Corey Brooks

Corey Brooks

1592033216

Intro to Kubeflow Pipelines

Continuous training in production, automatic tracking of metadata, and reusable ML components! These are just some of the ways that Kubeflow Pipelines handle the orchestration of ML workflows. In this episode of Kubeflow 101, Stephanie Wong shows you how Kubeflow Pipelines makes ML workflows easily composable, shareable, and reproducible.

#kubeflow #machine-learning #devops #developer #kubernetes

What is GEEK

Buddha Community

Intro to Kubeflow Pipelines
Martin  Soit

Martin Soit

1600410108

Machine Learning Pipelines with Kubeflow

Why Machine Learning Pipelines?

A lot of attention is being given now to the idea of Machine Learning Pipelines, which are meant to automate and orchestrate the various steps involved in training a machine learning model; however, it’s not always made clear what the benefits are of modeling machine learning workflows as automated pipelines.

When tasked with training a new ML model, most Data Scientists and ML Engineers will probably start by developing some new Python scripts or interactive notebooks that perform the data extraction and preprocessing necessary to construct a clean set of data on which to train the model. Then, they might create several additional scripts or notebooks to try out different types of models or different machine learning frameworks. And finally, they’ll gather and explore metrics to evaluate how each model performed on a test dataset, and then determine which model to deploy to production.

#kubeflow-pipelines #kubernetes #kubeflow #machine-learning

Royce  Reinger

Royce Reinger

1673726700

Pipelines: Machine Learning Pipelines for Kubeflow

Overview of the Kubeflow pipelines service

Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.

Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.

The Kubeflow pipelines service has the following goals:

  • End to end orchestration: enabling and simplifying the orchestration of end to end machine learning pipelines
  • Easy experimentation: making it easy for you to try numerous ideas and techniques, and manage your various trials/experiments.
  • Easy re-use: enabling you to re-use components and pipelines to quickly cobble together end to end solutions, without having to re-build each time.

Installation

Install Kubeflow Pipelines from choices described in Installation Options for Kubeflow Pipelines.

:star: [Alpha] Starting from Kubeflow Pipelines 1.7, try out Emissary Executor. Emissary executor is Container runtime agnostic meaning you are able to run Kubeflow Pipelines on Kubernetes cluster with any Container runtimes. The default Docker executor depends on Docker container runtime, which will be deprecated on Kubernetes 1.20+.

Documentation

Get started with your first pipeline and read further information in the Kubeflow Pipelines overview.

See the various ways you can use the Kubeflow Pipelines SDK.

See the Kubeflow Pipelines API doc for API specification.

Consult the Python SDK reference docs when writing pipelines using the Python SDK.

Refer to the versioning policy and feature stages documentation for more information about how we manage versions and feature stages (such as Alpha, Beta, and Stable).

Contributing to Kubeflow Pipelines

Before you start contributing to Kubeflow Pipelines, read the guidelines in How to Contribute. To learn how to build and deploy Kubeflow Pipelines from source code, read the developer guide.

Kubeflow Pipelines Community Meeting

The meeting is happening every other Wed 10-11AM (PST) Calendar Invite or Join Meeting Directly

Meeting notes

Kubeflow Pipelines Slack Channel

#kubeflow-pipelines

Blog posts

Acknowledgments

Kubeflow pipelines uses Argo Workflows by default under the hood to orchestrate Kubernetes resources. The Argo community has been very supportive and we are very grateful. Additionally there is Tekton backend available as well. To access it, please refer to Kubeflow Pipelines with Tekton repository.

Download Details:

Author: Kubeflow
Source Code: https://github.com/kubeflow/pipelines 
License: Apache-2.0 license

#machinelearning #kubernetes #datascience #pipeline 

Owen  Lemke

Owen Lemke

1620397920

Kubeflow: Not Yet Ready for Production?

We’re building a **reference machine learning architecture: **a free set of documents and scripts to combine our chosen open source tools into a reusable machine learning architecture that we can apply to most problems.

Kubeflow — a machine learning platform built on Kubernetes, and which has many of the same goals — seemed like a great fit for our project in the beginning. We tried it for several weeks, but after facing several challenges, we’ve now decided to drop it completely.

This article describes our Kubeflow experience. Our goal is to help others see — earlier than we did — that Kubeflow might not be everything it claims to be quite yet.

To be clear: Kubeflow has some shortcomings that prevented us from relying on it for this project. That said, we still respect Kubeflow’s goals, and we hope that as the project matures and addresses some of these issues, we can revisit the idea of using it in the future.

#machine-learning #data-science #kubeflow-pipelines #kubernetes #kubeflow

Corey Brooks

Corey Brooks

1592033216

Intro to Kubeflow Pipelines

Continuous training in production, automatic tracking of metadata, and reusable ML components! These are just some of the ways that Kubeflow Pipelines handle the orchestration of ML workflows. In this episode of Kubeflow 101, Stephanie Wong shows you how Kubeflow Pipelines makes ML workflows easily composable, shareable, and reproducible.

#kubeflow #machine-learning #devops #developer #kubernetes

Anton Palyonko

Anton Palyonko

1621173840

Scaling ML Pipelines with KALE — The Kubeflow Automated Pipeline Engine - Salman Iqbal, Learnk8s

Scaling ML pipelines with KALE — the Kubeflow Automated Pipeline Engine - Salman Iqbal, Learnk8s

One of the most common hurdles with developing AI and deep learning models is to design data pipelines that can operate at scale and in real-time. Data scientists and engineers are often expected to learn, develop and maintain the infrastructure for their experiments. What’s the best setup and what’s involved in getting models being production-ready? Where do you start? In this talk, you will learn about KALE — the Kubeflow Automated Pipeline Engine. With KALE you can finally link the work done by data scientists in Jupyter Notebooks to a production-grade pipeline that trains the models at scale and serves them in real-time.

#kubeflow #machine-learning