Use a custom container to deploy a model to an online endpoint

APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current)

Learn how to use a custom container to deploy a model to an online endpoint in Azure Machine Learning.

Custom container deployments can use web servers other than the default Python Flask server used by Azure Machine Learning. Users of these deployments can still take advantage of Azure Machine Learning's built-in monitoring, scaling, alerting, and authentication.

The following table lists various deployment examples that use custom containers such as TensorFlow Serving, TorchServe, Triton Inference Server, Plumber R package, and Azure Machine Learning Inference Minimal image.

Example Script (CLI) Description
minimal/multimodel deploy-custom-container-minimal-multimodel Deploy multiple models to a single deployment by extending the Azure Machine Learning Inference Minimal image.
minimal/single-model deploy-custom-container-minimal-single-model Deploy a single model by extending the Azure Machine Learning Inference Minimal image.
mlflow/multideployment-scikit deploy-custom-container-mlflow-multideployment-scikit Deploy two MLFlow models with different Python requirements to two separate deployments behind a single endpoint using the Azure Machine Learning Inference Minimal Image.
r/multimodel-plumber deploy-custom-container-r-multimodel-plumber Deploy three regression models to one endpoint using the Plumber R package
tfserving/half-plus-two deploy-custom-container-tfserving-half-plus-two Deploy a Half Plus Two model using a TensorFlow Serving custom container using the standard model registration process.
tfserving/half-plus-two-integrated deploy-custom-container-tfserving-half-plus-two-integrated Deploy a Half Plus Two model using a TensorFlow Serving custom container with the model integrated into the image.
torchserve/densenet deploy-custom-container-torchserve-densenet Deploy a single model using a TorchServe custom container.
torchserve/huggingface-textgen deploy-custom-container-torchserve-huggingface-textgen Deploy Hugging Face models to an online endpoint and follow along with the Hugging Face Transformers TorchServe example.
triton/single-model deploy-custom-container-triton-single-model Deploy a Triton model using a custom container

This article focuses on serving a TensorFlow model with TensorFlow (TF) Serving.


Microsoft might not be able to help troubleshoot problems caused by a custom image. If you encounter problems, you might be asked to use the default image or one of the images Microsoft provides to see if the problem is specific to your image.


Before following the steps in this article, make sure you have the following prerequisites:

  • An Azure Machine Learning workspace. If you don't have one, use the steps in the Quickstart: Create workspace resources article to create one.

  • The Azure CLI and the ml extension or the Azure Machine Learning Python SDK v2:

  • You, or the service principal you use, must have Contributor access to the Azure resource group that contains your workspace. You have such a resource group if you configured your workspace using the quickstart article.

  • To deploy locally, you must have Docker engine running locally. This step is highly recommended. It helps you debug issues.

Download source code

To follow along with this tutorial, clone the source code from GitHub.

git clone --depth 1
cd azureml-examples/cli

Initialize environment variables

Define environment variables:


Download a TensorFlow model

Download and unzip a model that divides an input by two and adds 2 to the result:

wget -O $BASE_PATH/half_plus_two.tar.gz
tar -xvf $BASE_PATH/half_plus_two.tar.gz -C $BASE_PATH

Run a TF Serving image locally to test that it works

Use docker to run your image locally for testing:

docker run --rm -d -v $PWD/$BASE_PATH:$MODEL_BASE_PATH -p 8501:8501 \
sleep 10

Check that you can send liveness and scoring requests to the image

First, check that the container is alive, meaning that the process inside the container is still running. You should get a 200 (OK) response.

curl -v http://localhost:8501/v1/models/$MODEL_NAME

Then, check that you can get predictions about unlabeled data:

curl --header "Content-Type: application/json" \
  --request POST \
  --data @$BASE_PATH/sample_request.json \

Stop the image

Now that you tested locally, stop the image:

docker stop tfserving-test

Deploy your online endpoint to Azure

Next, deploy your online endpoint to Azure.

Create a YAML file for your endpoint and deployment

You can configure your cloud deployment using YAML. Take a look at the sample YAML for this example:


name: tfserving-endpoint
auth_mode: aml_token


name: tfserving-deployment
endpoint_name: tfserving-endpoint
  name: tfserving-mounted
  version: {{MODEL_VERSION}}
  path: ./half_plus_two
  MODEL_BASE_PATH: /var/azureml-app/azureml-models/tfserving-mounted/{{MODEL_VERSION}}
  MODEL_NAME: half_plus_two
  #name: tfserving
  #version: 1
      port: 8501
      path: /v1/models/half_plus_two
      port: 8501
      path: /v1/models/half_plus_two
      port: 8501
      path: /v1/models/half_plus_two:predict
instance_type: Standard_DS3_v2
instance_count: 1

There are a few important concepts to notice in this YAML/Python parameter:

Readiness route vs. liveness route

An HTTP server defines paths for both liveness and readiness. A liveness route is used to check whether the server is running. A readiness route is used to check whether the server is ready to do work. In machine learning inference, a server could respond 200 OK to a liveness request before loading a model. The server could respond 200 OK to a readiness request only after the model is loaded into memory.

For more information about liveness and readiness probes, see the Kubernetes documentation.

Notice that this deployment uses the same path for both liveness and readiness, since TF Serving only defines a liveness route.

Locating the mounted model

When you deploy a model as an online endpoint, Azure Machine Learning mounts your model to your endpoint. Model mounting allows you to deploy new versions of the model without having to create a new Docker image. By default, a model registered with the name foo and version 1 would be located at the following path inside of your deployed container: /var/azureml-app/azureml-models/foo/1

For example, if you have a directory structure of /azureml-examples/cli/endpoints/online/custom-container on your local machine, where the model is named half_plus_two:

Diagram showing a tree view of the local directory structure.

And tfserving-deployment.yml contains:

    name: tfserving-mounted
    version: 1
    path: ./half_plus_two

Then your model will be located under /var/azureml-app/azureml-models/tfserving-deployment/1 in your deployment:

Diagram showing a tree view of the deployment directory structure.

You can optionally configure your model_mount_path. It lets you change the path where the model is mounted.


The model_mount_path must be a valid absolute path in Linux (the OS of the container image).

For example, you can have model_mount_path parameter in your tfserving-deployment.yml:

name: tfserving-deployment
endpoint_name: tfserving-endpoint
  name: tfserving-mounted
  version: 1
  path: ./half_plus_two
model_mount_path: /var/tfserving-model-mount

Then your model is located at /var/tfserving-model-mount/tfserving-deployment/1 in your deployment. Note that it's no longer under azureml-app/azureml-models, but under the mount path you specified:

Diagram showing a tree view of the deployment directory structure when using mount_model_path.

Create your endpoint and deployment

Now that you understand how the YAML was constructed, create your endpoint.

az ml online-endpoint create --name tfserving-endpoint -f endpoints/online/custom-container/tfserving-endpoint.yml

Creating a deployment might take a few minutes.

az ml online-deployment create --name tfserving-deployment -f endpoints/online/custom-container/tfserving-deployment.yml --all-traffic

Invoke the endpoint

Once your deployment completes, see if you can make a scoring request to the deployed endpoint.

RESPONSE=$(az ml online-endpoint invoke -n $ENDPOINT_NAME --request-file $BASE_PATH/sample_request.json)

Delete the endpoint

Now that you successfully scored with your endpoint, you can delete it:

az ml online-endpoint delete --name tfserving-endpoint