Category: Autopilot

Using Node-based pricing on GKE Autopilot

New this year, Autopilot now has two pricing models: the original Pod-based model, and the new node-based option. The pricing page does a pretty good job of explaining the difference (at least I hope it explains it well, as I wrote it), and how best to utilize each option, but here’s a quick recap anyway:… Continue reading Using Node-based pricing on GKE Autopilot

Serving Stable Diffusion with RayServe on GKE Autopilot

Now that you’ve tried out Stable Diffusion on GKE Autopilot via the WebUI, you might be wondering how you’d go about adding stable diffusion as a proper micro-service that other components of your application can call. One popular way is via Ray. Let’s try this tutorial: Serve a StableDiffusion text-to-image model on Kubernetes, on GKE… Continue reading Serving Stable Diffusion with RayServe on GKE Autopilot

Stable Diffusion WebUI on GKE Autopilot

I recently set out to run Stable Diffusion on GKE in Autopilot mode, building a container from scratch using the AUTOMATIC1111‘s webui. This is likely not how you’d host a stable diffusion service for production (which would make for a good topic of another blog post), but it’s a fun way to try out the… Continue reading Stable Diffusion WebUI on GKE Autopilot

LLM Model Serving on Autopilot

When building your business using LLMs as a key component, you may wish to be a master of your own domain and run your own model. Running your own LLM protects you from changes like pricing increases or API availability with third-party services, guarantees the privacy of your data (no data needs to leave your… Continue reading LLM Model Serving on Autopilot

Finding the NVIDIA Driver Version on GKE

Update: this information is now available in the official docs. If you want to know what version of your GPU drivers are active on GKE, here’s a one-liner: What this command does is get all the logs of Pods with the label k8s-app=nvidia-gpu-device-plugin (there are several different DaemonSets that can install the drivers depending on… Continue reading Finding the NVIDIA Driver Version on GKE

BYO Service Mesh on GKE Autopilot

Did you know you can now run any service mesh on Autopilot? That’s right! Even though Autopilot is a fully managed Kubernetes platform which by nature means you don’t have full root access to the node, that doesn’t mean you’re limited in what service mesh you can install. How does it work? Service Meshes require… Continue reading BYO Service Mesh on GKE Autopilot

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Strict Pod Co-location

Pod affinity is a useful technique in Kubernetes for expressing a requirement that a pod, say with the “reader” role, is co-located with another pod, say with the “writer’ role. You can express that requirement by adding something like the following to the reader pod. The catch is, if there is no space on the… Continue reading Strict Pod Co-location

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HA 3-zone Deployments with PodSpreadTopology on Autopilot

PodSpreadTopology is a way to get Kubernetes to spread out your pods across a failure domain, typically nodes or zones. Kubernetes platforms typically have some default spread built in, although it may not be as aggressive as you want (meaning, it might be more tolerant of imbalanced spread). Here’s an example Deployment with a PodSpreadToplogy… Continue reading HA 3-zone Deployments with PodSpreadTopology on Autopilot

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TensorFlow on GKE Autopilot with GPU acceleration

Last week, GKE announced GPU support for Autopilot. Here’s a fun way to try it out: a TensorFlow-enabled Jupyter Notebook with GPU-acceleration! We can even add state, so you can save your work between sessions. Autopilot makes all this really, really easy, as you can configure everything as a Kubernetes object. Update: this post is… Continue reading TensorFlow on GKE Autopilot with GPU acceleration

Provisioning one-off spare capacity for GKE Autopilot

I previously documented how to add spare capacity to an Autopilot Kubernetes cluster, whereby you create a placeholder Deployment to provision some scheduling headroom. This works to constantly give you a certain amount of headroom, so for example if you have a 2vCPU placeholder (a.k.a. balloon) Deployment, and use that capacity it will get rescheduled.… Continue reading Provisioning one-off spare capacity for GKE Autopilot

High-Performance Compute on Autopilot

This week, Autopilot announced support for the Scale-Out Compute Class, for both x86 and Arm architectures. The point of this compute class is to give you cores for better single-threaded performance, and improved price/performance for “scale-out” workloads — basically for when you are saturating the CPU, and/or need faster single-threaded performance (e.g. remote compilation, etc).… Continue reading High-Performance Compute on Autopilot

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Arm on Autopilot

Arm was made available in Preview on Google Cloud, and GKE Autopilot today! As this is an early stage Preview, there’s a few details to pay attention to if you want to try it out, like the version, regions and quota. I put together this quickstart for trying out Arm in Autopilot today. Arm nodes… Continue reading Arm on Autopilot

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Minimizing Pod Disruption on Autopilot

There are 3 common reasons why a Pod may be terminated on Autopilot: node upgrades, a cluster scale-down, and a node repair. PDBs and graceful termination periods modify the disruption to pods when these events happen, and maintenance windows and exclusions control when upgrade events can occur. Upgrade gracefulTerminationPeriod: limited to one hourPDB: is respected… Continue reading Minimizing Pod Disruption on Autopilot

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Building GKE Autopilot

Last month gave a presentation at KubeCon Europe in Valencia on “Building a Nodeless Kubernetes Platform”. In it, I shared the details about the creation of GKE Autopilot including some key decisions that we made, how the product was implemented, and why I believe that the design leads to an ideal fully managed platform. Autopilot… Continue reading Building GKE Autopilot

Preferring Spot in GKE Autopilot

Spot Pods are a great way to save money on Autopilot, currently 70% off the regular price. The catch is two-fold: Your workload can be disrupted There may not always be spot capacity available For workload disruption, this is simply a judgement call. You should only run workloads that can accept disruption (abrupt termination). If… Continue reading Preferring Spot in GKE Autopilot

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Separating Workloads in Autopilot

Autopilot while being operationally nodeless, still creates nodes for your workloads behind the scenes. Sometimes it may be desirable as an operator to separate your workloads so that certain workloads are scheduled on their separates nodes, a technique known as workload separation. One example I heard recently was a cluster that primarily processes large batch… Continue reading Separating Workloads in Autopilot

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Kubernetes Nodes and Autopilot

One of the key design decisions of GKE Autopilot is the fact that we kept the same semantic meaning of the Kubernetes node object. It’s “nodeless” in the sense that you don’t need to care about, or plan for nodes—they are provisioned and managed automatically based on your PodSpec. However, the node object still exists… Continue reading Kubernetes Nodes and Autopilot

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Creating an Autopilot cluster at a specific version

Sometimes you may wish to create or update a GKE Autopilot cluster with a specific version. For example, the big news this week is that mutating webhooks are supported in Autopilot (from version 1.21.3-gke.900). Rather than waiting for your desired version to be the default in your cluster’s release channel, you can update ahead of… Continue reading Creating an Autopilot cluster at a specific version

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