ents locally!

Running a pipeline on a cloud stackNow that we have our remote artifact store registered, we can register a new stack with it, just like we did in the previous chapter:

zenml stack register local_with_remote_storage -o default -a cloud_artifact_store

Now, using the code from the previous chapter, we run a training pipeline:

Set our local_with_remote_storage stack active:

zenml stack set local_with_remote_storage

Let us continue with the example from the previous page and run the training pipeline:

python run.py --training-pipeline

When you run that pipeline, ZenML will automatically store the artifacts in the specified remote storage, ensuring that they are preserved and accessible for future runs and by your team members. You can ask your colleagues to connect to the same ZenML server, and you will notice that if they run the same pipeline, the pipeline would be partially cached, even if they have not run the pipeline themselves before.

You can list your artifact versions as follows:

# This will give you the artifacts from the last 15 minutes

zenml artifact version list --created="gte:$(date -d '15 minutes ago' '+%Y-%m-%d %H:%M:%S')"

The ZenML Cloud features an Artifact Control Plane to visualize artifact versions:

You will notice above that some artifacts are stored locally, while others are stored in a remote storage location.

By connecting remote storage, you're taking a significant step towards building a collaborative and scalable MLOps workflow. Your artifacts are no longer tied to a single machine but are now part of a cloud-based ecosystem, ready to be shared and built upon.

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Last updated 15 days ago