Welcome to the ZenML Api Docs
Pipelines
A ZenML pipeline consists of tasks that execute in order and yield artifacts.
The artifacts are automatically stored within the artifact store and metadata
is tracked by ZenML. Each individual task within a pipeline is known as a
step. The standard pipelines within ZenML are designed to have easy interfaces
to add pre-decided steps, with the order also pre-decided. Other sorts of
pipelines can be created as well from scratch, building on the BasePipeline class.
Pipelines can be written as simple functions. They are created by using decorators appropriate to the specific use case you have. The moment it is run, a pipeline is compiled and passed directly to the orchestrator.
Materializers
Initialization of ZenML materializers.
Materializers are used to convert a ZenML artifact into a specific format. They
are most often used to handle the input or output of ZenML steps, and can be
extended by building on the BaseMaterializer class.
Model Deployers
Model deployers are stack components responsible for online model serving.
Online serving is the process of hosting and loading machine-learning models as part of a managed web service and providing access to the models through an API endpoint like HTTP or GRPC. Once deployed, you can send inference requests to the model through the web service's API and receive fast, low-latency responses.
Add a model deployer to your ZenML stack to be able to implement continuous model deployment pipelines that train models and continuously deploy them to a model prediction web service.
When present in a stack, the model deployer also acts as a registry for models that are served with ZenML. You can use the model deployer to list all models that are currently deployed for online inference or filtered according to a particular pipeline run or step, or to suspend, resume or delete an external model server managed through ZenML.
Steps
Initializer for ZenML steps.
A step is a single piece or stage of a ZenML pipeline. Think of each step as being one of the nodes of a Directed Acyclic Graph (or DAG). Steps are responsible for one aspect of processing or interacting with the data / artifacts in the pipeline.
ZenML currently implements a basic step interface, but there will be other more customized interfaces (layered in a hierarchy) for specialized implementations. Conceptually, a Step is a discrete and independent part of a pipeline that is responsible for one particular aspect of data manipulation inside a ZenML pipeline.
Steps can be subclassed from the BaseStep class, or used via our @step
decorator.
Alerter
Alerters allow you to send alerts from within your pipeline.
This is useful to immediately get notified when failures happen, and also for general monitoring / reporting.
Artifact Stores
ZenML's artifact-store stores artifacts in a file system.
In ZenML, the inputs and outputs which go through any step is treated as an
artifact and as its name suggests, an ArtifactStore is a place where these
artifacts get stored.
Out of the box, ZenML comes with the BaseArtifactStore and
LocalArtifactStore implementations. While the BaseArtifactStore establishes
an interface for people who want to extend it to their needs, the
LocalArtifactStore is a simple implementation for a local setup.
Moreover, additional artifact stores can be found in specific integrations
modules, such as the GCPArtifactStore in the gcp integration and the
AzureArtifactStore in the azure integration.
Constants
Config
The config module contains classes and functions that manage user-specific configuration.
ZenML's configuration is stored in a file called
config.yaml, located on the user's directory for configuration files.
(The exact location differs from operating system to operating system.)
The GlobalConfiguration class is the main class in this module. It provides
a Pydantic configuration object that is used to store and retrieve
configuration. This GlobalConfiguration object handles the serialization and
deserialization of the configuration options that are stored in the file in
order to persist the configuration across sessions.
Zen Server
ZenML Server Implementation.
The ZenML Server is a centralized service meant for use in a collaborative setting in which stacks, stack components, flavors, pipeline and pipeline runs can be shared over the network with other users.
You can use the zenml server up command to spin up ZenML server instances
that are either running locally as daemon processes or docker containers, or
to deploy a ZenML server remotely on a managed cloud platform. The other CLI
commands in the same zenml server group can be used to manage the server
instances deployed from your local machine.
To connect the local ZenML client to one of the managed ZenML servers, call
zenml server connect with the name of the server you want to connect to.
Data Validators
Services
Initialization of the ZenML services module.
A service is a process or set of processes that outlive a pipeline run.
Environment
Models
Post Execution
Initialization for the post-execution module.
