# Source: https://docs.zenml.io/stacks/stack-components/orchestrators/kubeflow.md

# Kubeflow Orchestrator

The Kubeflow orchestrator is an [orchestrator](https://docs.zenml.io/stacks/stack-components/orchestrators) flavor provided by the ZenML `kubeflow` integration that uses [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/overview/) to run your pipelines.

{% hint style="warning" %}
This component is only meant to be used within the context of a [remote ZenML deployment scenario](https://docs.zenml.io/getting-started/deploying-zenml/). Usage with a local ZenML deployment may lead to unexpected behavior!
{% endhint %}

### When to use it

You should use the Kubeflow orchestrator if:

* you're looking for a proven production-grade orchestrator.
* you're looking for a UI in which you can track your pipeline runs.
* you're already using Kubernetes or are not afraid of setting up and maintaining a Kubernetes cluster.
* you're willing to deploy and maintain Kubeflow Pipelines on your cluster.

### How to deploy it

To run ZenML pipelines on Kubeflow, you'll need to set up a Kubernetes cluster and deploy Kubeflow Pipelines on it. This can be done in a variety of ways, depending on whether you want to use a cloud provider or your own infrastructure:

{% tabs %}
{% tab title="AWS" %}

* Have an existing AWS [EKS cluster](https://docs.aws.amazon.com/eks/latest/userguide/create-cluster.html) set up.
* Make sure you have the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) set up.
* Download and [install](https://kubernetes.io/docs/tasks/tools/) `kubectl` and configure it to talk to your EKS cluster using the following command:

  ```powershell
  aws eks --region REGION update-kubeconfig --name CLUSTER_NAME
  ```
* [Install](https://www.kubeflow.org/docs/components/pipelines/operator-guides/installation/#deploying-kubeflow-pipelines) Kubeflow Pipelines onto your cluster.
* ( optional) [set up an AWS Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/aws-service-connector) to grant ZenML Stack Components easy and secure access to the remote EKS cluster.
  {% endtab %}

{% tab title="GCP" %}

* Have an existing GCP [GKE cluster](https://cloud.google.com/kubernetes-engine/docs/quickstart) set up.
* Make sure you have the [Google Cloud CLI](https://cloud.google.com/sdk/docs/install-sdk) set up first.
* Download and [install](https://kubernetes.io/docs/tasks/tools/) `kubectl` and [configure](https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl) it to talk to your GKE cluster using the following command:

  ```powershell
  gcloud container clusters get-credentials CLUSTER_NAME
  ```
* [Install](https://www.kubeflow.org/docs/distributions/gke/deploy/overview/) Kubeflow Pipelines onto your cluster.
* ( optional) [set up a GCP Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/gcp-service-connector) to grant ZenML Stack Components easy and secure access to the remote GKE cluster.
  {% endtab %}

{% tab title="Azure" %}

* Have an existing [AKS cluster](https://azure.microsoft.com/en-in/services/kubernetes-service/#documentation) set up.
* Make sure you have the [`az` CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli) set up first.
* Download and [install](https://kubernetes.io/docs/tasks/tools/) `kubectl` and ensure that it talks to your AKS cluster using the following command:

  ```powershell
  az aks get-credentials --resource-group RESOURCE_GROUP --name CLUSTER_NAME
  ```
* [Install](https://www.kubeflow.org/docs/components/pipelines/operator-guides/installation/#deploying-kubeflow-pipelines) Kubeflow Pipelines onto your cluster.

{% hint style="info" %}
Since Kubernetes v1.19, AKS has shifted to [`containerd`](https://docs.microsoft.com/en-us/azure/aks/cluster-configuration#container-settings). However, the workflow controller installed with the Kubeflow installation has `Docker` set as the default runtime. In order to make your pipelines work, you have to change the value to one of the options listed [here](https://argoproj.github.io/argo-workflows/workflow-executors/#workflow-executors), preferably `k8sapi`.

This change has to be made by editing the `containerRuntimeExecutor` property of the `ConfigMap` corresponding to the workflow controller. Run the following commands to first know what config map to change and then to edit it to reflect your new value:

```
kubectl get configmap -n kubeflow
kubectl edit configmap CONFIGMAP_NAME -n kubeflow
# This opens up an editor that can be used to make the change.
```

{% endhint %}
{% endtab %}

{% tab title="Other Kubernetes" %}

* Have an existing Kubernetes cluster set up.
* Download and [install](https://kubernetes.io/docs/tasks/tools/) `kubectl` and configure it to talk to your Kubernetes cluster.
* [Install](https://www.kubeflow.org/docs/components/pipelines/operator-guides/installation/#deploying-kubeflow-pipelines) Kubeflow Pipelines onto your cluster.
* ( optional) [set up a Kubernetes Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/kubernetes-service-connector) to grant ZenML Stack Components easy and secure access to the remote Kubernetes cluster. This is especially useful if your Kubernetes cluster is remotely accessible, as this enables other ZenML users to use it to run pipelines without needing to configure and set up `kubectl` on their local machines.
  {% endtab %}
  {% endtabs %}

