Kubeflow

Kubeflow is an open-source platform for machine learning and MLOps on Kubernetes introduced by Google. The different stages in a typical machine learning lifecycle are represented with different software components in Kubeflow, including model development (Kubeflow Notebooks[4]), model training (Kubeflow Pipelines,[5] Kubeflow Training Operator[6]), model serving (KServe[lower-alpha 1][7]), and automated machine learning (Katib[8]).

Kubeflow
Original author(s)Google
Developer(s)Kubeflow Contributors[1] - AWS, Bloomberg, Google, IBM, NVIDIA, Nutanix, Red Hat, Arrikto, and others
Initial releaseApril 5, 2018 (2018-04-05)[2]
Stable release
1.6[3] / September 7, 2022 (2022-09-07)
Repositorygithub.com/kubeflow
Written inGo, Python
PlatformKubernetes
TypeMachine Learning Platform
LicenseApache License 2.0
Websitekubeflow.org

Each component of Kubeflow can be deployed separately, and it is not a requirement to deploy every component.[9]

History

The Kubeflow project was first announced at KubeCon + CloudNativeCon North America 2017 by Google engineers David Aronchick, Jeremy Lewi, and Vishnu Kannan[10] to address a perceived lack of flexible options for building production-ready machine learning systems.[11] The project has also stated it began as a way for Google to open-source how they ran TensorFlow internally.[12]

The first release of Kubeflow (Kubeflow 0.1) was announced at KubeCon + CloudNativeCon Europe 2018[13] with claims of having already become among the top 2% of GitHub projects ever.[14] Kubeflow 1.0 was released in March 2020 via a public blog post announcing that many Kubeflow components were graduating to a "stable status", indicating they were now ready for production usage.[15]

Components

Kubeflow Notebooks for model development

Machine learning models are developed in the notebooks component called Kubeflow Notebooks. The component runs web-based development environments inside a Kubernetes cluster, with native support for Jupyter Notebook, Visual Studio Code, and RStudio.[16]

Kubeflow Pipelines for model training

Once developed, models are trained in the Kubeflow Pipelines component. The component acts as a platform for building and deploying portable, scalable machine learning workflows based on Docker containers.[17] Google Cloud Platform has adopted the Kubeflow Pipelines DSL within its Vertex AI Pipelines product.[18]

Kubeflow Training Operator for model training

For certain machine learning models and libraries, the Kubeflow Training Operator component provides Kubernetes custom resources support. The component runs distributed or non-distributed TensorFlow, PyTorch, Apache MXNet, XGBoost, and MPI training jobs on Kubernetes.[6]

KServe for model serving

The KServe component (previously named KFServing[19]) provides Kubernetes custom resources for serving machine learning models on arbitrary frameworks including TensorFlow, XGBoost, scikit-learn, PyTorch, and ONNX.[20] KServe was developed collaboratively by Google, IBM, Bloomberg, NVIDIA, and Seldon.[19] Publicly disclosed adopters of KServe include Bloomberg,[21] Gojek,[22] and others.[23]

Katib for automated machine learning

Lastly, Kubeflow includes a component for automated training and development of machine learning models, the Katib component. It is described as a Kubernetes-native project and features hyperparameter tuning, early stopping, and neural architecture search.[24]

