Storage: kubeflow kubernetes pvc (persistent volumes) and mounted storage in Azure

[cpereir1]

Hi,
I have a Azure deployment of Kubeflow, in a Kubernetes cluster.
I would like to use Kedro, and kedro kubeflow to abstract from the difficulty of creating kubeflow pipelines.
I am wondering a few things:

• Hoes kedro, or kedro kubeflow, interact with, for example, a locally downloaded dataset that kubeflow mounted into a local volume?
• How can a kedro node use GPUs to run, for example, a training operation?
• How can a kedro node execute, for example, a training pipeline, that consists of but multiple nodes and dependencies within it?
• Where/how does kedro kubeflow generate the kubeflow pipeline.yaml necessary by Kubeflow to execute pipelines?

Thank you so much!

[marrrcin]

Hi @cpereir1 , sorry for a late response:

Hoes kedro, or kedro kubeflow, interact with, for example, a locally downloaded dataset that kubeflow mounted into a local volume?

You can just read it from disk. Our plugin also mounts data volume under /home/kedro/datavolume as defined here:

kedro-kubeflow/kedro_kubeflow/generators/pod_per_node_pipeline_generator.py

Line 153 in ab70986

def _setup_volumes(self, volume_name, image, image_pull_policy):

How can a kedro node use GPUs to run, for example, a training operation?

See #202 - we're going to release it soon. You can build the plugin from develop branch to use it right away.

How can a kedro node execute, for example, a training pipeline, that consists of but multiple nodes and dependencies within it?

Node dependencies from Kedro are automatically translated to dependencies between nodes in KFP.

Where/how does kedro kubeflow generate the kubeflow pipeline.yaml necessary by Kubeflow to execute pipelines?

It's generated in the directory from which you're running your kedro pipeline. You can check out kedro kubeflow compile command.