This page shows how to deploy container images to a new Cloud Run worker pool or to a new revision of an existing Cloud Run worker pool.

Use the Google Cloud console, Google Cloud CLI, or YAML to see more details about a worker pool: ... Select Worker pools from the menu to display the available worker pools. Click the worker pool to display its details pane.

2 weeks ago - Pull-based workloads: Worker pools provide a reliable environment for running and scaling workloads that continuously pull messages from queues like Pub/Sub, Kafka, Github Runners or Redis task queues. Distributed AI/ML workloads: Worker pools are a great fit for distributed LLM training or fine-tuning workloads. At GA, worker pools support NVIDIA L4 and RTX PRO 6000 (Blackwell) GPUs.

gcloud run deploy SERVICE \ --source . \ --build-worker-pool WORKER_POOL ... SERVICE with name of your service. WORKER_POOL with the name of the private pool. If you are deploying a function, add the --function flag with the function entry point from your source code. You can delete build worker pools for existing services. To clear the Cloud Build worker pool for source deployments, use the --clear-build-worker-pool flag:

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions. After you've created a Cloud Run worker pool, you can use Workflows to automate its scaling.

To increase the availability of your GPU-accelerated worker pools during zonal outages, you can configure zonal redundancy specifically for GPUs: Zonal Redundancy Turned On (default): Cloud Run reserves GPU capacity for your worker pool across multiple zones.

If you use multiple arguments, specify each on its own line—for example, as shown, ARG-N. Create or update the worker pool using the following command: gcloud beta run worker-pools replace workerpool.yaml

gcloud iam service-accounts create ${SERVICE_ACCOUNT} \ --display-name="Service account for worker pool codelab" Lastly, grant your Cloud Run service account access to PubSub:

resource "google_cloud_run_v2_worker_pool" "default" { name = "cloudrun-worker-pool" location = "us-central1" deletion_protection = false template { containers { name = "foo-1" image = "us-docker.pkg.dev/cloudrun/container/worker-pool" depends_on = ["foo-2"] } containers { name = "foo-2" image = "us-docker.pkg.dev/cloudrun/container/worker-pool" startup_probe { http_get { path = "/healthz" port = 8080 } period_seconds = 5 timeout_seconds = 2 failure_threshold = 3 } } } }Copy

3 weeks ago - Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions. This tutorial shows you how to deploy a Cloud Run worker pool to process Pub/Sub messages, and automatically scale your consumer instances based on queue depth using Cloud Run External Metrics Autoscaling (CREMA).

To update the number of instances for a given worker pool, send a PATCH HTTP request to the Cloud Run Admin API workerPools endpoint.

1 month ago - Deploy the autoscaler CREMA service to dynamically scale the worker pool based on the Prometheus metrics. Test your CREMA service by observing service logs and verifying instance count changes in the Google Cloud console. In this document, you use the following billable components of Google Cloud: ... To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions. This page describes how to deploy a new worker pool or worker pool revision to Cloud Run directly from source code using a single gcloud CLI command, gcloud beta run worker-pools deploy with the --source flag.

Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions. This page provides best practices for optimizing performance when using a Cloud Run worker pool with AI workloads such as, training large language models (LLMs) using your preferred frameworks, fine-tuning, and performing batch or offline inference on LLMs.

IMAGE_URL: a reference to the container image that contains the worker pool, such as us-docker.pkg.dev/cloudrun/container/worker-pool:latest. CPU: the CPU limit value. Specify the value 1, 2, 4, 6, or 8 CPUs, or for less than 1 CPU, specify a value from 0.08 to less than 1.00, in increments of 0.01. (See the table under Setting and updating CPU limits for required settings.) Create or update the worker pool using the following command: gcloud beta run worker-pools replace workerpool.yaml

April 24, 2025 - Cloud Run IAP built-in Cloud Run GPU Cloud Run Worker Pools Cloud Run Health for regional failover VPC egressの改善 Cloud Run Threat Detection プレビュー ・ロードバランサ不要でIAPが適用可能 GA ・その名の通り、Cloud RunでGPUが利用可能 ・Tokyoリージョンは未サポート プレビュー ・service/jobsとは異なる新しい実行モデル ・詳細はのちほど GA ・確保が必要なサブネットIPアドレス数が半減(4倍→2倍) ・Cloud Run jobsでも利用可 プレビュー ・Security Command Centerとの統合 ・ランタイム攻撃や脆弱性等を検知 プレビュー ・マルチリージョンにおけるフェイルオーバーサポート ・startupProbeのシグナルとServerless NEGの連携

Behind the scenes, this command uses Google Cloud's buildpacks and Cloud Build to automatically build container images from your source code without having to install Docker on your machine or set up buildpacks or Cloud Build. By default, Cloud Run uses the default machine type provided by Cloud Build. Running gcloud beta run worker-pools deploy eliminates the need to also run the gcloud builds submit command.

4 days ago - WORKERPOOL: the name of your worker pool. VOLUME_NAME: the name you want to give your volume. SIZE: the disk size—for example, 100Gi. The size must be at least 10Gi for ephemeral-disk volumes. MOUNT_PATH: the relative path where you are mounting the volume, for example, /mnt/my-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.