Release timeline
Release timeline
VersionRelease DateRelease InformationRelease Blog
Kubeflow 0.1 5 April, 2018[2] - https://kubernetes.io/blog/2018/05/04/announcing-kubeflow-0.1/
Kubeflow 0.2 2 July, 2018[25] - https://medium.com/kubeflow/kubeflow-0-2-offers-new-components-and-simplified-setup-735e4c56988d
Kubeflow 0.3 5 October, 2018[26] - https://medium.com/kubeflow/kubeflow-0-3-simplifies-setup-improves-ml-development-98b8ca10bd69
Kubeflow 0.4 8 January, 2019[27] - https://medium.com/kubeflow/kubeflow-0-4-release-enhancements-for-machine-learning-productivity-d77c54df07a9
Kubeflow 0.5 9 April, 2019[28] - https://medium.com/kubeflow/kubeflow-v0-5-simplifies-model-development-with-enhanced-ui-and-fairing-library-78e19cdc9f50
Kubeflow 0.6 19 July, 2019[29] https://www.kubeflow.org/docs/releases/kubeflow-0.6/ https://medium.com/kubeflow/kubeflow-v0-6-a-robust-foundation-for-artifact-tracking-data-versioning-multi-user-support-9896d329412c
Kubeflow 0.7 17 October, 2019[30] https://www.kubeflow.org/docs/releases/kubeflow-0.7/ https://medium.com/kubeflow/kubeflow-v0-7-delivers-beta-functionality-in-the-leadup-to-v1-0-1e63036c07b8
Kubeflow 1.0 20 February, 2020[31] https://www.kubeflow.org/docs/releases/kubeflow-1.0/ https://blog.kubeflow.org/releases/2020/03/02/kubeflow-1-0-cloud-native-ml-for-everyone
Kubeflow 1.1 31 July, 2020[32] https://www.kubeflow.org/docs/releases/kubeflow-1.1/ https://blog.kubeflow.org/release/official/2020/07/31/kubeflow-1.1-blog-post
Kubeflow 1.2 18 November, 2020[33] https://www.kubeflow.org/docs/releases/kubeflow-1.2/ https://blog.kubeflow.org/release/official/2020/11/18/kubeflow-1.2-blog-post
Kubeflow 1.3 23 April, 2021[34] https://www.kubeflow.org/docs/releases/kubeflow-1.3/ https://blog.kubeflow.org/kubeflow-1.3-release/
Kubeflow 1.4 12 October, 2021[35] https://www.kubeflow.org/docs/releases/kubeflow-1.4/ https://blog.kubeflow.org/kubeflow-1.4-release/
Kubeflow 1.5 10 March, 2022[36] https://www.kubeflow.org/docs/releases/kubeflow-1.5/ https://blog.kubeflow.org/kubeflow-1.5-release/
Kubeflow 1.6 7 September, 2022[3] https://www.kubeflow.org/docs/releases/kubeflow-1.6/ https://blog.kubeflow.org/kubeflow-1.6-release/

Notes
  1. KServe was previously known as KFServing[lower-alpha 2]

References
  1. "Kubeflow Website - Working Groups".
  2. "Kubeflow 0.1 - Release Tag".
  3. "Kubeflow 1.6 - Release Information".
  4. "Kubeflow Website - Kubeflow Notebooks".
  5. "Kubeflow Website - Kubeflow Pipelines".
  6. "Kubeflow GitHub - Kubeflow Training Operator".
  7. "Kubeflow Website - KServe".
  8. "Kubeflow Website - Katib".
  9. "Kubeflow Website - Installing Kubeflow".
  10. ""Hot Dogs or Not" - At Scale with Kubernetes [I] - Vish Kannan & David Aronchick, Google".
  11. "Introducing Kubeflow - A Composable, Portable, Scalable ML Stack Built for Kubernetes".
  12. "Kubeflow Website - History".
  13. "Google-led Kubeflow, machine learning for Kubernetes, begins to take shape".
  14. "Announcing Kubeflow 0.1".
  15. "Kubeflow 1.0: Cloud-Native ML for Everyone".
  16. "Kubeflow Website - Kubeflow Notebooks Overview".
  17. "Kubeflow Website - Kubeflow Pipelines Introduction".
  18. "Vertex AI - Building a pipeline".
  19. "KServe: The next generation of KFServing".
  20. "KServe GitHub".
  21. "The journey to build Bloomberg's ML Inference Platform Using KServe (formerly KFServing)".
  22. "Merlin: Making ML Model Deployments Magical".
  23. "KServe Website - Adopters of KServe".
  24. "Kubeflow GitHub - Katib".
  25. "Kubeflow 0.2 - Release Tag".
  26. "Kubeflow 0.3 - Release Tag".
  27. "Kubeflow 0.4 - Release Tag".
  28. "Kubeflow 0.5 - Release Tag".
  29. "Kubeflow 0.6 - Release Information".
  30. "Kubeflow 0.7 - Release Information".
  31. "Kubeflow 1.0 - Release Information".
  32. "Kubeflow 1.1 - Release Information".
  33. "Kubeflow 1.2 - Release Information".
  34. "Kubeflow 1.3 - Release Information".
  35. "Kubeflow 1.4 - Release Information".
  36. "Kubeflow 1.5 - Release Information".
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