Hi Guys, i am new to pulumi and trying to deploy kubeflow on gcp. I am using pulumi python and GCP...and deployed pulumi kuberntes-gcp-python and now I would like to deploy kubeflow but I am stuck. Any help ? I have tried to run the following code but no success:

# new kubeflow
kubeflow = gcp.container.Registry("kubeflow")

deployment = Deployment(
    "kubeflow-deployment",
    spec=DeploymentSpecArgs(
        replicas=1,
        selector=LabelSelectorArgs(
            match_labels={
                "app": "kubeflow",
            },
        ),
        template=PodTemplateSpecArgs(
            metadata=ObjectMetaArgs(
                labels={
                    "app": "kubeflow",
                },
            ),
            spec=PodSpecArgs(
                containers=[
                    ContainerArgs(
                        name="kubeflow",
                        image="kubeflow",
                        env=[
                            EnvVarArgs(
                                name="NAMESPACE",
                                value="kubeflow",
                            ),
                        ],
                        command=["/bin/bash"],
                        args=[
                            "-c",
                            "/opt/deploy.sh",
                        ]
                        
                    )
                ]

            )
        )
    ),
    metadata=ObjectMetaArgs(
        labels={
            "app": "kubeflow",
        }
    )
)

pulumi.export("name", deployment.metadata["name"])

# Allocate an IP to the Deployment.
app_name = "kubeflow"
app_labels = { "app": app_name }
frontend = Service(
    app_name,
    metadata={
        "labels": deployment.spec["template"]["metadata"]["labels"],
    },
    spec={
        "type":  "LoadBalancer",
        "ports": [{ "port": 80, "target_port": 80, "protocol": "TCP" }],
        "selector": app_labels,
    })

# When "done", this will print the public IP.
result = None

ingress = frontend.status.apply(lambda v: v["load_balancer"]["ingress"][0] if "load_balancer" in v else None)
if ingress is not None:
    result = ingress.apply(lambda v: v["ip"] if "ip" in v else v["hostname"])

pulumi.export("ip", result)

I am getting the following error: * the Kubernetes API server reported that "default/kubeflow-deployment-d5cb3c03" failed to fully initialize or become live: 'kubeflow-deployment-d5cb3c03' timed out waiting to be Ready * [MinimumReplicasUnavailable] Deployment does not have minimum availability. * [ProgressDeadlineExceeded] ReplicaSet "kubeflow-deployment-d5cb3c03-769cdfbd67" has timed out progressing. * Minimum number of live Pods was not attained * [Pod kubeflow-deployment-d5cb3c03-769cdfbd67-4lsjp]: containers with unready status: [kubeflow] -- [ImagePullBackOff] Back-off pulling image "kubeflow"

I've had this when the image registry wasn't accessible (i.e., K8s needed a NAT to reach it).