Before v1.2 release, tensorflow-operator can only support TFJob on Kubernetes. Starting from v1.3, Training Operator provides Kubernetes custom resources that makes it easy to run distributed or non-distributed TensorFlow/PyTorch/MXNet/XGBoost jobs on Kubernetes.

• For a complete reference of the custom resource definitions, please refer to the API Definition.
  • Tensorflow API Definition
  • PyTorch API Definition
  • MXNet API Definition
  • XGBoost API Definition
• For details on API design, please refer to the v1alpha2 design doc.
• For details of all-in-one operator design, please refer to the All-in-one Kubeflow Training Operator
• For details on its obersibility, please refer to the monitoring design doc.

• Version >= 1.16 of Kubernetes
• Version >= 3.x of Kustomize
• Version >= 1.21.x of Kubectl

kubectl apply -k "github.com/kubeflow/tf-operator.git/manifests/overlays/standalone?ref=master"

kubectl apply -k "github.com/kubeflow/tf-operator.git/manifests/overlays/standalone?ref=v1.3.0"

For users who prefer to use original tensorflow controllers, please checkout v1.2-branch, we will maintain the bug fix in this branch.

kubectl apply -k "github.com/kubeflow/tf-operator.git/manifests/overlays/standalone?ref=v1.2.0"

Please refer to the quick-start-v1.md and Kubeflow Training User Guide for more information.

Please refer to API Documentation.

• Tensorflow API Documentation
• PyTorch API Documentation
• MXNet API Documentation
• XGBoost API Documentation

You can:

• Join our Slack channel.
• Check out who is using this operator.

This is a part of Kubeflow, so please see readme in kubeflow/kubeflow to get in touch with the community.

Please refer to the DEVELOPMENT

Please refer to CHANGELOG

The following table lists the most recent few versions of the operator.