{% hint style="info" %}
If one or more of the deployments are not in the `Running` state, try increasing the number of nodes in your cluster.
{% endhint %}

{% hint style="warning" %}
If you're installing Kubeflow Pipelines manually, make sure the Kubernetes service is called exactly `ml-pipeline`. This is a requirement for ZenML to connect to your Kubeflow Pipelines deployment.
{% endhint %}

### How to use it

To use the Kubeflow orchestrator, we need:

* A Kubernetes cluster with Kubeflow pipelines installed. See the [deployment section](#how-to-deploy-it) for more information.
* A ZenML server deployed remotely where it can be accessed from the Kubernetes cluster. See the [deployment guide](https://docs.zenml.io/getting-started/deploying-zenml/) for more information.
* The ZenML `kubeflow` integration installed. If you haven't done so, run

  ```shell
  zenml integration install kubeflow
  ```
* [Docker](https://www.docker.com) installed and running (unless you are using a remote [Image Builder](https://docs.zenml.io/stacks/image-builders/) in your ZenML stack).
* [kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl) installed (optional, see below)

{% hint style="info" %}
If you are using a single-tenant Kubeflow installed in a Kubernetes cluster managed by a cloud provider like AWS, GCP or Azure, it is recommended that you set up [a Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/service-connectors-guide) and use it to connect ZenML Stack Components to the remote Kubernetes cluster. This guarantees that your Stack is fully portable on other environments and your pipelines are fully reproducible.
{% endhint %}

* The name of your Kubernetes context which points to your remote cluster. Run `kubectl config get-contexts` to see a list of available contexts. **NOTE**: this is no longer required if you are using [a Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/service-connectors-guide) to connect your Kubeflow Orchestrator Stack Component to the remote Kubernetes cluster.
* A [remote artifact store](https://docs.zenml.io/stacks/artifact-stores/) as part of your stack.
* A [remote container registry](https://docs.zenml.io/stacks/container-registries/) as part of your stack.

We can then register the orchestrator and use it in our active stack. This can be done in two ways:

1. If you have [a Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/service-connectors-guide) configured to access the remote Kubernetes cluster, you no longer need to set the `kubernetes_context` attribute to a local `kubectl` context. In fact, you don't need the local Kubernetes CLI at all. You can [connect the stack component to the Service Connector](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/service-connectors-guide#connect-stack-components-to-resources) instead:

   ```shell
   # List all available Kubernetes clusters that can be accessed by service connectors
   zenml service-connector list-resources --resource-type kubernetes-cluster -e
   # Register the Kubeflow orchestrator and connect it to the remote Kubernetes cluster 
   zenml orchestrator register <ORCHESTRATOR_NAME> --flavor kubeflow --connector <SERVICE_CONNECTOR_NAME> --resource-id <KUBERNETES_CLUSTER_NAME>
   # Register a new stack with the orchestrator
   zenml stack register <STACK_NAME> -o <ORCHESTRATOR_NAME> -a <ARTIFACT_STORE_NAME> -c <CONTAINER_REGISTRY_NAME> ... # Add other stack components as needed
   ```

   The following example demonstrates how to register the orchestrator and connect it to a remote Kubernetes cluster using a Service Connector:

