Learning Environment

Install Seldon Core 2 in a local learning environment.

You can install Seldon Core 2 on your local computer that is running a Kubernetes cluster using kind.

Seldon publishes the Helm charts that are required to install Seldon Core 2. For more information about the Helm charts and the related dependencies,see Helm charts and Dependencies.

Note: These instructions guide you through installing Seldon Core 2 on a local Kubernetes cluster, focusing on ease of learning. Ensure your kind cluster is running on hardware with at least 32GB of RAM and a load balancer such as MetalLB is configured.

Prerequisites

• Install a Kubernetes cluster that is running version 1.27 or later.

• Install kubectl, the Kubernetes command-line tool.

• Install Helm, the package manager for Kubernetes or Ansible, the automation tool used for provisioning, configuration management, and application deployment.

Note: Ansible automates provisioning, configuration management, and handles all dependencies required for Seldon Core 2. With Helm, you need to configure and manage the dependencies yourself.

Installing Seldon Core 2

Helm

1. Create a namespace to contain the main components of Seldon Core 2. For example, create the seldon-mesh namespace.

   kubectl create ns seldon-mesh || echo "Namespace seldon-mesh already exists"

2. Add and update the Helm charts, seldon-charts, to the repository.

   helm repo add seldon-charts https://seldonio.github.io/helm-charts/
   helm repo update seldon-charts

3. Install Custom resource definitions for Seldon Core 2.

   helm upgrade seldon-core-v2-crds seldon-charts/seldon-core-v2-crds \
   --namespace default \
   --install 

4. Install Seldon Core 2 operator in the seldon-mesh namespace.

    helm upgrade seldon-core-v2-setup seldon-charts/seldon-core-v2-setup \
    --namespace seldon-mesh --set controller.clusterwide=true \
    --install

   This configuration installs the Seldon Core 2 operator across an entire Kubernetes cluster. To perform cluster-wide operations, create ClusterRoles and ensure your user has the necessary permissions during deployment. With cluster-wide operations, you can create SeldonRuntimes in any namespace.

   You can configure the installation to deploy the Seldon Core 2 operator in a specific namespace so that it control resources in the provided namespace. To do this, set controller.clusterwide to false.

5. Install Seldon Core 2 runtimes in the seldon-mesh namespace.

   helm upgrade seldon-core-v2-runtime seldon-charts/seldon-core-v2-runtime \
   --namespace seldon-mesh \
   --install

6. Install Seldon Core 2 servers in the seldon-mesh namespace. Two example servers named mlserver-0, and triton-0 are installed so that you can load the models to these servers after installation.

    helm upgrade seldon-core-v2-servers seldon-charts/seldon-core-v2-servers \
    --namespace seldon-mesh \
    --install

7. Check Seldon Core 2 operator, runtimes, servers, and CRDS are installed in the seldon-mesh namespace. It might take a couple of minutes for all the Pods to be ready. To check the status of the Pods in real time use this command: kubectl get pods -w -n seldon-mesh.

    kubectl get pods -n seldon-mesh

   The output should be similar to this:

   NAME                                            READY   STATUS             RESTARTS      AGE
   hodometer-749d7c6875-4d4vw                      1/1     Running            0             4m33s
   mlserver-0                                      3/3     Running            0             4m10s
   seldon-dataflow-engine-7b98c76d67-v2ztq         0/1     CrashLoopBackOff   5 (49s ago)   4m33s
   seldon-envoy-bb99f6c6b-4mpjd                    1/1     Running            0             4m33s
   seldon-modelgateway-5c76c7695b-bhfj5            1/1     Running            0             4m34s
   seldon-pipelinegateway-584c7d95c-bs8c9          1/1     Running            0             4m34s
   seldon-scheduler-0                              1/1     Running            0             4m34s
   seldon-v2-controller-manager-5dd676c7b7-xq5sm   1/1     Running            0             4m52s
   triton-0                                        2/3     Running            0             4m10s

Note: Pods with names starting with seldon-dataflow-engine, seldon-pipelinegateway, and seldon-modelgateway may generate log errors until they successfully connect to Kafka. This occurs because Kafka is not yet fully integrated with Seldon Core 2.

Ansible

Next Steps

If you installed Seldon Core 2 using Helm, you need to complete the installation of other components in the following order:

1. Integrating with Kafka

2. Installing a Service mesh