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Google Artifact Registry
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Google Artifact Registry

Leverage Google’s Container Registry in ZenML Pipelines with Google Artifact Registry Integration

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Leverage Google’s Container Registry in ZenML Pipelines with Google Artifact Registry Integration

Seamlessly integrate the Google Artifact Registry with ZenML. Store, manage, and deploy containerized components within your ML pipelines, leveraging the scalability and reliability of Google Cloud Platform without exposing the complexity to your data scientists.

Features with ZenML

  • Seamless integration of Google Artifact Registry as a container registry in ZenML stacks
  • Effortless storage and retrieval of containerized pipeline environments
  • Scalable and reliable container management backed by Google Cloud infrastructure
  • Streamlined deployment of ML pipelines with versioned container images

Google Artifact Registry integration screenshot

Main Features

  • Centralized container image storage and management
  • No local credentials necessary
  • Secure access control and permissions management
  • Automatic versioning and rebuilding of step docker images where necessary

How to use ZenML with Google Artifact Registry


# Step 1: Install all required gcp packaes 
zenml integration install gcp

# Step 2: Register the GCP container registry
zenml container-registry register gcp_registry \
  --flavor=gcp \
  --uri="<YOUR URI>"

# Step 3: Update your stack to use the selected container registry
# Make sure your stack already contains a remote artifact store
# and orchestrator
zenml stack update -c gcp_registry

# Step 4: Set up authentication (choose one method)
zenml container-registry connect gcp_registry -i

# Step 5: Validate that your stack has a remote orchestrator in it
# Not all orchestrators require a built image, so in order to use the
# container registry you would need a remote orchestrator/step operator
# used in your stack
zenml stack describe

from zenml import pipeline, step

@step
def example_step():
    print("This step will be containerized and pushed to GCP container registry")

@pipeline
def my_pipeline():
    example_step()

if __name__ == "__main__":
    my_pipeline()
    

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