Kubeflow, the Kubernetes native application for AI and Machine Learning, continues to accelerate feature additions and community growth. The community has released two new versions since the last Kubecon – 0.4 in January and 0.5 in April – and is currently working on the 0.6 release, to be released in July. The key features in each release are briefly discussed below.
Kubeflow at Kubecon
For those attending KubeCon + CloudNativeCon Europe 2019 in Barcelona, you can learn more about Kubeflow and how to apply it to your business in the following sessions:
TUESDAY, May 21
|14:00||Kubernetes the New Research Platform|
– Lindsey Tulloch, Brock University & Bob Killen, University of Michigan
|14:00||Tutorial: Introduction to Kubeflow Pipelines|
– Michelle Casbon, Dan Sanche, Dan Anghel, & Michal Zylinski, Google
|15:55||KubeFlow BoF (Birds of a Feather)|
– David Aronchick, Microsoft & Yaron Haviv, Iguazio
WEDNESDAY, May 22
|11:55||Towards Kubeflow 1.0, Bringing a Cloud Native Platform For ML to Kubernetes |
– David Aronchick, Microsoft & Jeremy Lewi, Google
|14:00||Building Cross-Cloud ML Pipelines with Kubeflow with Spark & Tensorflow|
– Holden Karau, Google & Trevor Grant, IBM
|14:50||Managing Machine Learning in Production with Kubeflow and DevOps|
– David Aronchick, Microsoft
THURSDAY, May 23
|11:55||A Tale of Two Worlds: Canary-Testing for Both ML Models and Microservices |
– Jörg Schad, ArangoDB & Vincent Lesierse, Vamp.io
|14:00||Moving People and Products with Machine Learning on Kubeflow |
– Jeremy Lewi, Google & Willem Pienaar, GO-JEK
|14:50||Economics and Best Practices of Running AI/ML Workloads on Kubernetes |
– Maulin Patel, Google & Yaron Haviv, Iguazio
What’s in Kubeflow 0.5?
- This is a summary of some of the key features:
- UI Improvements – new Central Dashboard and a new sidebar navigation
- JupyterHub Improvements – launch multiple notebooks, attach volume
- Fairing Python Library – build, train, and deploy models from notebooks or IDE
- Katib (hyperparameter) Improvements – more generic, updated CRD, better status
- KFCTL binary (configure and platform deploy). (https://deploy.kubeflow.cloud/)
- Pipelines Persistence (upgrade or reinstall)
- 150+ closed issues and 250+ merged PRs
You can learn more about the 0.5 release from the Kubeflow blog on 0.5.
What’s in Kubeflow 0.4?
- An updated JupyterHub UI that makes it easy to spawn notebooks with Persistent Volume Claims (PVCs).
- An alpha release of fairing, a python library that simplifies the build and train process for data scientists – they can start training jobs directly from a notebook or IDE.
- An initial release of a Custom Resource Definition (CRD) for managing Jupyter notebooks. You can use kubectl to create notebook containers.
- Kubeflow Pipelines for orchestrating ML workflows, which speeds the process of productizing models by reusing pipelines with different datasets or updated data.
- Katib support for TFJob, which makes it easier to tune models and compare performance with different hyper-parameters.
- Beta versions of the TFJob and PyTorch operators, which enable data scientists to program their training jobs against a more stable API and to more easily switch between training frameworks.
You can learn more about the 0.4 release from the Kubeflow blog on 0.4.