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 tutorial is for you to build a container image and deploy it to Cloud Run.

Codelab-at-a-conference setup

By using a kiosk at Google I/O, a test project has been created and can be accessed by using going to: https://console.cloud.google.com/.

These temporary accounts have existing projects that are set up with billing so that there are no costs associated for you with running this codelab.

Note that all these accounts will be disabled soon after the codelab is over.

Use these credentials to log into the machine or to open a new Google Cloud Console window https://console.cloud.google.com/. Accept the new account Terms of Service and any updates to Terms of Service.

When presented with this console landing page, please select the only project available. Alternatively, from the console home page, click on "Select a Project" :

Start Cloud Shell

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

Activate Google Cloud Shell

From the GCP Console click the Cloud Shell icon on the top right toolbar:

If you've never started Cloud Shell before, you'll be presented with an intermediate screen (below the fold) describing what it is. If that's the case, click "Continue" (and you won't ever see it again). Here's what that one-time screen looks like:

It should only take a few moments to provision and connect to the shell environment:

This 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. Much, if not all, of your work in this lab can be done with simply a browser or your Google Chromebook.

Once connected to Cloud Shell, you should see that you are already authenticated and that the project is already set to your PROJECT_ID.

Run the following command in Cloud Shell to confirm that you are authenticated:

gcloud auth list

Command output

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

Command output

[core]
project = <PROJECT_ID>

If it is not, you can set it with this command:

gcloud config set project <PROJECT_ID>

Command output

Updated property [core/project].

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

gcloud services enable cloudbuild.googleapis.com run.googleapis.com

This should produce a successful message similar to this one:

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

You'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 "Open Editor" pen-shaped icon), create a file named app.py and paste the following code into it:

app.py

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.
    app.run(host='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:

Dockerfile

# Use an official lightweight Python image.
# https://hub.docker.com/_/python
FROM python:3.7-slim

# Install production dependencies.
RUN pip install Flask gunicorn

# Copy local code to the container image.
WORKDIR /app
COPY . .

# Service must listen to $PORT environment variable.
# This default value facilitates local development.
ENV PORT 8080

# 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 0.0.0.0:$PORT --workers 1 --threads 8 --timeout 0 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)
echo $PROJECT_ID

DOCKER_IMG="gcr.io/$PROJECT_ID/helloworld-python"
echo $DOCKER_IMG

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.

Cloud Run is regional, which means the infrastructure that runs your Cloud Run services is located in a specific region and is managed by Google to be redundantly available across all the zones within that region. Define the region you'll use for your deployment, for example:

REGION="europe-west1"

Deploy your containerized application to Cloud Run with the following command:

gcloud run deploy helloworld-python \
  --image $DOCKER_IMG \
  --platform managed \
  --region $REGION \
  --allow-unauthenticated

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

Deploying container to Cloud Run service [helloworld-python] in project [PROJECT_ID...
✓ Deploying new service... Done.                                   
  ✓ Creating Revision... Revision deployment finished. Waiting for health check...
  ✓ Routing traffic...
  ✓ Setting IAM Policy...
Done.
Service [helloworld-python] revision [helloworld-python-...] has been deployed
and is serving 100 percent of traffic at https://helloworld-python-...run.app

You can also retrieve your service URL:

SERVICE_URL=$( \
  gcloud run services describe helloworld-python \
  --platform managed \
  --region $REGION  \
  --format "value(status.url)" \
)
echo $SERVICE_URL

This should display something like:

https://helloworld-python-...run.app

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

You can also call your service from Cloud Shell:

curl $SERVICE_URL?who=me

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 your Cloud Run service, use this command:

gcloud run services delete helloworld-python \
  --platform managed \
  --region $REGION

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: