nity and ask!

Running a pipeline on a cloud stackNow that we have our orchestrator and container registry registered, we can register a new stack, just like we did in the previous chapter:

zenml stack register minimal_cloud_stack -o skypilot_orchestrator -a cloud_artifact_store -c cloud_container_registry

Now, using the code from the previous chapter, we can run a training pipeline. First, set the minimal cloud stack active:

zenml stack set minimal_cloud_stack

and then, run the training pipeline:

python run.py --training-pipeline

You will notice this time your pipeline behaves differently. After it has built the Docker image with all your code, it will push that image, and run a VM on the cloud. Here is where your pipeline will execute, and the logs will be streamed back to you. So with a few commands, we were able to ship our entire code to the cloud!

Curious to see what other stacks you can create? The Component Guide has an exhaustive list of various artifact stores, container registries, and orchestrators that are integrated with ZenML. Try playing around with more stack components to see how easy it is to switch between MLOps stacks with ZenML.

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NextConfigure your pipeline to add compute

Last updated 14 days ago