 a_new_local_stack -o default -a my_artifact_storestack : This is the CLI group that enables interactions with the stacks

register: Here we want to register a new stack. Explore other operations withzenml stack --help.

a_new_local_stack : This is the unique name that the stack will have.

--orchestrator or -o are used to specify which orchestrator to use for the stack

--artifact-store or -a are used to specify which artifact store to use for the stack

The output for the command should look something like this:

Using the default local database.

Running with active workspace: 'default' (repository)

Stack 'a_new_local_stack' successfully registered!

You can inspect the stack with the following command:

zenml stack describe a_new_local_stack

Which will give you an output like this:

Stack Configuration

┏━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━┓

┃ COMPONENT_TYPE │ COMPONENT_NAME    ┃

┠────────────────┼───────────────────┨

┃ ORCHESTRATOR   │ default           ┃

┠────────────────┼───────────────────┨

┃ ARTIFACT_STORE │ my_artifact_store ┃

┗━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━┛

'a_new_local_stack' stack

Stack 'a_new_local_stack' with id '...' is owned by user default and is 'private'.

Switch stacks with our VS Code extension

If you are using our VS Code extension, you can easily view and switch your stacks by opening the sidebar (click on the ZenML icon). You can then click on the stack you want to switch to as well as view the stack components it's made up of.

Run a pipeline on the new local stack

Let's use the pipeline in our starter project from the previous guide to see it in action.

If you have not already, clone the starter template:

pip install "zenml[templates,server]" notebook

zenml integration install sklearn -y

mkdir zenml_starter

cd zenml_starter

zenml init --template starter --template-with-defaults

# Just in case, we install the requirements again

pip install -r requirements.txt

The starter template is the same as the ZenML quickstart. You can clone it like so: