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.
# 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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