Kubeflow is an open-source MLOps platform that runs on top of Kubernetes. Kubeflow 1.6 was released September 7 2022 with Canonical’s official distribution, Charmed Kubeflow, following shortly after. It came with support for Kubernetes 1.22.
However, the MLOps landscape evolves quickly and so does Charmed Kubeflow. As of today, Canonical supports the deployment of Charmed Kubeflow 1.6 on Charmed Kubernetes 1.23 and 1.24. This is essential as Kubernetes 1.22 is not maintained anymore, following the latest release of Kubernetes 1.25.
Kubeflow 1.6 for optimised advanced training
Kubeflow 1.6 came with new enhancements that focused on complex optimised model training. To be precise, the latest version focused on the stable version of the Kubeflow pipelines. They offer a better user experience through the stable version (KFP v2). Metadata is securely captured and recorded using the pipeline execution cache.
Hyperparameter is also enabled with the latest version of Kubeflow. Training operators are the champions here. They combine population-based training (PBT) with various AI frameworks such as Tensorflow or PyTorch.
The latest version of Kubeflow also makes data processing more seamless by providing better tracking capabilities. Trial logs are efficiently recorded and ML models are better measured. This makes evolution and debugging simpler. Preventing data drift is now possible, with the ability to detect data source failure.
Kubeflow and the Kubernetes lifecycle
Kubernetes’ lifecycle supports the latest three minor releases, based on the official guidelines. Canonical’s official distribution, Charmed Kubernetes, follows the same baseline. As an extra step, Canonical offers expanded security maintenance for the two older versions. Each version of Kubernetes reaches its end of life after approximately 10 months. They are always announced when a new version is released.
Kubeflow 1.6 on Kubernetes 1.23 and beyond
Canonical just finished the testing of Charmed Kubeflow 1.6 on two of the maintained versions of Charmed Kubernetes. It enables users to save time and continue using their Kubernetes version of choice when deploying the MLOps platform. Kubeflow has the same functionalities and features on all announced versions. It benefits from the new enhancements of Kubernetes.
From an enterprise perspective, this announcement is much more important. It allows the MLOps platform and orchestration tool to run in tandem and avoid security issues. It enables data scientists and machine learning engineers to focus on ML models, rather than infrastructure maintenance.
Currently, Canonical is working on supporting Charmed Kubeflow on the latest version of Kubernetes. It will be announced once the testing phase is completed and the application runs smoothly, and at maximum performance.
Learn more about Charmed Kubeflow
- What is Kubeflow?
- Charmed Kubeflow versioning information
- FAQ: MLOps with Charmed Kubeflow
- Charmed Kubeflow 1.6: what’s new?
- Hyperparamter tuning with an MLOps platform