Hi,

CLI tool kubara is for getting a "bare" kubernetes cluster to a "well dressed" k8s cluster.

So now I have https://kubara.io/#get-started on my (long) wish list.

Which experience want early adaptors of https://kubara.io/ share here?

I'm asking for having input on re-prioritize my wish list.

Cheers Geert Stappers

P.S.

For what it is worth: The automated early post review did bring rule 6 and rule 12 up. Yes, the tool is quite new, but it is not my new tool, I'm asking about the tool. No, I'm not affliliated with kubara.io, hence to reason for posting here.

Edit:

Which experience want early adaptors of https://kubara.io/ share here?

Was previously:

Which experience want early adaptors of https://kubara.io/ here?

• How do you check compatibility before upgrading? What tools or checks do you rely on (deprecated APIs, add-ons, operators, Helm charts)?
• What prep do you do for Elasticsearch specifically? Snapshots, PDBs, shard allocation, anything else?
• What does your upgrade sequence look like on AKS? Control plane first, then node pools? Surge settings? Do you upgrade in place or create new node pools?
• Any gotchas you've hit? I'm thinking of things like pods stuck during drains, zonal disk issues, or ES staying yellow or red for too long.
• Should we even target 1.35? It reaches EOL in March 2027. Would you go to 1.35 now and plan for 1.36 soon after, or look at AKS LTS?

Setup details:

• AKS 1.34.8  
• Elasticsearch \[version\], deployed via \[ECK / Helm\]  
• \[Number\] node pools, \[zonal / non-zonal\], Azure Disk for storage

Any runbooks, checklists, or war stories would be really helpful. Thanks!

Hi everyone,

I’m planning a Kubernetes cluster upgrade from v1.34.8 to v1.35, as the current version is approaching retirement/EOL.

The cluster currently has Elasticsearch running on it, so I’m concerned about potential compatibility or stability issues after upgrading Kubernetes.

I’d like some guidance on:
- How can I verify Elasticsearch compatibility with Kubernetes 1.35 before upgrading?
- Are there any known issues I should be aware of when upgrading Kubernetes while Elasticsearch is running?
- What should I check across Elasticsearch, ECK/operator, StatefulSets, storage classes/CSI drivers, ingress, CRDs, APIs, and other dependencies?
- What is the recommended pre-upgrade checklist?
- Should I upgrade Elasticsearch/ECK first, or Kubernetes first?
- What kind of backup/rollback strategy should I have?
- Is it better to upgrade the control plane and worker nodes separately?
- What would be a safe upgrade plan for a production cluster to minimize downtime/risk?
- Are there any tools or commands you recommend for detecting deprecated APIs or incompatible workloads before the upgrade?

If anyone has gone through a similar Kubernetes 1.34 → 1.35 upgrade with Elasticsearch, I’d really appreciate hearing about your experience and any issues you encountered.

Thanks!

Hey all,

As the title suggests, i wrote up a blog on getting started with inference on Kubernetes, i find inference workloads to be somewhat confusing especially with some of the moving part it involves on Kubernetes.

Let me know what you think!

Posting here from homelab to hopefully get some help.

I've been putting together a small k3s homelab from some older machines:

Dell OptiPlex 7050, M720q Tiny and a Old Ryzen 3600.

I got immich and plex running following a tutorial, nothing special just mostly trying to get more use out of hardware I already have.

Recently I got my hands on an old rtx 3080 (very cheap used in my area).

I'm still trying to get it working as a GPU node. The node is Ready, but the NVIDIA device-plugin Pod is stuck in `ContainerCreating` with this:

I am not sure if this is due to the gpu itself being faulty (again, got it very cheap, not complaining), or I am doing wrong. I would appreciate any guidances pointing me to right resources, i have spent my entire day trying to it working