Dokumen ini menunjukkan cara memilih kelas komputasi tertentu untuk menjalankan workload yang memiliki persyaratan hardware unik di cluster Autopilot Google Kubernetes Engine (GKE). Sebelum membaca dokumen ini, pastikan Anda memahami konsep kelas komputasi di GKE Autopilot.
Ringkasan kelas komputasi Autopilot
Autopilot menawarkan kelas komputasi yang dirancang untuk menjalankan
workload yang memiliki persyaratan hardware tertentu. Kelas komputasi ini berguna untuk workload seperti tugas machine learning dan AI, atau menjalankan database traffic tinggi secara real-time.
Kelas komputasi ini adalah subset dari rangkaian mesin Compute Engine, dan menawarkan fleksibilitas di luar kelas komputasi tujuan umum default untuk Autopilot.
Misalnya, kelas Scale-Out menonaktifkan multi-threading secara bersamaan sehingga setiap vCPU adalah satu inti fisik.
Berdasarkan kebutuhan Pod masing-masing, Anda dapat mengonfigurasi Pod Autopilot reguler atau Pod Spot untuk meminta node yang didukung oleh kelas komputasi ini. Anda juga dapat meminta arsitektur CPU tertentu, seperti
Arm, di kelas komputasi yang
mendukung arsitektur tersebut.
Sebelum memulai
Sebelum memulai, pastikan Anda telah melakukan tugas berikut:
Jika ingin menggunakan Google Cloud CLI untuk tugas ini,
instal lalu
lakukan inisialisasi
gcloud CLI. Jika sebelumnya Anda telah menginstal gcloud CLI, dapatkan versi terbaru dengan menjalankan gcloud components update.
Untuk memberi tahu Autopilot agar menempatkan Pod Anda di class komputasi tertentu,
tentukan label cloud.google.com/compute-class di
nodeSelector
atau aturan afinitas node,
seperti dalam contoh berikut:
Anda juga dapat meminta kelas komputasi tertentu untuk Pod Spot.
Menentukan permintaan resource
Saat memilih kelas komputasi, pastikan Anda menentukan permintaan resource
untuk Pod berdasarkan
Permintaan resource minimum dan maksimum
untuk kelas yang dipilih. Jika permintaan Anda kurang dari jumlah minimum, Autopilot akan melakukan penskalaan otomatis pada permintaan Anda. Namun, jika permintaan
Anda lebih besar dari maksimum, Autopilot tidak akan men-deploy
Pod Anda dan akan menampilkan pesan error.
Memilih arsitektur CPU
Beberapa kelas komputasi mendukung beberapa arsitektur CPU. Misalnya, kelas
Scale-Out mendukung arsitektur Arm dan x86. Jika Anda
tidak meminta arsitektur tertentu, Autopilot akan menyediakan node yang
memiliki arsitektur default dari kelas komputasi yang ditentukan. Jika Pod Anda perlu
menggunakan arsitektur yang berbeda, minta arsitektur tersebut di pemilih node
atau aturan afinitas node, bersama permintaan kelas komputasi Anda. Kelas komputasi
yang Anda minta harus mendukung arsitektur CPU yang Anda tentukan.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-04 UTC."],[],[],null,["# Choose compute classes for Autopilot Pods\n\n[Autopilot](/kubernetes-engine/docs/concepts/autopilot-overview)\n\n*** ** * ** ***\n\nThis document shows you how to select specific compute classes to run workloads that\nhave unique hardware requirements in your Google Kubernetes Engine (GKE)\nAutopilot clusters. Before reading this document,\nensure that you're familiar with the concept\nof [compute classes in GKE Autopilot](/kubernetes-engine/docs/concepts/autopilot-compute-classes).\n\nOverview of Autopilot compute classes\n-------------------------------------\n\nAutopilot offers *compute classes* that are designed to run\nworkloads that have specific hardware requirements. These compute classes are\nuseful for workloads such as machine learning and AI tasks, or running real-time\nhigh traffic databases.\n\nThese compute classes are a subset of the Compute Engine\n[machine series](/compute/docs/machine-types#machine_type_comparison), and offer\nflexibility beyond the default Autopilot general-purpose compute class.\nFor example, the `Scale-Out` class turns off simultaneous multi-threading so that each\nvCPU is one physical core.\n\nBased on your individual Pod needs, you can configure your regular\nAutopilot Pods or your Spot Pods to request nodes backed by\nthese compute classes. You can also request specific CPU architecture, such as\n[Arm](https://www.arm.com/architecture), in compute classes that\nsupport that architecture.\n\nBefore you begin\n----------------\n\nBefore you start, make sure that you have performed the following tasks:\n\n- Enable the Google Kubernetes Engine API.\n[Enable Google Kubernetes Engine API](https://console.cloud.google.com/flows/enableapi?apiid=container.googleapis.com)\n- If you want to use the Google Cloud CLI for this task, [install](/sdk/docs/install) and then [initialize](/sdk/docs/initializing) the gcloud CLI. If you previously installed the gcloud CLI, get the latest version by running `gcloud components update`. **Note:** For existing gcloud CLI installations, make sure to set the `compute/region` [property](/sdk/docs/properties#setting_properties). If you use primarily zonal clusters, set the `compute/zone` instead. By setting a default location, you can avoid errors in the gcloud CLI like the following: `One of [--zone, --region] must be supplied: Please specify location`. You might need to specify the location in certain commands if the location of your cluster differs from the default that you set.