lowc1012 commented on a change in pull request #96: URL: https://github.com/apache/incubator-yunikorn-site/pull/96#discussion_r762400155
########## File path: docs/user_guide/workloads/run_tensorflow.md ########## @@ -24,17 +25,69 @@ specific language governing permissions and limitations under the License. --> -Here is an example for Tensorflow job. You must install tf-operator first. -You can install tf-operator by applying all yaml from two website down below: -* CRD: https://github.com/kubeflow/manifests/tree/master/tf-training/tf-job-crds/base -* Deployment: https://github.com/kubeflow/manifests/tree/master/tf-training/tf-job-operator/base -Also you can install kubeflow which can auto install tf-operator for you, URL: https://www.kubeflow.org/docs/started/getting-started/ +This guide gives an overview of how to set up [training-operator](https://github.com/kubeflow/training-operator) +and how to run a Tensorflow job with YuniKorn scheduler. The training-operator is a unified training operator maintained by +Kubeflow. It not only supports TensorFlow but also PyTorch, XGboots, etc. -A simple Tensorflow job example: +## Install training-operator +You can run one command to install training operator in kubeflow namespace. If you have problems with installation, +please refer to [this doc](https://github.com/kubeflow/training-operator#installation) for more details. +``` +kubectl apply -k "github.com/kubeflow/training-operator/manifests/overlays/standalone?ref=v1.3.0" Review comment: Thanks @yangwwei The manifests is in kubeflow namespace by default, so "-n kubeflow" can be omitted. ref: https://github.com/kubeflow/training-operator/blob/master/manifests/overlays/standalone/kustomization.yaml#L3 -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
