Deployment on AWS EKS

You can use the API Deployer Kubernetes integration to deploy your API Services on AWS Elastic Kubernetes Service (EKS).


Create your EKS cluster

To create your Amazon Elastic Kubernetes Service (EKS) cluster, follow the AWS user guide. We recommend that you allocate at least 7 GB of memory for each cluster node.

Prepare your local aws, docker, and kubectl commands

Follow the AWS documentation to ensure the following on your local machine (where Dataiku DSS is installed):

  • The aws ecr command can list and create docker image repositories and authenticate docker for image push.

  • The kubectl command can create deployments and services on the cluster.

  • The docker command can successfully push images to the ECR repository.


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.

  • On EKS, the image registry URL is the one given by aws ecr describe-repositories, without the image name. It typically looks like, where XXXXXXXXXXXX is your AWS account ID, us-east-1 is the AWS region for the repository and PREFIX is an optional prefix to triage your repositories.

  • Once you have filled the registry URL, the “Image pre-push hook” field becomes visible: set it to “Enable push to ECR”.


You are now ready to deploy your API Services to EKS.

Using GPUs

AWS 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 AWS 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 EKS to configure the CUDA driver on your containers, the corresponding EKS 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