Serverless Workers on GCP Cloud Run - Python SDK

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On a GCP Cloud Run worker pool, you run a standard long-lived Temporal Worker. Register Workflows and Activities the same way you would with any other Python Worker, and Temporal Cloud scales the pool up and down as work arrives and drains.

A Cloud Run Worker needs no Cloud Run-specific package. The one addition to a standard Worker is Worker Versioning, which is required for Serverless Workers.

For the end-to-end deployment guide covering the Worker Pool, IAM, and compute configuration, see Deploy a Serverless Worker on GCP Cloud Run.

Create a versioned Worker

Build the Worker as you would any long-running Python Worker, then pass deployment_config to Worker() to declare the Worker Deployment Version and turn versioning on.

The following Worker reads its connection settings and Task Queue from the environment, so the same image can run against any Namespace:

import asyncio
import os

from temporalio.client import Client
from temporalio.common import VersioningBehavior, WorkerDeploymentVersion
from temporalio.envconfig import ClientConfig
from temporalio.worker import Worker, WorkerDeploymentConfig

from my_activities import my_activity
from my_workflows import MyWorkflow


async def main() -> None:
    client = await Client.connect(**ClientConfig.load_client_connect_config())

    worker = Worker(
        client,
        task_queue=os.environ["TEMPORAL_TASK_QUEUE"],
        workflows=[MyWorkflow],
        activities=[my_activity],
        deployment_config=WorkerDeploymentConfig(
            version=WorkerDeploymentVersion(
                deployment_name="my-app",
                build_id="build-1",
            ),
            use_worker_versioning=True,
            default_versioning_behavior=VersioningBehavior.PINNED,
        ),
    )
    await worker.run()


if __name__ == "__main__":
    asyncio.run(main())

deployment_name and build_id together identify the Worker Deployment Version. Both values must match the version you create with temporal worker deployment create-version in the deployment guide, or the Worker polls under a version the WCI does not manage.

Every Workflow needs a versioning behavior, either PINNED or AUTO_UPGRADE. Setting default_versioning_behavior as shown above covers every Workflow on the Worker. To set the behavior per Workflow instead, pass versioning_behavior to the @workflow.defn decorator:

from temporalio import workflow
from temporalio.common import VersioningBehavior


@workflow.defn(versioning_behavior=VersioningBehavior.PINNED)
class MyWorkflow:
    @workflow.run
    async def run(self, name: str) -> str:
        ...

For general Worker setup and options that are not specific to Cloud Run, see Run a Worker.

Configure the Temporal connection

The temporalio.envconfig package loads Temporal Client configuration from environment variables and an optional TOML config file, so the Worker code carries no Namespace or credentials. Set the non-secret values as environment variables on the Worker Pool, and mount the Temporal Cloud API key or TLS material from Secret Manager. For the full list of supported variables, the config file format, and profiles, see Environment configuration.

ClientConfig.load_client_connect_config() returns the keyword arguments for Client.connect, which is why the Worker above unpacks it with **. To inspect or change values before connecting, load the profile instead and convert it yourself:

from temporalio.envconfig import ClientConfigProfile

profile = ClientConfigProfile.load()
connect_config = profile.to_client_connect_config()
client = await Client.connect(**connect_config)

Keep Activities safe across scale-in

The WCI decides when to remove an instance from Task Queue activity, not from what an individual instance is doing. An instance running a long Activity can be stopped mid-execution.

Use Activity Heartbeats so a retry resumes from the last recorded progress instead of starting over:

from temporalio import activity


@activity.defn
async def my_activity(items: list[str]) -> str:
    for i, item in enumerate(items):
        activity.heartbeat(i)
        # ... process item
    return "done"

For how scale-in decisions are made, see Serverless Workers on GCP Cloud Run.

Add observability

A Cloud Run Worker emits the same traces and metrics as a Worker anywhere else. For how to configure metrics export and OpenTelemetry tracing interceptors, see Observability - Python SDK and the SDK metrics reference.

Create a versioned Worker​

Configure the Temporal connection​

Keep Activities safe across scale-in​

Add observability​

Create a versioned Worker

Configure the Temporal connection

Keep Activities safe across scale-in

Add observability