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Upgrade steps for Azure Container Instances web services to managed online endpoints

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Managed online endpoints help to deploy your ML models in a turnkey manner. Managed online endpoints work with powerful CPU and GPU machines in Azure in a scalable, fully managed way. Managed online endpoints take care of serving, scaling, securing, and monitoring your models, freeing you from the overhead of setting up and managing the underlying infrastructure. Details can be found on Deploy and score a machine learning model by using an online endpoint.

You can deploy directly to the new compute target with your previous models and environments, or use the scripts provided by us to export the current services and then deploy to the new compute without affecting your existing services. If you regularly create and delete Azure Container Instances (ACI) web services, we strongly recommend the deploying directly and not using the scripts.

Important

The scoring URL will be changed after upgrade. For example, the scoring url for ACI web service is like http://aaaaaa-bbbbb-1111.westus.azurecontainer.io/score. The scoring URI for a managed online endpoint is like https://endpoint-name.westus.inference.ml.azure.com/score.

Supported scenarios and differences

Auth mode

No auth isn't supported for managed online endpoint. If you use the upgrade scripts, it will convert it to key auth. For key auth, the original keys will be used. Token-based auth is also supported.

TLS

For ACI service secured with HTTPS, you don't need to provide your own certificates anymore, all the managed online endpoints are protected by TLS.

Custom DNS name isn't supported.

Resource requirements

ContainerResourceRequirements isn't supported, you can choose the proper SKU for your inferencing. The upgrade tool will map the CPU/Memory requirement to corresponding SKU. If you choose to redeploy manually through CLI/SDK V2, we also suggest the corresponding SKU for your new deployment.

CPU request Memory request in GB Suggested SKU
(0, 1] (0, 1.2] DS1 V2
(1, 2] (1.2, 1.7] F2s V2
(1, 2] (1.7, 4.7] DS2 V2
(1, 2] (4.7, 13.7] E2s V3
(2, 4] (0, 5.7] F4s V2
(2, 4] (5.7, 11.7] DS3 V2
(2, 4] (11.7, 16] E4s V3

"(" means greater than and "]" means less than or equal to. For example, “(0, 1]” means “greater than 0 and less than or equal to 1”.

Important

When upgrading from ACI, there will be some changes in how you'll be charged. See our blog for a rough cost comparison to help you choose the right VM SKUs for your workload.

Network isolation

For private workspace and VNet scenarios, see Use network isolation with managed online endpoints.

Important

As there are many settings for your workspace and VNet, we strongly suggest that redeploy through the Azure CLI extension v2 for machine learning instead of the script tool.

Not supported

  • EncryptionProperties for ACI container isn't supported.
  • ACI web services deployed through deploy_from_model and deploy_from_image isn't supported by the upgrade tool. Redeploy manually through CLI/SDK V2.

Upgrade steps

With our CLI or SDK

Redeploy manually with your model files and environment definition. You can find our examples on azureml-examples. Specifically, this is the SDK example for managed online endpoint.

With our upgrade tool

This tool will automatically create new managed online endpoint based on your existing web services. Your original services won't be affected. You can safely route the traffic to the new endpoint and then delete the old one.

Note

The upgrade script is a sample script and is provided without a service level agreement (SLA).

Use the following steps to run the scripts:

Tip

The new endpoint created by the scripts will be created under the same workspace.

  1. Use a bash shell to run the scripts. For example, a terminal session on Linux or the Windows Subsystem for Linux (WSL).

  2. Install Python SDK V1 to run the Python script.

  3. Install Azure CLI.

  4. Clone the repository to your local env. For example, git clone https://github.com/Azure/azureml-examples.

  5. Edit the following values in the migrate-service.sh file. Replace the values with ones that apply to your configuration.

    • <SUBSCRIPTION_ID> - The subscription ID of your Azure subscription that contains your workspace.
    • <RESOURCEGROUP_NAME> - The resource group that contains your workspace.
    • <WORKSPACE_NAME> - The workspace name.
    • <SERVICE_NAME> - The name of your existing ACI service.
    • <LOCAL_PATH> - A local path where resources and templates used by the script are downloaded.
    • <NEW_ENDPOINT_NAME> - The name of the new endpoint that will be created. We recommend that the new endpoint name is different from the previous service name. Otherwise, the original service won't be displayed if you check your endpoints on the portal.
    • <NEW_DEPLOYMENT_NAME> - The name of the deployment to the new endpoint.

  6. Run the bash script. For example, ./migrate-service.sh. It will take about 5-10 minutes to finish the new deployment.

    Tip

    If you receive an error that the script is not executable, or an editor opens when you try to run the script, use the following command to mark the script as executable:

    chmod +x migrate-service.sh

  7. After the deployment is completes successfully, you can verify the endpoint with the az ml online-endpoint invoke command.

Contact us

If you have any questions or feedback on the upgrade script, contact us at moeonboard@microsoft.com.

Next steps