Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning workflows based on Docker containers within the Kubeflow project.
Use Kubeflow Pipelines to compose a multi-step workflow (pipeline) as a graph of containerized tasks using Python code and/or YAML. Then, run your pipeline with specified pipeline arguments, rerun your pipeline with new arguments or data, schedule your pipeline to run on a recurring basis, organize your runs into experiments, save machine learning artifacts to compliant artifact registries, and visualize it all through the Kubeflow Dashboard.
Installation
To install kfp, run:
pip install kfp
Getting started
The following is an example of a simple pipeline that uses the kfp v2 syntax:
from kfp import dsl
import kfp
@dsl.component
def add(a: float, b: float) -> float:
'''Calculates sum of two arguments'''
return a + b
@dsl.pipeline(
name='Addition pipeline',
description='An example pipeline that performs addition calculations.')
def add_pipeline(
a: float = 1.0,
b: float = 7.0,
):
first_add_task = add(a=a, b=4.0)
second_add_task = add(a=first_add_task.output, b=b)
client = kfp.Client(host='<my-host-url>')
client.create_run_from_pipeline_func(
add_pipeline, arguments={
'a': 7.0,
'b': 8.0
})
1 maintainer
Release files for kfp-leinao 2.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| kfp-leinao-2.4.0.tar.gz | 401.1 kB | Details |
Release files / kfp-leinao-2.4.0.tar.gz
| Download URL | kfp-leinao-2.4.0.tar.gz |
|---|---|
| Size | 401.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
7185e13a00fddeff48f3243d5978370c5e665c8fe46d0f27855135e6b0da5229
|
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BLAKE2b-256 checksum How to use checksums |
be19f6884655e6e09b422cba024950e6922d6c9aeb5aa57938c28fa5211d0b70
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| Upload date | |
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No |
| Uploaded via |
twine/5.0.0 CPython/3.10.14
|
