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print(f"Received event with ID: {cloud_event['id']} and data {cloud_event.data}")
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```
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> Your function is passed a single [CloudEvent](https://github.com/cloudevents/sdk-python/blob/master/cloudevents/sdk/event/v1.py) parameter.
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> Your function is passed a single [CloudEvent](https://github.com/cloudevents/sdk-python/blob/main/cloudevents/sdk/event/v1.py) parameter.
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Run the following command to run `hello_cloud_event` target locally:
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@@ -302,7 +302,7 @@ After you've written your function, you can simply deploy it from your local mac
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### Cloud Run/Cloud Run on GKE
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Once you've written your function and added the Functions Framework to your `requirements.txt` file, all that's left is to create a container image. [Check out the Cloud Run quickstart](https://cloud.google.com/run/docs/quickstarts/build-and-deploy) for Python to create a container image and deploy it to Cloud Run. You'll write a `Dockerfile` when you build your container. This `Dockerfile` allows you to specify exactly what goes into your container (including custom binaries, a specific operating system, and more). [Here is an example `Dockerfile` that calls Functions Framework.](https://github.com/GoogleCloudPlatform/functions-framework-python/blob/master/examples/cloud_run_http)
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Once you've written your function and added the Functions Framework to your `requirements.txt` file, all that's left is to create a container image. [Check out the Cloud Run quickstart](https://cloud.google.com/run/docs/quickstarts/build-and-deploy) for Python to create a container image and deploy it to Cloud Run. You'll write a `Dockerfile` when you build your container. This `Dockerfile` allows you to specify exactly what goes into your container (including custom binaries, a specific operating system, and more). [Here is an example `Dockerfile` that calls Functions Framework.](https://github.com/GoogleCloudPlatform/functions-framework-python/blob/main/examples/cloud_run_http)
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If you want even more control over the environment, you can [deploy your container image to Cloud Run on GKE](https://cloud.google.com/run/docs/quickstarts/prebuilt-deploy-gke). With Cloud Run on GKE, you can run your function on a GKE cluster, which gives you additional control over the environment (including use of GPU-based instances, longer timeouts and more).
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