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Understanding stacks

Learning how to switch the infrastructure backend of your code.
Now that we have ZenML deployed, we can take the next steps in making sure that our machine learning workflows are production-ready. As you were running your first pipelines, you might have already noticed the term stack in the logs and on the dashboard.
A stack is the configuration of tools and infrastructure that your pipelines can run on. When you run ZenML code without configuring a stack, the pipeline will run on the so-called default stack.
ZenML is the translation layer that allows your code to run on any of your stacks

Separation of code from configuration and infrastructure

As visualized in the diagram above, there are two separate domains that are connected through ZenML. The left side shows the code domain. The user's Python code is translated into a ZenML pipeline. On the right side, you can see the infrastructure domain, in this case, an instance of the default stack. By separating these two domains, it is easy to switch the environment that the pipeline runs on without making any changes in the code. It also allows domain experts to write code/configure infrastructure without worrying about the other domain.

The default stack

You can explore all your stacks in the dashboard. When you click on a specific one you can see its configuration and all the pipeline runs that were executed using this stack.
The default stack on the Dashboard
zenml stack describe lets you find out details about your active stack:
Stack Configuration
┃ ARTIFACT_STORE │ default ┃
┃ ORCHESTRATOR │ default ┃
'default' stack (ACTIVE)
Stack 'default' with id '...' is owned by user default and is 'private'.
zenml stack list lets you see all stacks that are registered in your zenml deployment.
┃ 👉 │ default │ ... │ ➖ │ default │ default │ default ┃
As you can see a stack can be active on your client. This simply means that any pipeline you run will be using the active stack as its environment.

Components of a stack

As you can see in the section above, a stack consists of multiple components. All stacks have at minimum an orchestrator and an artifact store.


The orchestrator is responsible for executing the pipeline code. In the simplest case, this will be a simple Python thread on your machine. Let's explore this default orchestrator.
Default orchestrator in the dashboard.
zenml orchestrator list lets you see all orchestrators that are registered in your zenml deployment.
┃ 👉 │ default │ ... │ local │ ➖ │ default ┃

Artifact store

The artifact store is responsible for persisting the step outputs. As we learned in the previous section, the step outputs are not passed along in memory, rather the outputs of each step are stored in the artifact store and then loaded from there when the next step needs them. By default this will also be on your own machine:
Default artifact store in the dashboard.
zenml artifact-store list lets you see all artifact stores that are registered in your zenml deployment.
┃ 👉 │ default │ ... │ local │ ➖ │ default ┃

Other stack components

There are many more components that you can add to your stacks, like experiment trackers, model deployers, and more. You can see all supported stack component types in a single table view here
Perhaps the most important stack component after the orchestrator and the artifact store is the container registry. A container registry stores all your containerized images, which hold all your code and the environment needed to execute them. We will learn more about them in the next section!

Registering a stack

Just to illustrate how to interact with stacks, let's create an alternate local stack. We start by first creating a local artifact store.

Create an artifact store

Creating an Artifact Store in the dashboard.
zenml artifact-store register my_artifact_store --flavor=local
Let's understand the individual parts of this command:
  • artifact-store : This describes the top-level group, to find other stack components simply run zenml --help
  • register : Here we want to register a new component, instead, we could also update , delete and more zenml artifact-store --help will give you all possibilities
  • my_artifact_store : This is the unique name that the stack component will have.
  • --flavor=local: A flavor is a possible implementation for a stack component. So in the case of an artifact store, this could be an s3-bucket or a local filesystem. You can find out all possibilities with zenml artifact-store flavor --list
This will be the output that you can expect from the command above.
Using the default local database.
Running with active workspace: 'default' (global)
Running with active stack: 'default' (global)
Successfully registered artifact_store `my_artifact_store`.bash
To see the new artifact store that you just registered, just run:
zenml artifact-store describe my_artifact_store

Create a local stack

With the artifact store created, we can now create a new stack with this artifact store.
Register a new stack.
zenml stack register a_new_local_stack -o default -a my_artifact_store
  • stack : 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
┃ 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'.

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
Above doesn't work? Here is an alternative
The starter template is the same as the ZenML quickstart. You can clone it like so:
git clone [email protected]:zenml-io/zenml.git
cd examples/quickstart
pip install -r requirements.txt
zenml init
To run a pipeline using the new stack:
  1. 1.
    Set the stack as active on your client
    zenml stack set a_new_local_stack
  2. 2.
    Run your pipeline code:
    python run.py --training-pipeline
Keep this code handy as we'll be using it in the next chapters!
ZenML Scarf