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Kubeflow
Open-source machine learning platform From Wikipedia, the free encyclopedia
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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[a][7]), and automated machine learning (Katib[8]).
 | A major contributor to this article appears to have a close connection with its subject. (September 2022) |
Each component of Kubeflow can be deployed separately, and it is not a requirement to deploy every component.[9]
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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][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]
In October 2022, Google announced that the Kubeflow project had applied to join the Cloud Native Computing Foundation.[16][17] In July 2023, the foundation voted to accept Kubeflow as an incubating stage project.[18][19]
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Components
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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.[20]
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.[21] Google Cloud Platform has adopted the Kubeflow Pipelines DSL within its Vertex AI Pipelines product.[22]
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[23]) provides Kubernetes custom resources for serving machine learning models on arbitrary frameworks including TensorFlow, XGBoost, scikit-learn, PyTorch, and ONNX.[24] KServe was developed collaboratively by Google, IBM, Bloomberg, NVIDIA, and Seldon.[23] Publicly disclosed adopters of KServe include Bloomberg,[25] Gojek,[26] the Wikimedia Foundation,[27] and others.[28]
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.[29]
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Notes
- KServe was previously known as KFServing[23]
References
External links
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