[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["没有我需要的信息","missingTheInformationINeed","thumb-down"],["太复杂/步骤太多","tooComplicatedTooManySteps","thumb-down"],["内容需要更新","outOfDate","thumb-down"],["翻译问题","translationIssue","thumb-down"],["示例/代码问题","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-08-25。"],[],[],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)."]]