docker.md

Run a Function in a Docker Container

To run your function in a container, create a Dockerfile with the following contents:

# Use the official Node.js 10 image.
# https://hub.docker.com/_/node
FROM node:10
# Create and change to the app directory.
WORKDIR /usr/src/app
# Copy application dependency manifests to the container image.
# A wildcard is used to ensure both package.json AND package-lock.json are copied.
# Copying this separately prevents re-running npm install on every code change.
COPY package.json package*.json ./
# Install production dependencies.
RUN npm install --only=production
# Copy local code to the container image.
COPY . .
# Run the web service on container startup.
CMD [ "npm", "start" ]

Start the container locally by running docker build and docker run:

docker build -t helloworld . && docker run --rm -p 8080:8080 helloworld

Send requests to this function using curl from another terminal window:

curl localhost:8080
# Output: Hello, World

Configure gcloud

To use Docker with gcloud, configure the Docker credential helper:

gcloud auth configure-docker

Deploy a Container

You can deploy your containerized function to Cloud Run by following the Cloud Run quickstart.

Use the docker and gcloud CLIs to build and deploy a container to Cloud Run, replacing the project id $GOOGLE_CLOUD_PROJECT and image name helloworld:

docker build -t gcr.io/$GOOGLE_CLOUD_PROJECT/helloworld .
docker push gcr.io/$GOOGLE_CLOUD_PROJECT/helloworld
gcloud run deploy helloworld --image gcr.io/$GOOGLE_CLOUD_PROJECT/helloworld --region us-central1

If you want even more control over the environment, you can deploy your container image to Cloud Run on 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).