Kubeflow 1.0 brings cloud-native machine learning to Kubernetes

Kubeflow became open source software in December of 2017 at Kubecon USA. Now, in March of 2020, the first major release has arrived. Kubeflow 1.0 graduates several applications that help develop, build, train, and deploy models on Kubernetes.

Kubeflow is a machine learning toolkit designed to make deploying scalable ML workflows on Kubernetes easier. It includes a custom TensorFlow training job operator and services for creating and deploying Jupyter notebooks and focuses on leveraging cloud assets.

Let’s take a look at what the 1.0 milestone includes and what else Kubeflow has under the hood.

SEE ALSO: Why Kubernetes and containers are the perfect fit for machine learning

Kubeflow v1.0 graduating apps

What’s new? According to the v1.0 announcement blog, 1.0 graduates a “core set of stable applications needed to develop, build, train, and deploy models on Kubernetes efficiently”.

We’re proud and excited to announce a major milestone for our project: Kubeflow’s 1.0 release!https://t.co/bvMncVS4fH

Thank you to the countless organizations, contributors and users who helped us bring efficient #machinelearning tooling to Kubernetes.

— Kubeflow (@kubeflow) March 2, 2020

These graduating applications include:

• The central dashboard and UI navigation for Kubeflow components
• Jupyter notebook controller and web app, allowing users to create custom notebooks
• Tensorflow Operator (TFJob) for training a model with TensorFlow
• PyTorch Operator, for creating and monitoring PyTorch jobs and distributed training
• kfctl, the command-line interface
• Profile controller and UI, used to solve access management in multi-user Kubernetes clusters

As work on Kubeflow continues, expect to see more applications graduate to 1.0, including Pipelines, Metadata, and Katib.

v1.0 features

Using a variety of different Kubeflow components, users can develop, build, train, and deploy. Version 1.0 allows users to use develop models with Jupyter, build containers with Kubeflow fairing, and deploy them with KFServing.

Kubeflow 1.0 makes it easier than ever to deploy. It provides a CLI and configuration files that deploy with only one command:

kfctl apply -f kfctl_gcp_iap.v1.0.0.yaml

kfctl apply -f kfctl_k8s_istio.v1.0.0.yaml

kfctl apply -f kfctl_aws_cognito.v1.0.0.yaml

kfctl apply -f kfctl_ibm.v1.0.0.yaml

For a deeper look at the newest milestone, refer to the official Kubeflow blog by Thea Lamkin.

SEE ALSO: Unleash chaos engineering: Kubethanos kills half your Kubernetes pods

Tour of Kubeflow

Under the hood, Kubeflow makes scalable machine learning with Kubernetes easier by providing simple solutions and builds upon the power of Kubernetes.

With Kubeflow, anywhere that Kubernetes can run, you can also use the machine learning stack.

Kubeflow central dashboard. Source.

Its UI includes a central dashboard, Kubeflow Pipelines dashboard, Jupyter notebook servers, Katlib for hyperparameter tuning, artifact metadata tracking, and settings for sharing user access across namespaces in deployment.

The easy to navigate UI includes pre-built Docker images for Jupyter. You can use Jupyter notebooks to create interactive data science that integrates with the rest of the Kubeflow components. Users can set up multiple notebook servers per Kubeflow deployment.

That’s just scratching the surface of everything that Kubeflow can do. Visit the documentation for a getting started guide and learn all about its components and use cases.

Looking to get involved with the community? Join the kubeflow-discuss mailing list and keep up to date with progress or attend the weekly community meeting.

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