. It allows you to deploy both the pretrained models, such as YOLOv8, and your own models trained in Supervisely.

In this guide, you'll learn how to run a Serving App, deploy models and make predictions.

You can interact with the deployed model in different ways:

How to Deploy a Model

1. Run Serving App

Find your app

Run app

Run an app by clicking the "Run App" button, and the app interface will be opened in a new tab. In the first time, the app will download a docker image, it may take some time. After the app is started, you'll see the Serving GUI.

Serving App Interface:

If the app did not open automatically, you can find it in the "Apps" section of the platform.

Open the app from the Apps section

2. Deploy Model

After you've opened the app, you'll see the app's interface with the following sections:

  1. "Pretrained" and "Custom" Tabs: In the pretrained tab, you can find the list of available pretrained checkpoints to select one. In the custom tab, you can select your own model trained in Supervisely.

  2. Device Selector: Choose the device to load the model on.

  3. Serve Button: Click the "Serve" button to start the deployment process. The app will download model weights, load it on device, and start the FastAPI server for API requests.

After the model is deployed, you'll see a green checkmark with text "Model has been successfully loaded on device", and the "Full model info" section will be filled with the model's information.

How to Predict

Apply Model in Platform

After the model is deployed, you can apply it using the NN Applying apps. They allow you to predict the entire projects or datasets:

Search "apply" to find available apps:

NN Image Labeling

Run NN Image Labeling

Connect to the deployed model

In the app interface, select the deployed model from the list. Click the "Connect" button. After that, you can start annotating images with the help of the model.

Inference via API

You can automate the inference process by sending requests to the deployed model via API. A running Serving App acts like a server and can process inference requests.

import os
from dotenv import load_dotenv
import supervisely as sly

# Get your Serving App's task_id from the Supervisely platform
task_id = 27209

# init sly.Api
load_dotenv(os.path.expanduser("~/supervisely.env"))
api = sly.Api()

# Create Inference Session
session = sly.nn.inference.Session(api, task_id=task_id)

prediction = session.inference_image_id(image_id=123)

predictions = session.inference_project_id(project_id=123)

Apply model in platform: You can predict easily with NN Applying apps. They allows you to predict the entire projects or datasets: , .

NN Image Labeling: You can run the app right in the labeling interface and annotate images with the help of a deployed model.

Inference via API: Each model deployed in the Supervisely Platform acts like a server and ready to process inference requests via API. You can communicate with the model using Supervisely SDK (see the ), which has convenient methods for efficient model inference on images, projects, and videos. We also support Tracking-by-detection algorithms, such as BoT-Sort.

To deploy a model, you need to run a Serving App. You can search an app on the , or find available serving apps in the section of the platform.

Open Labeling Tool and run the app from the "Apps" panel. The app will be opened in the labeling interface.

See the for more details.

🔮
Apply NN to Images
Apply NN to Videos
NN Image Labeling
Inference API Tutorial
Ecosystem
Neural Networks
Apply NN to Images
Apply NN to Videos
Apply Classifier to Images
Apply Florence-2 + SAM 2 to Images
NN Image Labeling
Inference API Tutorial
Supervisely Serving Apps
Neural Networks Section
Serving App Interface
Apps Section
Model Deployed
Apply Apps
NN Image Labeling
Apply Model in Labeling