In this guide, we are going to show how to deploy a
YOLOv8
model to
EC2
using the Roboflow Inference Server. This SDK works with
YOLOv8
models trained on both Roboflow and in custom training processes outside of Roboflow.
You can deploy applications that use vision models directly onto your hardware with Roboflow Workflows. Here is an example of a Workflow that runs YOLOv8 on an image then plots bounding box results:
Below, we will show how to deploy this Workflow onto an image or video, and how to use custom models trained on or uploaded to Roboflow.
You can deploy the above workflow using a default model trained on the Microsoft COCO dataset. To deploy the model, click "Fork Workflow" to bring it into your Roboflow account. From there, you can deploy the model in two ways:
1. On images (either in the cloud or on your device), and;
2. On video streams (on your device, connected to a webcam or RTSP stream).
Once you have forked a Workflow, click "Deploy Workflow" to see instructions on how to run your model.
Let's get started!
First, create a Roboflow account and create a new project. When you have created a new project, upload your project data, then generate a new dataset version. With that version ready, you can upload your model weights to Roboflow.
Download the Roboflow Python SDK:
Then, use the following script to upload your model weights:
Read the Roboflow model weight upload documentation for more information about uploading model weights.
You will need your project name, version, API key, and model weights. The following documentation shows how to retrieve your API key and project information:
- Retrieve your Roboflow project name and version
- Retrieve your API key
Change the path in the script above to the path where your model weights are stored.
When you have configured the script above, run the code to upload your weights to Roboflow.
Now you are ready to start deploying your model.
The Roboflow Inference Server allows you to deploy computer vision models to a range of devices, including
EC2
.
You can run Roboflow Inference in Docker, or via the Python SDK.
For this guide, we will run Inference with Docker and use the Python SDK to interface with our Docker deployment. We will deploy our model on a
EC2
.
To install Inference and set up an Inference server in Docker, run:
Now you have the Roboflow Inference Server running, you can use your model on
EC2
.
For a Jetson deployment, using Docker is highly recommended since everything has to be installed special depending on your Jetpack version. Absent Docker, it is easy to accidentally do these installs incorrectly and need to reflash everything to the device.
You can run inference on images with Roboflow Inference.
Create a new Python file and add the following code:
Substitute the model name and version with the values associated with your Roboflow account and project, then run the script.
This code will return a Python object with results from your model.
You can process these results and plot them on an image with the supervision Python package:
You can run inference on videos with Roboflow Inference and the InferencePipeline feature.
Create a new Python file and add the following code:
Substitute the model name and version with the values associated with your Roboflow account and project, then run the script.
This code will run a model on frames from a webcam stream. To use RTSP, set the video_reference
value to an RTSP stream URL. To use video, set the video_reference
value to a video file path.
Predictions are annotated using the render_boxes
helper function. You can specify any function to process each prediction in the on_prediction
parameter.
To learn how to define your own callback function with custom logic, refer to the Define Custom Prediction Logic documentation.