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de execution browser automation inference with gpus overview services run llm inference on cloud run gpus with ollama run agents with gemma 4 models on cloud run run opencv on cloud run with gpu acceleration run llm inference on cloud run gpus with hugging face transformers js jobs fine tune llms using gpus with cloud run jobs run batch inference using gpus with cloud run jobs gpu accelerated video transcoding with ffmpeg ai assisted development and vibe coding introduction to cloud run for ai assisted developers cookbook migrate an existing web service from app engine from cloud run functions 1st gen from aws lambda from heroku from cloud foundry migration overview choose an oci compliant strategy migrate to oci containers migrate configuration sample migration spring music from vmware tanzu from a vm using migrate to containers from kubernetes to gke troubleshoot introduction troubleshoot errors local troubleshooting tutorial known issues samples all cloud run code samples all cloud run functions code samples code samples for all products ai and ml application development application hosting compute data analytics and pipelines databases distributed hybrid and multicloud industry solutions migration networking observability and monitoring security storage access and resources management costs and usage management infrastructure as code sdk languages frameworks and tools home documentation application hosting cloud run guides send feedback configure cloud storage volume mounts for worker pools stay organized with collections save and categorize content based on your preferences this page shows how to mount a cloud storage bucket as a storage volume using cloud run volume mounts mounting the bucket as a volume in cloud run presents the bucket content as files in the container file system after you mount the bucket as a volume you access the bucket as if it were a directory on your local file system using your programming language s file system operations and libraries instead of using google api client libraries you can mount your volume as read only and you can also specify mount options for your volume important you don t need to install cloud storage fuse in your container or modify the container in any way to use this feature memory requirements cloud storage volume mounts use the cloud run container memory for the following activities for all cloud storage fuse caching cloud run uses the stat cache setting with a time to live ttl of 60 seconds by default the default maximum size of the stat cache is 32 mb the default maximum size of the type cache is 4 mib when reading from cloud storage cloud storage fuse makes api calls to read an object directly without downloading the whole file to a local directory cloud storage fuse establishes a tcp connection and reads back the entire cloud storage object or just portions of the file you specify in your application or the operating system through an offset when reading cloud storage fuse also consumes memory other than stat and type caches such as a 1 mib array for every file it reads and for goroutines when writing to cloud storage cloud storage fuse supports streaming writes a write path by default cloud storage fuse uploads data directly to cloud storage without fully staging the file each file you open for streaming writes consumes approximately 64 mib of memory during the upload process this reduces both latency and disk space usage making it particularly beneficial for large sequential writes note for background information on caching see the cloud storage fuse read write page limitations since cloud run uses cloud storage fuse for this volume mount there are a few things to keep in mind when mounting a cloud storage bucket as a volume cloud storage fuse does not provide concurrency control for multiple writes file locking to the same file when multiple writes try to replace a file the last write wins and all previous writes are lost cloud storage fuse is not a fully posix compliant file system for more details refer to the cloud storage fuse documentation disallowed paths cloud run does not allow you to mount a volume at dev proc or sys or on their subdirectories before you begin you need a cloud storage bucket to mount as the volume for optimal read write performance to cloud storage see optimizing cloud storage fuse network bandwidth performance required roles to get the permissions that you need to configure cloud storage volume mounts ask your administrator to grant you the following iam roles cloud run developer roles run developer on the cloud run worker pool service account user roles iam serviceaccountuser on the service identity to get the permissions that your service identity needs to access the file and cloud storage bucket ask your administrator to grant the service identity the storage object viewer roles storage objectviewer role if the service identity needs to also perform write operations in a bucket grant the storage object user roles storage objectuser role instead for more details on cloud storage roles and permissions see iam for cloud storage for a list of iam roles and permissions that are associated with cloud run see cloud run iam roles and cloud run iam permissions if your cloud run worker pool interfaces with google cloud apis such as cloud client libraries see the service identity configuration guide for more information about granting roles see deployment permissions and manage access mount a cloud storage volume you can mount multiple buckets at different mount paths you can also mount a volume to more than one container using the same or different mount paths across containers if you are using multiple containers first specify the volumes then specify the volume mounts for each container you can configure a cloud storage volume for a cloud run worker pool using the google cloud console the google cloud cli yaml or terraform console in the google cloud console go to cloud run go to cloud run select worker pools from the menu and click deploy container to configure a new worker pool if you are configuring an existing worker pool click the worker pool then click edit and deploy new revision if you are configuring a new worker pool fill out the initial worker pool page then click containers networking security to expand the worker pools configuration page click the volumes tab click mount volume select cloud storage bucket as the volume type in the mount path field enter the path where you want to mount the volume browse and select the cloud storage bucket or a specific directory to be used for the volume optionally create a new bucket if you want to make the bucket read only select the read only checkbox optionally expand mount options to specify mount options click save click create or deploy gcloud to add a volume mount gcloud run worker pools update worker_pool add volume mount path mount_path type cloud storage bucket bucket_name readonly read_only replace the following worker_pool the name of your worker pool mount_path the relative path where you are mounting the volume for example mnt my volume bucket_name the name of your cloud storage bucket read_only true to make the volume read only or false to allow writes if you are using multiple containers first specify your volume s then specify the volume mount s for each container gcloud run worker pools update worker_pool add volume name volume_name type cloud storage bucket bucket_name container