After executing a pipeline, the user needs to be able to fetch it from history and perform certain tasks. The post_execution submodule provides a set of interfaces with which the user can interact with artifacts, the pipeline, steps, and the post-run pipeline object.
Secrets Managers
Logger
Utils
Initialization of the utils module.
The utils module contains utility functions handling analytics, reading and
writing YAML data as well as other general purpose functions.
Exceptions
Secret
Initialization of the ZenML Secret module.
A ZenML Secret is a grouping of key-value pairs. These are accessed and administered via the ZenML Secret Manager (a stack component).
Secrets are distinguished by having different schemas. An AWS SecretSchema, for
example, has key-value pairs for AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY
as well as an optional AWS_SESSION_TOKEN. If you don't specify a schema at the
point of registration, ZenML will set the schema as ArbitrarySecretSchema, a
kind of default schema where things that aren't attached to a grouping can be
stored.
Orchestrators
Initialization for ZenML orchestrators.
An orchestrator is a special kind of backend that manages the running of each step of the pipeline. Orchestrators administer the actual pipeline runs. You can think of it as the 'root' of any pipeline job that you run during your experimentation.
ZenML supports a local orchestrator out of the box which allows you to run your pipelines in a local environment. We also support using Apache Airflow as the orchestrator to handle the steps of your pipeline.
Annotators
Console
Step Operators
Step operators allow you to run steps on custom infrastructure.
While an orchestrator defines how and where your entire pipeline runs, a step operator defines how and where an individual step runs. This can be useful in a variety of scenarios. An example could be if one step within a pipeline should run on a separate environment equipped with a GPU (like a trainer step).
Client
Container Registries
Initialization for ZenML's container registries module.
A container registry is a store for (Docker) containers. A ZenML workflow involving a container registry would automatically containerize your code to be transported across stacks running remotely. As part of the deployment to the cluster, the ZenML base image would be downloaded (from a cloud container registry) and used as the basis for the deployed 'run'.
For instance, when you are running a local container-based stack, you would therefore have a local container registry which stores the container images you create that bundle up your pipeline code. You could also use a remote container registry like the Elastic Container Registry at AWS in a more production setting.
Repository
Io
The io module handles file operations for the ZenML package.
It offers a standard interface for reading, writing and manipulating files and
directories. It is heavily influenced and inspired by the io module of tfx.
Visualizers
Initialization of the visualizers module.
The visualizers module offers a way of constructing and displaying
visualizations of steps and pipeline results. The BaseVisualizer class is at
the root of all the other visualizers, including options to view the results of
pipeline runs, steps and pipelines themselves.
Artifacts
Artifacts are the data that power your experimentation and model training.
It is actually steps that produce artifacts, which are then stored in the artifact store. Artifacts are written in the signature of a step like so:
def my_step(first_artifact: int, second_artifact: torch.nn.Module -> int:
# first_artifact is an integer
# second_artifact is a torch.nn.Module
return 1
Artifacts can be serialized and deserialized (i.e. written and read from the
Artifact Store) in various ways like TFRecords or saved model pickles,
depending on what the step produces.The serialization and deserialization logic
of artifacts is defined by the appropriate Materializer.
Stack
Initialization of the ZenML Stack.
The stack is essentially all the configuration for the infrastructure of your MLOps platform.
A stack is made up of multiple components. Some examples are:
- An Artifact Store
- An Orchestrator
- A Step Operator (Optional)
- A Container Registry (Optional)
Recipes
Zen Stores
Experiment Trackers
Experiment trackers let you track your ML experiments.
They log the parameters used and allow you to compare between runs. In the ZenML world, every pipeline run is considered an experiment, and ZenML facilitates the storage of experiment results through ExperimentTracker stack components. This establishes a clear link between pipeline runs and experiments.
Feature Stores
A feature store enables an offline and online serving of feature data.
Feature stores allow data teams to serve data via an offline store and an online low-latency store where data is kept in sync between the two. It also offers a centralized registry where features (and feature schemas) are stored for use within a team or wider organization.
As a data scientist working on training your model, your requirements for how you access your batch / 'offline' data will almost certainly be different from how you access that data as part of a real-time or online inference setting. Feast solves the problem of developing train-serve skew where those two sources of data diverge from each other.