This page describes Google Cloud products that are integrated with Cloud Storage FUSE.
For a list of Google Cloud products that are integrated with Cloud Storage generally, see Integration with Google Cloud services and tools.
| Product | How Cloud Storage FUSE is integrated |
|---|---|
| Google Kubernetes Engine (GKE) | The Cloud Storage FUSE CSI driver manages the integration of Cloud Storage FUSE with the Kubernetes API to consume Cloud Storage buckets as volumes. You can use the Cloud Storage FUSE CSI driver to mount buckets as file systems on Google Kubernetes Engine nodes. |
| Vertex AI training | You can access data from a Cloud Storage bucket as a mounted file system when you perform custom training on Vertex AI. For more information, see Prepare training code. |
| Vertex AI Workbench | Vertex AI Workbench instances include a Cloud Storage integration that lets you browse buckets and work with compatible files located in Cloud Storage from within the JupyterLab interface. The Cloud Storage integration lets you access all of the Cloud Storage buckets and files that your instance has access to within the same project as your Vertex AI Workbench instance. To set up the integration, see Vertex AI Workbench instructions for how to access Cloud Storage buckets and files in JupyterLab | .
| Deep Learning VM Images | Cloud Storage FUSE comes pre-installed with Deep Learning VM Images. |
| Deep Learning Containers | To mount Cloud Storage buckets for Deep Learning Containers, you can either use the Cloud Storage FUSE CSI driver (recommended) or install Cloud Storage FUSE. |
| Batch | Cloud Storage FUSE lets you mount Cloud Storage buckets as storage volumes when you create and run Batch jobs. You can specify a bucket in a job's definition, and the bucket gets automatically mounted to the VMs for the job when the job runs. |
| Cloud Run | Cloud Run lets you mount a Cloud Storage bucket as a volume and presents the bucket content as files in the container file system. To set up volume mounting, see Mount a Cloud Storage volume. |
| Cloud Composer | When you create an environment, Cloud Composer stores the source code for your workflows and their dependencies in specific folders in a Cloud Storage bucket. Cloud Composer uses Cloud Storage FUSE to map the folders in the bucket to the Airflow components in the Cloud Composer environment. |
Cloud Storage FUSE for machine learning
Cloud Storage FUSE is a common choice for developers looking to store and access machine learning (ML) training and model data as objects in Cloud Storage. Cloud Storage FUSE provides several benefits for developing ML projects:
Cloud Storage FUSE lets you mount Cloud Storage buckets as a local file system so your applications can access training and model data using standard file system semantics. This means that you can avoid the cost of rewriting or refactoring your application's code when using Cloud Storage to store ML data.
From training to inference, Cloud Storage FUSE lets you use the built-in high scalability, performance, and cost effectiveness of Cloud Storage, so you can run your ML workloads at scale.
Cloud Storage FUSE lets you start training jobs quickly by providing compute resources with direct access to data in Cloud Storage, so you don't need to download training data to the compute resource.
For more information, see ML frameworks supported by Cloud Storage FUSE.