container_1 add volume mount volume volume_name mount path mount_path container container_2 add volume mount volume volume_name mount path mount_path2 yaml if you are creating a new worker pool skip this step if you are updating an existing worker pool download its yaml configuration gcloud run worker pools describe worker_pool format export worker pool yaml the following example contains the yaml configuration apiversion run googleapis com v1 kind workerpool metadata name worker_pool spec template spec containers image image_url volumemounts name translate no mountpath mount_path volumes name volume_name csi driver gcsfuse run googleapis com readonly is_read_only volumeattributes bucketname bucket_name replace the following worker_pool the name of your cloud run worker pool image_url a reference to the container image that contains the worker pool such as us docker pkg dev cloudrun container worker pool latest mount_path the relative path where you are mounting the volume for example mnt my volume volume_name any name you want for your volume the volume_name value is used to map the volume to the volume mount is_read_only true to make the volume read only or false to allow writes bucket_name the name of the cloud storage bucket create or update the worker pool using the following command gcloud run worker pools replace worker pool yaml the gcloud run worker pools replace command defaults to using worker pool yaml file if present terraform to learn how to apply or remove a terraform configuration see basic terraform commands resource google_cloud_run_v2_worker_pool default name worker_pool location region template containers image image_url volume_mounts name volume_name mount_path mount_path volumes name volume_name gcs bucket google_storage_bucket default name read_only is_read_only resource google_storage_bucket default name bucket_name location region replace the following worker_pool the name of your worker pool region the google cloud region image_url a reference to the container image that contains the worker pool such as us docker pkg dev cloudrun container worker pool latest volume_name any name you want for your volume the volume_name value is used to map the volume to the volume mount mount_path the relative path where you are mounting the volume for example mnt my volume is_read_only true to make the volume read only or false to allow writes bucket_name the name of the cloud storage bucket view environment variable configuration for the worker pool in the google cloud console go to cloud run go to cloud run click worker pools to display the list of deployed worker pools click the worker pool you want to examine to display its details pane click the containers tab to display worker pool container configuration reading and writing to a volume if you use the cloud run volume mount feature you access a mounted volume using the same libraries in your programming language that you use to read and write files on your local file system this is especially useful if you re using an existing container that expects data to be stored on the local file system and uses regular file system operations to access it the following snippets assume a volume mount with a mountpath set to mnt my volume nodejs use the file system module to create a new file or append to an existing file in the volume mnt my volume var fs require fs fs appendfilesync mnt my volume sample logfile txt hello logs flag a python write to a file kept in the volume mnt my volume f open mnt my volume sample logfile txt a go use the os package to create a new file kept in the volume mnt my volume f err os create mnt my volume sample logfile txt java use the java io file class to create a log file in the volume mnt my volume import java io file file f new file mnt my volume sample logfile txt volume configuration using mount options you can optionally use mount options to configure various properties of your volume mount the available mount options allow you to configure cache settings mount a specific directory enable debug logging and other behaviors specify mount options you can specify mount options using the google cloud console the google cloud cli yaml or terraform the mount options are separated by semicolons in google cloud cli and are separated by commas in yaml as shown in the following tabs console to specify mount options for an existing volume in the google cloud console go to the cloud run worker pools page go to cloud run click the worker pool then click edit and deploy new revision click the volumes tab click the edit volume mount expand mount options configure mount options by updating the appropriate fields or by adding mount options manually click save click deploy gcloud to add a volume and mount it with mount options gcloud run worker pools update worker_pool add volume mount path mount_path type cloud storage bucket bucket_name readonly read_only mount options option_1 value_1 option_n value_n replace the following worker_pool the name of your worker pool mount_path the relative path where you are mounting the volume for example cache bucket_name the name of your cloud storage bucket option_1 the first mount option note that you can specify as many mount options as you need with each mount option and value pair separated by a semicolon value_1 the setting you want for the first mount option option_n the next mount option value_n the setting for the next mount option commonly used mount options mount options are commonly used to configure cache settings mount only a specific directory from the cloud storage bucket configure the ownership of the volume uid gid turn off implicit directories specify debug logging levels configure caching settings note refer to the cloud storage fuse caching documentation for more information on cache options you can change the caching settings for your volume by setting the caching related mount options the following table lists the settings along with the default cloud run values cache setting description default cache dir the in memory volume name to use as the underlying directory to persist files from your cloud storage bucket in the format cr volume volume name for example if you have an in memory volume named in memory 1 that you want to use as the cache directory specify cr volume in memory 1 for instructions on setting up in memory volumes see configure in memory volume mounts for services when you enable these features cloud run changes the resource accounting of the cloud storage fuse process and counts this towards the container memory limits to increase the container memory limit see configure memory limits for services file cache download chunk size mb specifies the size of each read request in mib that each goroutine makes to cloud storage when downloading the object into the file cache 200 file cache enable parallel downloads accelerates reads of large files by using the file cache directory as a prefetch buffer using multiple workers to download multiple parts of a file in parallel true file cache max parallel downloads the maximum number of goroutines that can be spawned at any given time across all the download jobs of files twice the number of cpu cores on your machine or 16 whichever is higher file cache parallel downloads per file the number of concurrent download requests per file 16 file cache cache file for range read determines whether the full object should be downloaded asynchronously and stored in the cloud storage fuse cache directory when the f...