Google Cloud Run

Lithops with GCP Cloud Run as serverless compute backend.

Installation

1. Install Google Cloud Platform backend dependencies:

python3 -m pip install lithops[gcp]

2. Login to Google Cloud Console (or sign up if you don’t have an account).

3. Create a new project. Name it lithops or similar.

4. Navigate to IAM & Admin > Service Accounts.

5. Click on Create Service Account. Name the service account lithops-executor or similar. Then click on Create.

6. Add the following roles to the service account:

   • Service Accounts –> Service Account User

   • Cloud Run –> Cloud Run Admin

   • Cloud Storage –> Storage Admin

7. Click on Continue. Then, click on Create key. Select JSON and then Create. Download the JSON file to a secure location on your computer. Click Done.

8. Enable the Cloud Build API : Navigate to APIs & services tab on the menu. Click ENABLE APIS AND SERVICES. Look for “Cloud Build API” at the search bar. Click Enable.

9. Enable the Cloud Run API : Navigate to APIs & services tab on the menu. Click ENABLE APIS AND SERVICES. Look for “Cloud Run API” at the search bar. Click Enable.

10. Enable the Artifact Registry API: Navigate to APIs & services tab on the menu. Click ENABLE APIS AND SERVICES. Look for “Artifact Registry API” at the search bar. Click Enable.

11. Create a Docker repository in Artifact Registry (in the same region as Cloud Run), for example named lithops, or set artifact_registry_repository in config to match your repository name. From a shell with gcloud and the correct project:

gcloud artifacts repositories create lithops \
  --repository-format=docker \
  --location=<REGION_NAME> \
  --description="Lithops Cloud Run runtimes"

Grant the service account Artifact Registry Writer (or Artifact Registry Create-on-push Writer if you prefer) so it can push images.

Example command:

gcloud artifacts repositories add-iam-policy-binding gcf-artifacts \
  --location=us-east1 \
  --project=lithops-dev \
  --member="serviceAccount:lithops-executor@lithops-dev.iam.gserviceaccount.com" \
  --role="roles/artifactregistry.writer"

Replace gcf-artifacts, us-east1, lithops-dev, and the service account email with your own values.

Configuration

1. Edit your Lithops config and add the following keys:

    lithops:
        backend: gcp_cloudrun

    gcp:
        region : <REGION_NAME>
        credentials_path : <FULL_PATH_TO_CREDENTIALS_JSON>

Summary of configuration keys for Google Cloud

Google Cloud Platform:

Group	Key	Default	Mandatory	Additional info
gcp	region		yes	Region name of the GCP services (e.g. us-east1)
gcp	credentials_path		yes	Absolute path of your JSON key file downloaded in step 7 (e.g. /home/myuser/lithops-invoker1234567890.json). Alternatively you can set GOOGLE_APPLICATION_CREDENTIALS environment variable. If not provided it will try to load the default credentials from the environment

Google Cloud Run

Group	Key	Default	Mandatory	Additional info
gcp_cloudrun	region		no	Region name (e.g. us-east1). Lithops will use the region set under the gcp section if it is not set here
gcp_cloudrun	min_workers	0	no	Minimum number of workers of a given runtime to keep in warm status
gcp_cloudrun	max_workers	1000	no	Maximum number of workers to scale a given runtime
gcp_cloudrun	worker_processes	1	no	Number of Lithops processes within a given worker. This can be used to parallelize function activations within a worker. It is recommended to set this value to the same number of CPUs as the container.
gcp_cloudrun	runtime		no	Container image name
gcp_cloudrun	runtime_cpu	0.25	no	CPU limit. Default 0.25vCPU
gcp_cloudrun	runtime_memory	256	no	Memory limit in MB. Default 256Mi
gcp_cloudrun	runtime_timeout	300	no	Runtime timeout in seconds. Default 5 minutes
gcp_cloudrun	trigger	https	no	Currently it supports ‘https’ trigger
gcp_cloudrun	invoke_pool_threads	100	no	Number of concurrent threads used for invocation
gcp_cloudrun	runtime_include_function	False	no	If set to true, Lithops will automatically build a new runtime, including the function’s code, instead of transferring it through the storage backend at invocation time. This is useful when the function’s code size is large (on the order of tens of MB) and the code does not change frequently
gcp_cloudrun	docker_server	pkg.dev	no	Marker for Artifact Registry default image names (REGION-docker.pkg.dev/PROJECT/REPOSITORY/IMAGE).
gcp_cloudrun	artifact_registry_repository	lithops	no	Docker repository name in Artifact Registry (must exist under your project in the configured region).

Test Lithops

Once you have your compute and storage backends configured, you can run a Hello World function with:

lithops hello -b gcp_cloudrun -s gcp_storage

Viewing the execution logs

You can view the function executions logs in your local machine using the lithops client:

lithops logs poll