ImageSpec with GAR#

In this section we explain how to set up and use Google Artifact Registry (GAR) to build and deploy task container images using ImageSpec.

Prerequisites#

If you are using GAR in the same Google Cloud Platform (GCP) project as your Union data plane, then you do not need to configure anything. Access to GAR in the same project is enabled by default.

If you want to store your task container images in a GAR repository in a GCP project other than the one that holds your data plane, you must enable the node pool of your data plane to access that GAR. See Enable Google Artifact Registry for details.

Set up the image repository#

Unlike GitHub Container Registry, GAR does not allow you to simply push an arbitrarily named image to the registry. Instead, you must first create a repository in the GAR instance and then push the image to that repository.

Registry, repository, and image

In GAR terminology the registry is the top-level storage service. The registry holds a collection of repositories. Each repository in turn holds some number of images, and each specific image name can have different versions.

Note that this differs from the arrangement in AWS ECR where the repository name and image name are essentially the same.

When you push an image to GAR, you are actually pushing it to an image name within a repository within that registry. Strictly speaking, the term image refers to a specific image version within that repository.

This means that you have to decide on the name of your repository and create it, before registering your workflow. You can, however, decide on the image name later, when you push the image to the repository.

Adapting the example from the previous section, we will assume the following

  • The GAR instance you will be using has the base URL us-east1-docker.pkg.dev/my-union-dataplane/my-registry/.

  • Your repository will be called my-image-repository.

  • Your image will be called simple-example-image.

In the GCP console, within your Union data plane project, go to Artifact Registry. You should see a list of repositories. The existing ones are used internally by Union. For your own work you should create a new one. Click Create Repository:

![]/_static/images/gar-create-repository-1.png)

On the Create repository page,

  • Enter the name of the repository. In this example it would be my-image-repository.

  • Select Docker for the artifact type.

  • Select the region. If you want to access the GAR without further configuration, make sure this the same region as your Union data plane.

  • Click Create:

![]/_static/images/gar-create-repository-2.png)

Your GAR repository is now created.

Authenticate to the registry#

You will need to set up your local Docker client to authenticate with GAR. This is needed for unionai to be able to push the image built according to the ImageSpec to GAR.

Directions can be found in the GAR console interface. Click on Setup Instructions:

![]/_static/images/gar-setup-instructions.png)

The directions are also reproduced below. (We show the directions for the us-east1 region. You may need to adjust the command accordingly):

Setup Instructions

Follow the steps below to configure your client to push and pull packages using this repository. You can also view more detailed instructions here. For more information about working with artifacts in this repository, see the documentation.

Initialize gcloud

The Google Cloud SDK is used to generate an access token when authenticating with Artifact Registry. Make sure that it is installed and initialized with Application Default Credentials before proceeding.

Configure Docker

Run the following command to configure gcloud as the credential helper for the Artifact Registry domain associated with this repository’s location:

$ gcloud auth configure-docker us-east1-docker.pkg.dev

Register your workflow to Union#

You can now register tasks with ImageSpec declarations that reference this repository.

For example, to use the example GAR repository shown here, we would alter the Python code in the previous section, to have the following ImageSpec declaration:

image_spec = ImageSpec(

us-east1-docker.pkg.dev/dogfood-gcp-dataplane/hello/imagespec:0OiA3VrE3Z-CXrkFNZW--w..

    registry="us-east1-docker.pkg.dev/my-union-dataplane/my-registry/my-image-repository`.
    name="simple-example-image",
    base_image="ghcr.io/flyteorg/flytekit:py3.11-latest",
    requirements="image-requirements.txt"
)