Kubeflow (KFP) Pipeline

A Kubeflow (KFP) Pipeline is a workflow pipeline for a Kubeflow platforem.

• Example(s):
  • ...
• Counter-Example(s):
  • a TFX Pipeline.
  • a Airflow Workflow.
• See: ML Workflow, KFP Pipeline DSL, Python Decorator.

---

---

References

2023

• chat
  • Q: What is a KFP pipeline?
  • A: Kubeflow Pipelines (KFP) is an open-source project that helps users create and manage end-to-end machine learning (ML) workflows on Kubernetes. The core component of KFP is the pipeline, which is a collection of steps that define an ML workflow. Each step in a pipeline represents a containerized operation, such as data preprocessing, training, and serving.

    A pipeline is defined using the KFP pipeline DSL (domain-specific language), which is based on Python and provides a high-level, easy-to-use interface for specifying the steps and dependencies of a pipeline. The DSL abstracts away the underlying Kubernetes constructs, such as pods and services, and handles the deployment and scaling of the pipeline on a Kubernetes cluster.

    Once a pipeline is defined, it can be run and monitored using the KFP UI, a web-based user interface that provides visibility into the pipeline's progress and performance. You can also use the KFP SDK to programmatically interact with the pipeline, for example, to trigger runs, retrieve run artifacts, or access metadata.

    In summary, KFP is a powerful tool for building and deploying ML workflows on Kubernetes, that enables data scientist, data engineers to focus on ML related functionality, rather than infrastructure, scaling, and monitoring.

2023

• chat
  • Q: Provide an example KFP pipeline DSL.
  • A: Sure, here's an example of a simple pipeline that trains a model using the popular MNIST dataset.

import kfp
from kfp import dsl

@dsl.pipeline(
   name='MNIST pipeline',
   description='A pipeline to train a model on the MNIST dataset'
)
def mnist_pipeline():
   # Define a step that downloads the MNIST dataset
   download_data = dsl.ContainerOp(
       name='Download MNIST data',
       image='alpine',
       command=['sh', '-c'],
       arguments=['wget -O /data/mnist.npz http://localhost/mnist.npz']
   )

   # Define a step that trains a model
   train = dsl.ContainerOp(
       name='Train',
       image='tensorflow/tensorflow:2.4.0',
       command=['python', '-m', 'trainer.task'],
       arguments=[
           '--data-dir', download_data.output,
           '--model-dir', '/output'
      ]
   )

   # Define the pipeline dependencies
   train.after(download_data)

if __name__ == '__main__':
   kfp.compiler.Compiler().compile(mnist_pipeline, 'mnist-pipeline.yaml')

• • This pipeline is defined using the dsl.pipeline decorator, which creates a Python function that represents the pipeline. Inside the function, you define the steps of the pipeline using the dsl.ContainerOp class. Each step is defined as a container operation that runs a specific command in a specified container image.

    In this example, the pipeline has two steps: "Download MNIST data" and "Train". The first step uses the wget command to download the MNIST dataset to a local directory. The second step uses the python command to run a trainer script that trains a model on the downloaded data and writes the output to /output directory.

    The pipeline has also a dependency set, train.after(download_data) this defines that "Train" step should run only after "Download MNIST data" step has successfully run, if the first one fail, the second one will never execute.

    Finally, the pipeline is compiled into a YAML file using the kfp.compiler.Compiler().compile method. The resulting YAML file can be used to create and run the pipeline on a Kubernetes cluster.

    This is just a simple example, of course depending on the complexity of the pipeline and the operations it is performing, it can get much more complex and sophisticated, but it serves as an example of how to define a pipeline in KFP.

2023

• chat
  • Q: what is a Python decorator?
  • ... Decorators in KFP pipelines are used to define pipeline structure, components, inputs, outputs and dependencies between them, it's a way to specify the pipeline behavior and structure without modifying the function that creates it, making the pipeline definition more readable and understandable. ...

---

---