Compile a Pipeline

Compile pipelines and components to YAML

To submit a pipeline for execution, you must compile it to YAML with the KFP SDK compiler:

from kfp import dsl
from kfp import compiler

def comp(message: str) -> str:
    return message

def my_pipeline(message: str) -> str:
    """My ML pipeline."""
    return comp(message=message).output

compiler.Compiler().compile(my_pipeline, package_path='pipeline.yaml')

In this example, the compiler creates a file called pipeline.yaml, which contains a hermetic representation of your pipeline. The output is called intermediate representation (IR) YAML. You can view an example of IR YAML on GitHub. The contents of the file is the serialized PipelineSpec protocol buffer message and is not intended to be human-readable.

You can find human-readable information about the pipeline in the comments at the top of the compiled YAML:

# Name: my-pipeline
# Description: My ML pipeline.
# Inputs:
#    message: str
# Outputs:
#    Output: str

You can also compile components, as opposed to pipelines, to IR YAML:

def comp(message: str) -> str:
    return message

compiler.Compiler().compile(comp, package_path='component.yaml')

Compiler arguments

The Compiler.compile method accepts the following arguments:

Name Type Description
pipeline_func function Required
Pipeline function constructed with the @dsl.pipeline or component constructed with the @dsl.component decorator.
package_path string Required
Output YAML file path. For example, ~/my_pipeline.yaml or ~/my_component.yaml.
pipeline_name string Optional
If specified, sets the name of the pipeline template in the field in the compiled IR YAML output. Overrides the name of the pipeline or component specified by the name parameter in the @dsl.pipeline decorator.
pipeline_parameters Dict[str, Any] Optional
Map of parameter names to argument values. This lets you provide default values for pipeline or component parameters. You can override these default values during pipeline submission.
type_check bool Optional
Indicates whether static type checking is enabled during compilation.

Type checking

By default, the DSL compiler statically type checks your pipeline to ensure type consistency between components that pass data between one another. Static type checking helps identify component I/O inconsistencies without having to run the pipeline, shortening development iterations.

Specifically, the type checker checks for type equality between the type of data a component input expects and the type of the data provided. See Data Types for more information about KFP data types.

For example, for parameters, a list input may only be passed to parameters with a typing.List annotation. Similarly, a float may only be passed to parameters with a float annotation.

Input data types and annotations must also match for artifacts, with one exception: the Artifact type is compatible with all other artifact types. In this sense, the Artifact type is both the default artifact type and an artifact “any” type.

As described in the following section, you can disable type checking.


The IR YAML is an intermediate representation of a compiled pipeline or component. It is an instance of the PipelineSpec protocol buffer message type, which is a platform-agnostic pipeline representation protocol. It is considered an intermediate representation because the KFP backend compiles PipelineSpec to Argo Workflow YAML as the final pipeline definition for execution.

Unlike the v1 component YAML, the IR YAML is not intended to be written directly.

While IR YAML is not intended to be easily human readable, you can still inspect it if you know a bit about its contents:

Section Description Example
components This section is a map of the names of all components used in the pipeline to ComponentSpec. ComponentSpec defines the interface, including inputs and outputs, of a component.
For primitive components, ComponentSpec contains a reference to the executor containing the component implementation.

For pipelines used as components, ComponentSpec contains a DagSpec instance, which includes references to the underlying primitive components.
View on Github
deployment_spec This section contains a map of executor name to ExecutorSpec. ExecutorSpec contains the implementation for a primitive component. View on Github
root This section defines the steps of the outermost pipeline definition, also called the pipeline root definition. The root definition is the workflow executed when you submit the IR YAML. It is an instance of ComponentSpec. View on Github
pipeline_info This section contains pipeline metadata, including the field. This field contains the name of your pipeline template. When you upload your pipeline, a pipeline context name is created based on this template name. The pipeline context lets the backend and the dashboard associate artifacts and executions from pipeline runs using the pipeline template. You can use a pipeline context to determine the best model by comparing metrics and artifacts from multiple pipeline runs based on the same training pipeline. View on Github
sdk_version This section records the version of the KFP SDK used to compile the pipeline. View on Github
schema_version This section records the version of the PipelineSpec schema used for the IR YAML. View on Github
default_pipeline_root This section records the remote storage root path, such as a MinIO URI or Google Cloud Storage URI, where the pipeline output is written. View on Github


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