사전 구성된 Google Kubernetes Engine YAML 파일을 사용하여 Cloud Storage FUSE 성능 최적화
이 페이지에서는 특정 워크로드에 맞게 Cloud Storage FUSE 성능을 최적화하는 데 도움이 되는 사전 구성된 Google Kubernetes Engine YAML 파일을 제공합니다. 학습, 제공, 체크포인트에 Cloud GPU 또는 Cloud TPU를 사용하는 경우 이 페이지에 제공된 샘플 구성을 사용하여 GKE 포드를 빠르게 배포하고 Cloud Storage 버킷에 액세스할 수 있습니다.
샘플 YAML 파일을 사용하여 Cloud Storage FUSE로 GKE 워크로드 구성 및 배포
샘플 구성을 활용하려면 다음 단계를 수행하세요.
Google Kubernetes Engine용 Cloud Storage FUSE CSI 드라이버가 GKE 버전 1.32.2-gke.1297001 이상의 GKE 클러스터에서 실행되는지 확인합니다.
Google Kubernetes Engine 서비스 계정에 대상 Cloud Storage 버킷에 액세스하는 데 필요한 권한이 있는지 확인합니다.
다음 YAML 파일 중 하나를 사용하여 머신 유형과 워크로드에 따라 적용할 샘플 구성을 찾습니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["필요한 정보가 없음","missingTheInformationINeed","thumb-down"],["너무 복잡함/단계 수가 너무 많음","tooComplicatedTooManySteps","thumb-down"],["오래됨","outOfDate","thumb-down"],["번역 문제","translationIssue","thumb-down"],["샘플/코드 문제","samplesCodeIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-08-26(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)."]]
