onfiguration, if specified overrides for this stepenable_artifact_metadata: True

enable_artifact_visualization: True

enable_cache: False

enable_step_logs: True

# Same as pipeline level configuration, if specified overrides for this step

extra: {}

# Same as pipeline level configuration, if specified overrides for this step

model: {}

# Same as pipeline level configuration, if specified overrides for this step

settings:

docker: {}

resources: {}

# Stack component specific settings

step_operator.sagemaker:

estimator_args:

instance_type: m7g.medium

Deep-dive

enable_XXX parameters

These are boolean flags for various configurations:

enable_artifact_metadata: Whether to associate metadata with artifacts or not.

enable_artifact_visualization: Whether to attach visualizations of artifacts.

enable_cache: Utilize caching or not.

enable_step_logs: Enable tracking step logs.

enable_artifact_metadata: True

enable_artifact_visualization: True

enable_cache: True

enable_step_logs: True

build ID

The UUID of the build to use for this pipeline. If specified, Docker image building is skipped for remote orchestrators, and the Docker image specified in this build is used.

build: <INSERT-BUILD-ID-HERE>

Configuring the model

Specifies the ZenML Model to use for this pipeline.

model:

name: "ModelName"

version: "production"

description: An example model

tags: ["classifier"]

Pipeline and step parameters

A dictionary of JSON-serializable parameters specified at the pipeline or step level. For example:

parameters:

gamma: 0.01

steps:

trainer:

parameters:

gamma: 0.001

Corresponds to:

from zenml import step, pipeline

@step

def trainer(gamma: float):

# Use gamma as normal

print(gamma)

@pipeline

def my_pipeline(gamma: float):

# use gamma or pass it into the step

print(0.01)

trainer(gamma=gamma)