Getting Started with Google Cloud Run: Deploying Python Scripts
Google Cloud Run is a fully managed compute platform that automatically scales your Python scripts in containers. It allows developers to run their code in containers, deploy it in a serverless environment, and easily scale applications as needed. In this article, we’ll explore how to use Google Cloud Run to deploy and run Python scripts, making it an ideal platform for building and deploying web services, APIs, and more.
Before you begin, make sure you have the following:
- A Google Cloud Platform (GCP) account.
- Google Cloud SDK installed on your local machine.
- A basic understanding of Python and containerization.
Let’s dive into deploying a Python script on Google Cloud Run. We’ll go through the following steps:
- Writing a Python Script
- Creating a Container Image
- Deploying the Image on Google Cloud Run
Step 1: Writing a Python Script
For this example, we’ll create a simple Python script that serves an HTTP response using Flask. First, create a Python file named app.py:
from flask import Flask
app = Flask(__name)
return ‘Hello, Cloud Run!’
if __name__ == ‘__main__’:
This script uses the Flask web framework to create a basic web server that responds with “Hello, Cloud Run!” when accessed.
Step 2: Creating a Container Image
To deploy your Python script on Google Cloud Run, you need to containerize it. Create a Dockerfile in the same directory as your Python script:
# Use the official Python runtime as a parent image
# Set the working directory to /app
# Copy the current directory contents into the container at /app
COPY . /app
# Install any needed packages specified in requirements.txt
RUN pip install — trusted-host pypi.python.org -r requirements.txt
# Make port 8080 available to the world outside this container
# Define environment variable
ENV NAME World
# Run app.py when the container launches
CMD [“python”, “app.py”]
This Dockerfile uses an official Python 3.9 image, sets up the working directory, copies the Python script and requirements file, installs dependencies, exposes port 8080, and runs the Python script.
Step 3: Deploying the Image on Google Cloud Run
Before deploying the container image, you need to build and push it to a container registry. Google Cloud provides a Container Registry service, but you can also use other container registries like Docker Hub.
To build the image and push it to Google Container Registry, use the following commands:
# Set environment variables
# Build the container image
docker build -t gcr.io/$PROJECT_ID/$IMAGE_NAME:$TAG .
# Push the container image to Google Container Registry
docker push gcr.io/$PROJECT_ID/$IMAGE_NAME:$TAG
Make sure to replace your-project-id with your GCP project ID and set a suitable image name and tag.
With the container image pushed to the registry, you can now deploy it on Google Cloud Run:
# Deploy the container image to Cloud Run
gcloud run deploy $IMAGE_NAME \
— image gcr.io/$PROJECT_ID/$IMAGE_NAME:$TAG \
— platform managed \
— region $REGION
During deployment, you will be prompted to allow unauthenticated access. Choose the “Allow all” option.
You have successfully deployed a Python script as a containerized application on Google Cloud Run. This serverless platform allows you to run your Python scripts with ease, automatically handling scaling and load balancing. It is powered by Knative.