Autogenerate a template yaml file

To help you figure out what you can put in your configuration file, simply autogenerate a template.

If you want to generate a template yaml file of your specific pipeline, you can do so by using the .write_run_configuration_template() method. This will generate a yaml file with all options commented out. This way you can pick and choose the settings that are relevant to you.

from zenml import pipeline

...

@pipeline(enable_cache=True) # set cache behavior at step level

def simple_ml_pipeline(parameter: int):

dataset = load_data(parameter=parameter)

train_model(dataset)

simple_ml_pipeline.write_run_configuration_template(path="<Insert_path_here>")

build: Union[PipelineBuildBase, UUID, NoneType]

enable_artifact_metadata: Optional[bool]

enable_artifact_visualization: Optional[bool]

enable_cache: Optional[bool]

enable_step_logs: Optional[bool]

extra: Mapping[str, Any]

model:

audience: Optional[str]

description: Optional[str]

ethics: Optional[str]

license: Optional[str]

limitations: Optional[str]

name: str

save_models_to_registry: bool

suppress_class_validation_warnings: bool

tags: Optional[List[str]]

trade_offs: Optional[str]

use_cases: Optional[str]

version: Union[ModelStages, int, str, NoneType]

was_created_in_this_run: bool

parameters: Optional[Mapping[str, Any]]

run_name: Optional[str]

schedule:

catchup: bool

cron_expression: Optional[str]

end_time: Optional[datetime]

interval_second: Optional[timedelta]

name: Optional[str]

run_once_start_time: Optional[datetime]

start_time: Optional[datetime]

settings:

docker:

apt_packages: List[str]

build_context_root: Optional[str]

build_options: Mapping[str, Any]

copy_files: bool

copy_global_config: bool

dockerfile: Optional[str]

dockerignore: Optional[str]

environment: Mapping[str, Any]

install_stack_requirements: bool

parent_image: Optional[str]

python_package_installer: PythonPackageInstaller