   ```shell
   $ zenml service-connector list-resources --resource-type kubernetes-cluster -e
   The following 'kubernetes-cluster' resources can be accessed by service connectors that you have configured:
   ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━┓
   ┃             CONNECTOR ID             │ CONNECTOR NAME        │ CONNECTOR TYPE │ RESOURCE TYPE         │ RESOURCE NAMES      ┃
   ┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨
   ┃ e33c9fac-5daa-48b2-87bb-0187d3782cde │ aws-iam-multi-eu      │ 🔶 aws         │ 🌀 kubernetes-cluster │ kubeflowmultitenant ┃
   ┃                                      │                       │                │                       │ zenbox              ┃
   ┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨
   ┃ ed528d5a-d6cb-4fc4-bc52-c3d2d01643e5 │ aws-iam-multi-us      │ 🔶 aws         │ 🌀 kubernetes-cluster │ zenhacks-cluster    ┃
   ┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨
   ┃ 1c54b32a-4889-4417-abbd-42d3ace3d03a │ gcp-sa-multi          │ 🔵 gcp         │ 🌀 kubernetes-cluster │ zenml-test-cluster  ┃
   ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━┛

   $ zenml orchestrator register aws-kubeflow --flavor kubeflow --connector aws-iam-multi-eu --resource-id zenhacks-cluster
   Successfully registered orchestrator `aws-kubeflow`.
   Successfully connected orchestrator `aws-kubeflow` to the following resources:
   ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┓
   ┃             CONNECTOR ID             │ CONNECTOR NAME   │ CONNECTOR TYPE │ RESOURCE TYPE         │ RESOURCE NAMES   ┃
   ┠──────────────────────────────────────┼──────────────────┼────────────────┼───────────────────────┼──────────────────┨
   ┃ ed528d5a-d6cb-4fc4-bc52-c3d2d01643e5 │ aws-iam-multi-us │ 🔶 aws         │ 🌀 kubernetes-cluster │ zenhacks-cluster ┃
   ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┛

   # Create a new stack with the orchestrator
   $ zenml stack register --set aws-kubeflow -o aws-kubeflow -a aws-s3 -c aws-ecr
   Stack 'aws-kubeflow' successfully registered!
           Stack Configuration           
   ┏━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━┓
   ┃ COMPONENT_TYPE     │ COMPONENT_NAME  ┃
   ┠────────────────────┼─────────────────┨
   ┃ ARTIFACT_STORE     │ aws-s3          ┃
   ┠────────────────────┼─────────────────┨
   ┃ ORCHESTRATOR       │ aws-kubeflow    ┃
   ┠────────────────────┼─────────────────┨
   ┃ CONTAINER_REGISTRY │ aws-ecr         ┃
   ┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━┛
           'aws-kubeflow' stack         
   No labels are set for this stack.
   Stack 'aws-kubeflow' with id 'dab28f94-36ab-467a-863e-8718bbc1f060' is owned by user user.
   Active global stack set to:'aws-kubeflow'
   ```
2. if you don't have a Service Connector on hand and you don't want to [register one](https://docs.zenml.io/how-to/infrastructure-deployment/auth-management/service-connectors-guide#register-service-connectors), the local Kubernetes `kubectl` client needs to be configured with a configuration context pointing to the remote cluster. The `kubernetes_context` must also be configured with the value of that context:

   ```shell
   zenml orchestrator register <ORCHESTRATOR_NAME> --flavor=kubeflow --kubernetes_context=<KUBERNETES_CONTEXT>

   # Register a new stack with the orchestrator
   zenml stack register <STACK_NAME> -o <ORCHESTRATOR_NAME> -a <ARTIFACT_STORE_NAME> -c <CONTAINER_REGISTRY_NAME> ... # Add other stack components as needed
   ```

{% hint style="info" %}
ZenML will build a Docker image called `<CONTAINER_REGISTRY_URI>/zenml:<PIPELINE_NAME>` which includes all required software dependencies and use it to run your pipeline steps in Kubeflow. Check out [this page](https://docs.zenml.io/how-to/customize-docker-builds/) if you want to learn more about how ZenML builds these images and how you can customize them.
{% endhint %}

You can now run any ZenML pipeline using the Kubeflow orchestrator:

```shell
python file_that_runs_a_zenml_pipeline.py
```

#### Kubeflow UI

Kubeflow comes with its own UI that you can use to find further details about your pipeline runs, such as the logs of your steps. For any runs executed on Kubeflow, you can get the URL to the Kubeflow UI in Python using the following code snippet:

```python
from zenml.client import Client

pipeline_run = Client().get_pipeline_run("<PIPELINE_RUN_NAME>")
orchestrator_url = pipeline_run.run_metadata["orchestrator_url"]
```

#### Additional configuration

For additional configuration of the Kubeflow orchestrator, you can pass `KubeflowOrchestratorSettings` which allows you to configure (among others) the following attributes:

* `client_args`: Arguments to pass when initializing the KFP client.
* `user_namespace`: The user namespace to use when creating experiments and runs.
* `pod_settings`: Node selectors, affinity, and tolerations to apply to the Kubernetes Pods running your pipeline. These can be either specified using the Kubernetes model objects or as dictionaries.

```python
from zenml.integrations.kubeflow.flavors.kubeflow_orchestrator_flavor import KubeflowOrchestratorSettings
from kubernetes.client.models import V1Toleration

kubeflow_settings = KubeflowOrchestratorSettings(
    client_args={},
    user_namespace="my_namespace",
    pod_settings={
        "affinity": {
            "nodeAffinity": {
                "requiredDuringSchedulingIgnoredDuringExecution": {
                    "nodeSelectorTerms": [
                        {
                            "matchExpressions": [
                                {
                                    "key": "node.kubernetes.io/name",
                                    "operator": "In",
                                    "values": ["my_powerful_node_group"],
                                }
                            ]
                        }
                    ]
                }
            }
        },
        "tolerations": [
            V1Toleration(
                key="node.kubernetes.io/name",
                operator="Equal",
                value="",
                effect="NoSchedule"
            )
        ]
    }
)


@pipeline(
    settings={
        "orchestrator": kubeflow_settings
    }
)


...
```

Check out the [SDK docs](https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-kubeflow.html#zenml.integrations.kubeflow) for a full list of available attributes and [this docs page](https://docs.zenml.io/concepts/steps_and_pipelines/configuration) for more information on how to specify settings.

