Deployment on Azure AKS

You can use the API Deployer Kubernetes integration to deploy your API Services on Azure Kubernetes Service (AKS).


Create your ACR registry

Follow the Azure documentation on how to create your ACR registry. We recommend that you pay extra attention to the pricing plan since it is directly related to the registry storage capacity.

Create your AKS cluster

Follow Azure’s documentation on how to create your AKS cluster. We recommend that you allocate at least 8GB of memory for each cluster node.

Once the cluster is created, you must modify its IAM credentials to grant it access to ACR (Kubernetes secret mode is not supported). This is required for the worker nodes to pull images from the registry.

Prepare your local az, docker and kubectl commands

Follow the Azure documentation to make sure that:

  • Your local (on the DSS machine) az command is properly logged in. As of October 2019, this implies running the az login --service-principal --username client_d --password client_secret --tenant tenant_id command. You must use a service principal that has sufficient IAM permissions to write to ACR and full control on AKS.

  • Your local (on the DSS machine) docker command can successfully push images to the ACR repository. As of October 2019, this implies running the az acr login --name your-registry-name.

  • Your local (on the DSS machine) kubectl command can interact with the cluster. As of October 2019, this implies running the az aks get-credentials --resource-group your-rg --name your-cluster-name command.


Cluster management has been tested with the following versions of Kubernetes:
  • 1.23

  • 1.24

  • 1.25

  • 1.26

  • 1.27

  • 1.28

  • 1.29

There is no known issue with other Kubernetes versions.

Setup the infrastructure

Follow the usual setup steps as indicated in Setting up. In particular, to set up the image registry, in the API Deployer go to Infrastructures > your-infrastructure > Settings, and in the “Kubernetes cluster” section, set the “Registry host” field to


You’re now ready to deploy your API Services to Azure AKS.

Using GPUs

Azure provides GPU-enabled instances with NVidia GPUs. Several steps are required in order to use them for API Deployer deployments.

Building an image with CUDA support

The base image that is built by default does not have CUDA support and cannot use NVidia GPUs. You need to build a CUDA-enabled base image. To enable CUDA add the --with-cuda option to the command line:

./bin/dssadmin build-base-image --type apideployer --with-cuda

We recommend that you give this image a specific tag using the --tag option and keep the default base image “pristine”. We also recommend that you add the DSS version number in the image tag.

./bin/dssadmin build-base-image --type apideployer --with-cuda --tag dataiku-apideployer-base-cuda:X.Y.Z

where X.Y.Z is your DSS version number


  • This image contains CUDA 10.0 and CuDNN 7.6. You can use --cuda-version X.Y to specify another DSS-provided version (9.0, 10.0, 10.1, 10.2, 11.0 and 11.2 are available). If you require other CUDA versions, you would have to create a custom image.

  • Remember that depending on which CUDA version you build the base image (by default 10.0) you will need to use the corresponding tensorflow version.


After each upgrade of DSS, you must rebuild all base images and update code envs.

If you used a specific tag, go to the infrastructure settings, and in the “Base image tag” field, enter dataiku-apideployer-base-cuda:X.Y.Z

Create a cluster with GPUs

Follow Azure documentation for how to create a cluster with GPU.

Add a custom reservation

In order for your API Deployer deployments to be located on nodes with GPU devices, and for AKS to configure the CUDA driver on your containers, the corresponding AKS pods must be created with a custom “limit” (in Kubernetes parlance) to indicate that you need a specific type of resource (standard resource types are CPU and memory).

You can configure this limit either at the infrastructure level (all deployments on this infrastructure will use GPUs) or at the deployment level.

At the infrastructure level

  • Go to Infrastructure > Settings

  • Go to “Sizing and Scaling”

  • In the “Custom limits” section, add a new entry with key and value: 1 (to request 1 GPU)

  • Don’t forget to add the new entry, and save settings

At the deployment level

  • Go to Deployment > Settings

  • Go to “Sizing and Scaling”

  • Enable override of infrastructure settings in the “Container limits” section

  • In the “Custom limits” section, add a new entry with key and value: 1 (to request 1 GPU)

  • Don’t forget to add the new entry, and save settings


You can now deploy your GPU-requiring deployments.

This applies to:

  • Python functions (your endpoint needs to use a code environment that includes a CUDA-using package like tensorflow-gpu)

  • Python predictions