Last week, we hosted the “Intro to Kubeflow: Fundamentals Training and Certification” prep course. In this blog post we’ll recap some highlights from the class plus give a summary of the Q&A. Ok, let’s dig in!
Congratulations to Gaurav Agrawal!
What topics were covered in the course?
This initial course aimed to get data scientists and DevOps engineers with little or no experience familiar with the fundamentals of how Kubeflow works.
- Kubeflow architecture
- Overview of machine learning workflows
- Kubeflow components
- Tools and add-ons (Kale, Rok, Istio, etc)
- Kubeflow as a Service
- Community overview
What did I miss?
Here’s a short teaser from the 90 minute training. In this video we demonstrate three things in regards to Katib (which is a Kubeflow component that provides AutoML, hyperparameter tuning and early stopping):
- How to view an experiment
- How to set up experiments
- How to identify the best run
You can find a playlist of all the demos from the workshop here.
Missed the Sep 22 Kubeflow Fundamentals training?
If you were unable to join us last week, but would still like to attend a future training, the next “Kubeflow Fundamentals” training is happening on Oct 20. You can sign up for this and the upcoming Notebooks, Pipelines and Kale/Katib courses here.
NEW: Kubernetes, Distributed Training and Notebooks & Pipelines Workshops
We are excited to announce a new series of FREE workshops focused on taking popular Kaggle and Udacity machine learning examples from “Notebook to Pipeline.” Registration is now open for the following workshops:
- Sep 28: The Kaggle Covid-19 OpenVaccine Machine Learning Example
- Sep 29: Training Course: Jupyter Notebooks Fundamentals
- Sep 29: MLOps Meetup
- Oct 5: Distributed Training Workshop
- Oct 6: Training Course: Pipelines Fundamentals
- Oct 12: From Kubernetes to Kubeflow Workshop
- Oct 13: Training Course: Katib & Kale Fundamentals
If you are ready to put what you’ve learned into practice with hands-on labs? Then check out Arrikto Academy! On this site you’ll find a variety of FREE skills-building labs and tutorials including:
- Kubeflow Use Cases: Kaggle OpenVaccine, Kaggle Titanic Disaster, Kaggle Blue Book for Bulldozers, Dog Breed Classification, Distributed Training, Kaggle Digit Recognizer Competition
- Kubeflow Functionality – Kale, Katib
- Enterprise Kubeflow Skills – Kale SDK, Rok Registry
Q&A from the training
Below is a summary of some of the questions that popped into the Q&A box during the course. [Edited for readability and brevity.]
How can I use the KFP library on an existing Jupyter Notebook?
Yes. Existing Jupypter Notebooks can be modified to run as pipelines using either the KFP SDK (using a combination of Docker and Kubeflow DSL) or in an automated fashion using the Kale JupyterLab extension. You can catch an upcoming Kaggle workshop where we demonstrate how to take generic Jupyter Notebooks and get them to run as Kubeflow pipelines. More info here: https://www.arrikto.com/kubeflow-mlops-events/category/workshops/
Does Kale only work with Rok?
There are a few corner cases, where Kale can work without Rok today, and some community members use it that way, but in general it depends on Rok for most of its advanced functionality. That said, there will be interesting announcements coming later this year, please take a look at this message in the Kubeflow mailing list:
Is MLFlow bundled with Kubeflow?
How can I install Kubeflow on a local machine?
The options that you have for installing Kubeflow are here. According to the official Docs Charmed Kubeflow from Canonical appears to be the only packaged distribution that facilitates a local install.