#### Enabling CUDA for GPU-backed hardware

Note that if you wish to use this orchestrator to run steps on a GPU, you will need to follow [the instructions on this page](https://docs.zenml.io/user-guides/tutorial/distributed-training/) to ensure that it works. It requires adding some extra settings customization and is essential to enable CUDA for the GPU to give its full acceleration.

### Important Note for Multi-Tenancy Deployments

Kubeflow has a notion of [multi-tenancy](https://www.kubeflow.org/docs/components/multi-tenancy/overview/) built into its deployment. Kubeflow's multi-user isolation simplifies user operations because each user only views and edited the Kubeflow components and model artifacts defined in their configuration.

Using the ZenML Kubeflow orchestrator on a multi-tenant deployment without any settings will result in the following error:

```shell
HTTP response body: {"error":"Invalid input error: Invalid resource references for experiment. ListExperiment requires filtering by namespace.","code":3,"message":"Invalid input error: Invalid resource references for experiment. ListExperiment requires filtering by 
namespace.","details":[{"@type":"type.googleapis.com/api.Error","error_message":"Invalid resource references for experiment. ListExperiment requires filtering by namespace.","error_details":"Invalid input error: Invalid resource references for experiment. ListExperiment requires filtering by namespace."}]}
```

In order to get it to work, we need to leverage the `KubeflowOrchestratorSettings` referenced above. By setting the namespace option, and by passing in the right authentication credentials to the Kubeflow Pipelines Client, we can make it work.

First, when registering your Kubeflow orchestrator, please make sure to include the `kubeflow_hostname` parameter. The `kubeflow_hostname` **must end with the `/pipeline` post-fix**.

```shell
zenml orchestrator register <NAME> \
    --flavor=kubeflow \
    --kubeflow_hostname=<KUBEFLOW_HOSTNAME> # e.g. https://mykubeflow.example.com/pipeline
```

Then, ensure that you use the pass the right settings before triggering a pipeline run. The following snippet will prove useful:

```python
import requests

from zenml.client import Client
from zenml.integrations.kubeflow.flavors.kubeflow_orchestrator_flavor import (
    KubeflowOrchestratorSettings,
)

NAMESPACE = "namespace_name"  # This is the user namespace for the profile you want to use
USERNAME = "admin"  # This is the username for the profile you want to use
PASSWORD = "abc123"  # This is the password for the profile you want to use

# Use client_username and client_password and ZenML will automatically fetch a session cookie
kubeflow_settings = KubeflowOrchestratorSettings(
    client_username=USERNAME,
    client_password=PASSWORD,
    user_namespace=NAMESPACE
)


# You can also pass the cookie in `client_args` directly
# kubeflow_settings = KubeflowOrchestratorSettings(
#     client_args={"cookies": session_cookie}, user_namespace=NAMESPACE
# )

@pipeline(
    settings={
        "orchestrator": kubeflow_settings
    }
)

:
...

if "__name__" == "__main__":
# Run the pipeline
```

Note that the above is also currently not tested on all Kubeflow versions, so there might be further bugs with older Kubeflow versions. In this case, please reach out to us on [Slack](https://zenml.io/slack).

#### Using secrets in settings

The above example encoded the username and password in plain text as settings. You can also set them as secrets.

```shell
zenml secret create kubeflow_secret \
    --username=admin \
    --password=abc123
```

And then you can use them in code:

```python
# Use client_username and client_password and ZenML will automatically fetch a session cookie
kubeflow_settings = KubeflowOrchestratorSettings(
    client_username="{{kubeflow_secret.username}}",  # secret reference
    client_password="{{kubeflow_secret.password}}",  # secret reference
    user_namespace="namespace_name"
)
```

See full documentation of using ZenML secrets [here](https://docs.zenml.io/how-to/project-setup-and-management/interact-with-secrets).

For more information and a full list of configurable attributes of the Kubeflow orchestrator, check out the [SDK Docs](https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-kubeflow.html#zenml.integrations.kubeflow) .

<figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>