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.
- 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.
| Operator Version | API Version | Kubernetes Version |
|---|---|---|
v1.0.x |
v1 |
1.16+ |
v1.1.x |
v1 |
1.16+ |
v1.2.x |
v1 |
1.16+ |
v1.3.x |
v1 |
1.18+ |
latest (master HEAD) |
v1 |
1.18+ |