\n\n\u003c!-- --\u003e\n\n- [Ensure that you have a GKE Autopilot cluster](/kubernetes-engine/docs/how-to/creating-an-autopilot-cluster) running GKE version 1.24.1-gke.1400 or later.\n\nRequest a compute class in your Autopilot Pod\n---------------------------------------------\n\nTo tell Autopilot to place your Pods on a specific compute class, specify the `cloud.google.com/compute-class` label in a [nodeSelector](https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#nodeselector) or a [node affinity rule](https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#node-affinity), such as in the following examples:\n\n### nodeSelector\n\n```yaml\n apiVersion: apps/v1\n kind: Deployment\n metadata:\n name: hello-app\n spec:\n replicas: 3\n selector:\n matchLabels:\n app: hello-app\n template:\n metadata:\n labels:\n app: hello-app\n spec:\n nodeSelector:\n cloud.google.com/compute-class: \"\u003cvar translate=\"no\"\u003eCOMPUTE_CLASS\u003c/var\u003e\"\n containers:\n - name: hello-app\n image: us-docker.pkg.dev/google-samples/containers/gke/hello-app:1.0\n resources:\n requests:\n cpu: \"2000m\"\n memory: \"2Gi\"\n \n```\n\nReplace \u003cvar translate=\"no\"\u003eCOMPUTE_CLASS\u003c/var\u003e with the name of the [compute class](/kubernetes-engine/docs/concepts/autopilot-compute-classes#when-to-use)\nbased on your use case, such as `Scale-Out`. If you select `Accelerator`,\nyou must also specify a compatible GPU. For instructions, see [Deploy GPU workloads in Autopilot](/kubernetes-engine/docs/how-to/autopilot-gpus). If\nyou select `Performance`, you can optionally select a Compute Engine machine\nseries in the node selector. If you don't specify a machine series, GKE uses the\nC4 machine series depending on [regional\navailability](/compute/docs/regions-zones#available). For instructions, see [Run CPU-intensive workloads with optimal\nperformance](/kubernetes-engine/docs/how-to/performance-pods).\n\n### nodeAffinity\n\n```yaml\n apiVersion: apps/v1\n kind: Deployment\n metadata:\n name: hello-app\n spec:\n replicas: 3\n selector:\n matchLabels:\n app: hello-app\n template:\n metadata:\n labels:\n app: hello-app\n spec:\n terminationGracePeriodSeconds: 25\n containers:\n - name: hello-app\n image: us-docker.pkg.dev/google-samples/containers/gke/hello-app:1.0\n resources:\n requests:\n cpu: \"2000m\"\n memory: \"2Gi\"\n ephemeral-storage: \"1Gi\"\n affinity:\n nodeAffinity:\n requiredDuringSchedulingIgnoredDuringExecution:\n nodeSelectorTerms:\n - matchExpressions:\n - key: cloud.google.com/compute-class\n operator: In\n values:\n - \"\u003cvar translate=\"no\"\u003eCOMPUTE_CLASS\u003c/var\u003e\"\n \n```\n\nReplace \u003cvar translate=\"no\"\u003eCOMPUTE_CLASS\u003c/var\u003e with the name of the\n[compute\nclass](/kubernetes-engine/docs/concepts/autopilot-compute-classes#when-to-use) based on your use case, such as `Scale-Out`. If you select\n`Accelerator`, you must also specify a compatible GPU. For instructions, see [Deploy GPU workloads in Autopilot](/kubernetes-engine/docs/how-to/autopilot-gpus).\nIf you select `Performance`, you can optionally select a\nCompute Engine machine series in the node selector. If you don't specify a machine series, GKE uses the C4\nmachine series depending on [regional\navailability](/compute/docs/regions-zones#available). For instructions, see [Run CPU-intensive workloads with\noptimal performance](/kubernetes-engine/docs/how-to/performance-pods).\n\nYou can also request specific compute classes for your Spot Pods.\n\n### Specify resource requests\n\nWhen you choose a compute class, make sure that you specify resource requests\nfor your Pods based on the\n[Minimum and maximum resource requests](/kubernetes-engine/docs/concepts/autopilot-resource-requests#min-max-requests)\nfor your selected class. If your requests are less than the minimum,\nAutopilot automatically scales your requests up. However, if your\nrequests are greater than the maximum, Autopilot does not deploy your\nPods and displays an error message.\n\nChoose a CPU architecture\n-------------------------\n\nSome compute classes support multiple CPU architectures. For example, the\n`Scale-Out` class supports both Arm and x86 architectures. If you\ndon't request a specific architecture, Autopilot provisions nodes that\nhave the default architecture of the specified compute class. If your Pods need\nto use a different architecture, request that architecture in your node selector\nor node affinity rule, alongside your compute class request. The compute class\nthat you request must support the CPU architecture you specify.\n\nFor instructions, refer to\n[Deploy Autopilot Pods on Arm architecture](/kubernetes-engine/docs/how-to/autopilot-arm-workloads).\n\nWhat's next\n-----------\n\n- [Learn more about Autopilot cluster architecture](/kubernetes-engine/docs/concepts/autopilot-architecture).\n- [Learn about the lifecycle of Pods](https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/).\n- [Learn about the available Autopilot compute classes](/kubernetes-engine/docs/concepts/autopilot-compute-classes).\n- [Read about the default, minimum, and maximum resource requests for each\n platform](/kubernetes-engine/docs/concepts/autopilot-resource-requests)."]]