Cloud Run is a managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. Cloud Run is serverless: it abstracts away all infrastructure management, so you can focus on what matters most — building great applications.

It is built from Knative, letting you choose to run your containers either fully managed with Cloud Run, or in your Google Kubernetes Engine cluster with Cloud Run on GKE.

The goal of this codelab is for you to build a container image and deploying it to Cloud Run.

Self-paced environment setup

If you don't already have a Google Account (Gmail or Google Apps), you must create one. Sign-in to Google Cloud Platform console ( and create a new project:

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Remember the project ID, a unique name across all Google Cloud projects (the name above has already been taken and will not work for you, sorry!). It will be referred to later in this codelab as PROJECT_ID.

Next, you'll need to enable billing in the Cloud Console in order to use Google Cloud resources.

Running through this codelab shouldn't cost you more than a few dollars, but it could be more if you decide to use more resources or if you leave them running (see "cleanup" section at the end of this document).

New users of Google Cloud Platform are eligible for a $300 free trial.

Google Cloud Shell

While Google Cloud can be operated remotely from your laptop, in this codelab we will be using Google Cloud Shell, a command line environment running in the Cloud.

This Debian-based virtual machine is loaded with all the development tools you'll need. It offers a persistent 5GB home directory, and runs on the Google Cloud, greatly enhancing network performance and authentication. This means that all you will need for this codelab is a browser (yes, it works on a Chromebook).

To activate Google Cloud Shell, from the developer console simply click the button on the top right-hand side (it should only take a few moments to provision and connect to the environment):


Click the "Start Cloud Shell" button:

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Once connected to the cloud shell, you should see that you are already authenticated and that the project is already set to your PROJECT_ID :

gcloud auth list

Command output

Credentialed accounts:
 - <myaccount>@<mydomain>.com (active)
gcloud config list project

Command output

project = <PROJECT_ID>

Cloud Shell also sets some environment variables by default which may be useful as you run future commands.


Command output


If for some reason the project is not set, simply issue the following command :

gcloud config set project <PROJECT_ID>

Looking for your PROJECT_ID? Check out what ID you used in the setup steps or look it up in the console dashboard:


IMPORTANT: Finally, set the default zone and project configuration:

gcloud config set compute/zone us-central1-f

You can choose a variety of different zones. Learn more in the Regions & Zones documentation.

Enable the Cloud Run API

From Cloud Shell, enable the Cloud Build and Cloud Run APIs:

gcloud services enable

This should produce a successful message similar to this one:

Operation "operations/acf.cc11852d-40af-47ad-9d59-477a12847c9e" finished successfully.

We'll build a simple Flask-based Python application responding to HTTP requests.

To build your application, use Cloud Shell to create a new directory named helloworld-python and change directory into it:

mkdir ~/helloworld-python
cd ~/helloworld-python

Using one of your preferred command line editors (nano, vim, or emacs) or the Cloud Shell web editor (click on the "Launch code editor" pen-shaped icon), create a file named and paste the following code into it:

from flask import Flask, request

app = Flask(__name__)

@app.route('/', methods=['GET'])
def hello():
    """Return a friendly HTTP greeting."""
    who = request.args.get('who', 'World')
    return f'Hello {who}!\n'

if __name__ == '__main__':
    # Used when running locally only. When deploying to Cloud Run,
    # a webserver process such as Gunicorn will serve the app.'localhost', port=8080, debug=True)

This code creates a basic web server responding to HTTP GET requests with a friendly message. Your app is now ready to be containerized, tested, and uploaded to Container Registry.

To containerize the sample app, create a new file named Dockerfile in the same directory as the source files, and copy the following content:


# Use an official lightweight Python image.
FROM python:3.7-slim

# Install production dependencies.
RUN pip install Flask gunicorn

# Copy local code to the container image.
COPY . .

# Service must listen to $PORT environment variable.
# This default value facilitates local development.

# Run the web service on container startup. Here we use the gunicorn
# webserver, with one worker process and 8 threads.
# For environments with multiple CPU cores, increase the number of workers
# to be equal to the cores available.
CMD exec gunicorn --bind$PORT --workers 1 --threads 8 app:app

Define the PROJECT_ID and DOCKER_IMG environment variables which will be used throughout the next steps and make sure they have the correct values:

PROJECT_ID=$(gcloud config get-value project)


Now, build your container image using Cloud Build, by running the following command from the directory containing the Dockerfile:

gcloud builds submit --tag $DOCKER_IMG

Once pushed to the registry, you will see a SUCCESS message containing the image name. The image is stored in Container Registry and can be re-used if desired.

You can list all the container images associated with your current project using this command:

gcloud container images list

Before deploying, run and test the application locally from Cloud Shell, you can start it using these standard docker commands:

docker pull $DOCKER_IMG
docker run -p 8080:8080 $DOCKER_IMG

In the Cloud Shell window, click on the "Web preview" icon and select "Preview on port 8080":

This should open a browser window showing the "Hello World!" message. You can also simply use curl localhost:8080 from another Cloud Shell session. When you're done, you can stop your docker run command with Ctrl+c.

Deploying your containerized application to Cloud Run is done using the following command:

gcloud run deploy --image $DOCKER_IMG --platform managed

When prompted:

Then wait a few moments until the deployment is complete. On success, the command line displays the service URL:

Service [helloworld-python] revision [helloworld-python-...] has been deployed
and is serving traffic at

You can now visit your deployed container by opening the service URL in a web browser:

Congratulations! You have just deployed an application packaged in a container image to Cloud Run. Cloud Run automatically and horizontally scales your container image to handle the received requests, then scales down when demand decreases. You only pay for the CPU, memory, and networking consumed during request handling.

While Cloud Run does not charge when the service is not in use, you might still be charged for storing the built container image.

You can either decide to delete your GCP project to avoid incurring charges, which will stop billing for all the resources used within that project, or simply delete your helloworld-python image using this command:

gcloud container images delete $DOCKER_IMG

To delete the Cloud Run service, use this command:

gcloud run services delete helloworld-python

A good next step would be to Deploy to Cloud Run on GKE.

For more information on building a stateless HTTP container suitable for Cloud Run from code source and pushing it to Container Registry, see: