What is Kubeflow?
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow.
TensorFlow model training
Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes.
Model serving
Kubeflow supports a TensorFlow Serving container to export trained TensorFlow models to Kubernetes. Kubeflow is also integrated with Seldon Core, an open source platform for deploying machine learning models on Kubernetes, NVIDIA Triton Inference Server for maximized GPU utilization when deploying ML/DL models at scale, and MLRun Serving, an open-source serverless framework for deployment and monitoring of real-time ML/DL pipelines.
Multi-framework
Our development plans extend beyond TensorFlow. We're working hard to extend the support of PyTorch, Apache MXNet, MPI, XGBoost, Chainer, and more. We also integrate with Istio and Ambassador for ingress, Nuclio as a fast multi-purpose serverless framework, and Pachyderm for managing your data science pipelines.
Community
We are an open and welcoming community of software developers, data scientists, and organizations! Join our Slack Workspace!
Join our community
Check out the weekly community call, get involved in discussions on the mailing list or chat with others on the Slack Workspace!
Join the community