Menggunakan file YAML Google Kubernetes Engine yang telah dikonfigurasi sebelumnya untuk mengoptimalkan performa Cloud Storage FUSE
Halaman ini menyediakan file YAML Google Kubernetes Engine yang telah dikonfigurasi sebelumnya untuk membantu Anda mengoptimalkan performa Cloud Storage FUSE untuk workload tertentu. Baik Anda menggunakan GPU Cloud atau TPU Cloud untuk pelatihan, penayangan, atau pembuatan titik pemeriksaan, Anda dapat menggunakan konfigurasi contoh yang disediakan di halaman ini untuk men-deploy pod GKE dengan cepat dan mengakses bucket Cloud Storage Anda.
Mengonfigurasi dan men-deploy workload GKE dengan Cloud Storage FUSE menggunakan file YAML contoh
Untuk menggunakan konfigurasi sampel, lakukan langkah-langkah berikut:
Verifikasi bahwa driver CSI Cloud Storage FUSE untuk Google Kubernetes Engine berjalan di cluster GKE versi 1.32.2-gke.1297001 atau yang lebih baru.
Pastikan akun layanan Google Kubernetes Engine memiliki izin yang diperlukan untuk mengakses bucket Cloud Storage target.
Temukan konfigurasi sampel yang ingin Anda terapkan berdasarkan jenis dan workload mesin Anda menggunakan salah satu file YAML berikut:
GPU
Gunakan salah satu file YAML khusus Cloud GPU berikut berdasarkan jenis workload Anda:
Deploy PersistentVolume dan PersistentVolumeClaim dengan menerapkan file PersistentVolume.
Webhook penerimaan pod GKE
memeriksa atribut PersistentVolume untuk menerapkan potensi pengoptimalan
seperti injeksi container pendamping sebelum pod dijadwalkan.
kubectl apply -f PERSISTENT_VOLUME_YAML_FILE_NAME
Dengan:
PERSISTENT_VOLUME_YAML_FILE_NAME adalah nama file YAML PersistentVolume. Contoh, serving-pv.yaml.
Deploy spesifikasi pod yang mereferensikan PersistentVolumeClaim:
kubectl apply -f POD_YAML_FILE_NAME
Dengan:
POD_YAML_FILE_NAME adalah nama file konfigurasi YAML pod. Contoh, serving-pod.yaml.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Informasi yang saya butuhkan tidak ada","missingTheInformationINeed","thumb-down"],["Terlalu rumit/langkahnya terlalu banyak","tooComplicatedTooManySteps","thumb-down"],["Sudah usang","outOfDate","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Masalah kode / contoh","samplesCodeIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-25 UTC."],[],[],null,["# Use pre-configured Google Kubernetes Engine YAML files to optimize Cloud Storage FUSE performance\n\nThis page provides pre-configured Google Kubernetes Engine YAML files to help you optimize\nCloud Storage FUSE performance for your specific workload. Whether you're using\nCloud GPUs or Cloud TPU for training, serving, or checkpointing, you can\nuse the sample configurations provided in this page to quickly deploy your\nGKE pods and access your Cloud Storage bucket.\n| **Note:** This page provides instructions for tuning Cloud Storage FUSE performance in Google Kubernetes Engine-specific pre-configured YAML files. To learn about tuning Cloud Storage FUSE using the configuration file and CLI options, see [Cloud Storage FUSE performance tuning best practices](/storage/docs/cloud-storage-fuse/performance).\n\nConfigure and deploy GKE workloads with Cloud Storage FUSE using sample YAML files\n----------------------------------------------------------------------------------\n\nTo utilize sample configurations, perform the following steps:\n\n1. Verify that the Cloud Storage FUSE CSI driver for Google Kubernetes Engine is running on\n GKE clusters of GKE versions 1.32.2-gke.1297001\n or later.\n\n2. Verify that the Google Kubernetes Engine service account possesses the necessary\n permissions to access the target Cloud Storage bucket.\n\n3. Locate the sample configurations you want to apply based on your machine\n type and workload by using one of the following YAML files:\n\n ### GPU\n\n Use one of the following Cloud GPUs-specific YAML files based on your\n workload type:\n - [Training](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/gpu/training-pv.yaml)\n\n - [Serving](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/gpu/serving-pv.yaml)\n\n - [Checkpointing and JIT cache](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/gpu/checkpointing-pv.yaml)\n\n ### TPU\n\n Use one of the following Cloud TPU-specific YAML files based on your\n workload type:\n - [Training](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/tpu/training-pv.yaml)\n\n - [Serving](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/tpu/serving-pv.yaml)\n\n - [Checkpointing and JIT cache](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/tpu/checkpointing-pv.yaml)\n\n4. Deploy the corresponding pod specification that accesses the\n PersistentVolumeClaim using one the following YAML files:\n\n ### GPU\n\n Deploy the corresponding Cloud GPUs-specific pod specification that\n accesses the PersistentVolumeClaim based on your workload type:\n - [Training](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/gpu/training-pod.yaml)\n\n - [Serving](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/gpu/serving-pod.yaml)\n\n - [Checkpointing and JIT cache](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/gpu/checkpointing-pod.yaml)\n\n ### TPU\n\n Deploy the corresponding Cloud TPU-specific pod specification that\n accesses the PersistentVolumeClaim based on your workload type:\n - [Training](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/tpu/training-pod.yaml)\n\n - [Serving](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/tpu/serving-pod.yaml)\n\n - [Checkpointing and JIT cache](https://github.com/GoogleCloudPlatform/gcsfuse/blob/master/samples/gke-csi-yaml/tpu/checkpointing-pod.yaml)\n\n5. Deploy the PersistentVolume and PersistentVolumeClaim by applying the\n PersistentVolume file.\n\n The GKE pod admission webhook\n inspects the PersistentVolume's attributes to apply potential optimizations\n such as the injection of sidecar containers before the pod is scheduled. \n\n ```\n kubectl apply -f PERSISTENT_VOLUME_YAML_FILE_NAME\n ```\n\n Where:\n - \u003cvar translate=\"no\"\u003ePERSISTENT_VOLUME_YAML_FILE_NAME\u003c/var\u003e is the name of the PersistentVolume YAML filename. For example, `serving-pv.yaml`.\n6. Deploy the pod specification that references the PersistentVolumeClaim:\n\n ```\n kubectl apply -f POD_YAML_FILE_NAME\n ```\n\n Where:\n - \u003cvar translate=\"no\"\u003ePOD_YAML_FILE_NAME\u003c/var\u003e is the name of the pod YAML configuration file. For example, `serving-pod.yaml`.\n\nWhat's next\n-----------\n\n- [Learn how to monitor Cloud Storage FUSE performance using metrics](/storage/docs/cloud-storage-fuse/metrics)."]]
