Kubeflow with GPU support on WSL2

Prerequisites

kustomize

curl -s "https://raw.githubusercontent.com/kubernetes-sigs/kustomize/master/hack/install_kustomize.sh"  | bash
sudo mv kustomize /usr/local/bin

minikube

curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
sudo install minikube-linux-amd64 /usr/local/bin/minikube && rm minikube-linux-amd64

GPU support

Follow the installation instructions here: https://minikube.sigs.k8s.io/docs/tutorials/nvidia/

Start minikube

minikube start --driver docker --container-runtime docker --gpus all --memory 8192 --cpus 4 --disk-size 40g
minikube addons enable metrics-server
minikube stop
minikube start

Update nvidia-device-plugin installation

kubectl delete -n kube-system daemonsets.apps nvidia-device-plugin-daemonset
kubectl create -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.15.0-rc.2/nvidia-device-plugin.yml

Verify the installation

kubectl apply -f samples/cuda-vectoradd.yaml

Output should look similar to the following:

[Vector addition of 50000 elements]
Copy input data from the host memory to the CUDA device
CUDA kernel launch with 196 blocks of 256 threads
Copy output data from the CUDA device to the host memory
Test PASSED
Done

Install Kubeflow

Clone repository

git clone --depth 1 --branch v1.8.0 https://github.com/kubeflow/manifests.git

Install kubeflow

cd manifests
while ! kustomize build example | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 10; done

Wait until all pods are ready. This takes a few minutes.

Connect to the Kubeflow Cluster:

kubectl port-forward svc/istio-ingressgateway -n istio-system 8080:80

Email Address: user@example.com
Password: 12341234

Create a notebook with GPU support

Notebooks -> New Notebook
Name: gpu-test GPUs: 1 - NVIDIA

Wait until the notebook is created and Connect to it.
Open a new terminal and check if the GPU is recognized correctly by running:

nvidia-smi

Build a Pipeline

Apply policies to create a pipeline with the kfp client:

kubectl apply -f samples/policies.yaml 

Create a new Jupyter Notebook and run the following code to create a pipeline which outputs "Hello, World!":

import kfp
from kfp import compiler, dsl

@dsl.component(base_image='python:3.11')
def say_hello(name: str) -> str:
    hello_text = f'Hello, {name}!'
    print(hello_text)
    return hello_text

@dsl.pipeline
def hello_pipeline(recipient: str) -> str:
    hello_task = say_hello(name=recipient)
    return hello_task.output

compiler.Compiler().compile(hello_pipeline, 'pipeline.yaml')

client = kfp.Client()
run = client.create_run_from_pipeline_package(
    'pipeline.yaml',
    arguments={
        'recipient': 'World',
    },
)