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Train Yolov8 with AzureML

This repository provides an example showing how to train the yolov8 model with the az cli or the python SDK.

az cli

You can find the detailed instructions to train the yolov8 model with the az cli in this document.

Azure machine learning python SDK

Here is a notebook showing how to train the yolov8 model with the python SDK.

Deploy model for inference

Register the model from the workspace UI

You can register the model resulting from a training job. Go to your job Overview and select "Register model". Select model of type Unspecified type enable "Show all default outputs" > and select best.pt. (Note that your training environment needs azureml-mlflow==1.52.0 and mlflow==2.4.2 to enable mlflow logging and being able to retrieve the model)

Create the deployment

In azureml/deployment.yaml, specify your model

You can either specify a registered model.

model: azureml:<your-model-name>:<version>

Or specify the relative path of a local .pt file:

model:
  path: <model-relative-path-to-azureml-folder>

Note that you might need to increase the request_timeout_ms by specifying it in the deployment.yaml if running your inference takes time

Deploy your model for inference

To deploy your endpoint in your azureml workspace:

Configure your default resource group and azureml workspace:

az configure --defaults group=$YOUR_RESOURCE_GROUP workspace=$YOUR_AZ_ML_WORKSPACE
./deploy-endpoint.sh

Note your endpoint name and score uri (you can retrieve them from the azure workspace).

Test the endpoint and allocate traffic

To be able invoke our endpoint with an http client, you need to allocate traffic to your endpoint. (For more information see this doc)

az ml online-endpoint show -n $ENDPOINT_NAME --query traffic

You can see that 0% is allocated to the blue deployment, so let's allocate 100% traffic to our unique blue deployment:

az ml online-endpoint update --name $ENDPOINT_NAME --traffic "blue=100"

Now you should be able to call your endpoint with curl. You need to retrieve your endpoint key from the azure ml workspace in Endpoints > Consume > Basic consumption info.

ENDPOINT_KEY=$YOUR_ENDPOINT_KEY
curl --request POST "$SCORING_URI" --header "Authorization: Bearer $ENDPOINT_KEY" --header 'Content-Type: application/json' --data '{"image_url": "https://ultralytics.com/images/bus.jpg"}'