Docling Operator

The Docling Operator distributes docling-serve together with the docling-jobkit Kubeflow jobs.

Description

The Docling Operator configures the docling-serve API Deployment and related Secret, ConfigMap, Service. It also configures the docling-kfp-job Data Science Pipeline for running the distributed Docling conversion. This is launched and inspected from docling-serve using the k8s api. With docling-serve you can deploy with different compute engines. With the docling operator you can configure which compute engine to use for the deployment.

Getting Started

Prerequisites

• go version v1.24.1+
• docker version 17.03+.
• kubectl version v1.11.3+.
• Access to a Kubernetes v1.11.3+ cluster.

Kubeflow Pipeline Engine

The engine is set to local by default. To deploy a Kubeflow Pipeline engine, adjust the custom resource at config/samples/docling_v1alpha1_doclingserve.yaml and add a Kubeflow endpoint.

engine:
    kfp:
      enpoint: <kubeflow-endpoint>

To Deploy on the cluster

git clone https://github.com/docling-project/docling-operator.git

cd <project>

make generate

make manifests

Build and push your image to the location specified by IMG:

make docker-build docker-push IMG=<some-registry>/docling-operator:tag

NOTE: This image ought to be published in the personal registry you specified. And it is required to have access to pull the image from the working environment. Make sure you have the proper permission to the registry if the above commands don’t work.

Install the CRDs into the cluster:

make install

Deploy the Manager to the cluster with the image specified by IMG:

make deploy IMG=<some-registry>/docling-operator:tag

NOTE: If you encounter RBAC errors, you may need to grant yourself cluster-admin privileges or be logged in as admin.

Create instances of your solution You can apply the samples (examples) from the config/sample:

kubectl apply -k config/samples/docling_v1alpha1_doclingserve.yaml

NOTE: Ensure that the samples has default values to test it out.

To Uninstall

Delete the instances (CRs) from the cluster:

kubectl delete -k config/samples/docling_v1alpha1_doclingserve.yaml

Delete the APIs(CRDs) from the cluster:

make uninstall

UnDeploy the controller from the cluster:

make undeploy