A pipeline comprises one or more nodes that are (in many cases) connected with each other to define execution dependencies. A node is an instance of a configurable component that commonly only implements a single unit of work to make it reusable. A unit of work can represent any task, such as loading data, pre-processing data, analyzing data, training a machine learning model, deploying a model for serving, querying a service, or sending an email.
Note though that multiple components might implement the “same” task. For example, one component might load data from a SQL database, whereas another component might download data from S3 storage. Conceptually both components load data, but how they load it is entirely different.
Elyra supports two types of components: generic components and custom components. A pipeline that utilizes only generic components is called a generic pipeline, whereas a pipeline that utilizes generic components and/or custom components is referred to as runtime-specific pipeline.
Pipelines are assembled using the Visual Pipeline Editor. The editor includes a palette, the canvas, and a properties panel, shown on the left, in the center, and the right, respectively.
Please review the Best practices for file-based pipeline nodes topic in the User Guide if your pipelines include generic components.
Elyra pipelines support three runtime platforms:
A generic pipeline comprises only of nodes that are implemented using generic components. This Elyra release includes three generic components that allow for execution of Jupyter notebooks, Python scripts, and R scripts.
Generic pipelines are portable, meaning they can run locally in JupyterLab, or remotely on Kubeflow Pipelines or Apache Airflow.
A runtime-specific pipeline is permanently associated with a runtime platform, such as Kubeflow Pipelines or Apache Airflow. A runtime-specific pipeline may include nodes that are implemented using generic components or custom components for that runtime.
For illustrative purposes the Elyra component registry includes a couple example custom components. You can add your own components as outlined in Managing custom components.
Note that it is not possible to convert a generic pipeline to a runtime-specific pipeline or a runtime-specific pipeline from one type to another.
Creating pipelines using the Visual Pipeline Editor¶
The tutorials provide comprehensive step-by-step instructions for creating and running pipelines. To create a pipeline using the editor:
Open the JupyterLab Launcher and select the desired pipeline editor type (Generic, Kubeflow Pipelines, or Apache Airflow).
Expand the properties panel and define the pipeline properties. Pipeline properties include a description and default values for node properties. (Support for pipeline properties varies by release.)
Drag and drop components from the palette onto the canvas or double click on a palette entry.
You can also drag and drop Jupyter notebooks, Python scripts, or R scripts from the JupyterLab File Browser onto the canvas.
Define the dependencies between nodes by connecting them, essentially creating an execution graph.
Define the runtime properties for each node. Highlight a node, right click, and select
Open Properties. Runtime properties configure a component and govern its execution behavior.
Runtime properties are component specific. For generic components (Jupyter notebook, Python script, and R script) the properties are defined as follows:
- Required. The container image you want to use to run the notebook or script.
CPU, GPU, and RAM
- Optional. Resources that the notebook or script requires.
- Optional. A list of files to be passed from the local working environment into each respective step of the pipeline. Files should be in the same directory (or subdirectory thereof) as the file it is associated with. Specify one file, directory, or expression per line. Supported patterns are
- Optional. A list of environment variables to be set inside in the container. Specify one variable/value pair per line, separated by
- Optional. A list of files generated by the notebook inside the image to be passed as inputs to the next step of the pipeline. Specify one file, directory, or expression per line. Supported patterns are
Associate each node with a comment to document its purpose.
Save the pipeline file.
Note: You can rename the pipeline file in the JupyterLab File Browser.
Pipelines can be run from the Visual Pipeline Editor and the
elyra-pipeline command line interface. Before you can run a pipeline on Kubeflow Pipelines or Apache Airflow you must create a runtime configuration. A runtime configuration contains information about the target environment, such as server URL and credentials.
Running a pipeline from the Visual Pipeline Editor
To run a pipeline from the Visual Pipeline Editor:
Run Pipelinein the editor’s tool bar.
For generic pipelines select a runtime platform (local, Kubeflow Pipelines, Apache Airflow) and a runtime configuration for that platform. For runtime-specific pipelines select a runtime configuration.
Elyra does not include a pipeline run monitoring interface for pipelines:
- For local/JupyterLab execution check the console output.
- For Kubeflow Pipelines open the Central Dashboard link.
- For Apache Airflow open the web GUI link.
The pipeline run output artifacts are stored in the following locations:
- For local/JupyterLab execution all artifacts are stored in the local file system.
- For Kubeflow Pipelines and Apache Airflow output artifacts for generic components are stored in the runtime configuration’s designated object storage bucket.
Running a pipeline from the command line interface
elyra-pipeline command line interface
provides an informative command:
describe and two execution commands:
elyra-pipeline describe command to display pipeline details such as name, version, etc.
$ elyra-pipeline describe elyra-pipelines/a-notebook.pipeline
elyra-pipeline run command to run a generic pipeline in your JupyterLab environment:
$ elyra-pipeline run elyra-pipelines/a-notebook.pipeline
elyra-pipeline submit command to run a generic or runtime-specific pipeline remotely on Kubeflow Pipelines or Apache Airflow, specifying a compatible runtime configuration as parameter:
$ elyra-pipeline submit elyra-pipelines/a-kubeflow.pipeline \ --runtime-config kfp-shared-tekton
Note: Refer to the Managing runtime configurations using the Elyra CLI topic in the User Guide for details on how to list and manage runtime configurations.
Exporting a pipeline¶
When you export a pipeline Elyra only prepares it for later execution, but does not upload it to the Kubeflow Pipelines or Apache Airflow server. Export performs two tasks. It packages dependencies for generic components and uploads them to cloud storage, and it generates pipeline code for the target runtime.
Before you can export a pipeline on Kubeflow Pipelines or Apache Airflow you must create a runtime configuration. A runtime configuration contains information about the target environment, such as server URL and credentials.
To export a pipeline from the Visual Pipeline Editor:
Export Pipelinein the editor’s tool bar.
For generic pipelines select a runtime platform (local, Kubeflow Pipelines, or Apache Airflow) and a runtime configuration for that platform. For runtime-specific pipelines select a runtime configuration.
Select an export format.
Import the exported pipeline file using the Kubeflow Central Dashboard or add it to the Git repository that Apache Airflow is monitoring.