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Posted to reviews@yunikorn.apache.org by GitBox <gi...@apache.org> on 2021/12/04 06:45:52 UTC
[GitHub] [incubator-yunikorn-site] lowc1012 opened a new pull request #96: [YUNIKORN-953] Investigate of kubeflow/training-operator
lowc1012 opened a new pull request #96:
URL: https://github.com/apache/incubator-yunikorn-site/pull/96
Update the run_tensorflow.md
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[GitHub] [incubator-yunikorn-site] yangwwei merged pull request #96: [YUNIKORN-953] Investigate of kubeflow/training-operator
Posted by GitBox <gi...@apache.org>.
yangwwei merged pull request #96:
URL: https://github.com/apache/incubator-yunikorn-site/pull/96
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[GitHub] [incubator-yunikorn-site] lowc1012 commented on a change in pull request #96: [YUNIKORN-953] Investigate of kubeflow/training-operator
Posted by GitBox <gi...@apache.org>.
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/namespace.yaml
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[GitHub] [incubator-yunikorn-site] yangwwei commented on a change in pull request #96: [YUNIKORN-953] Investigate of kubeflow/training-operator
Posted by GitBox <gi...@apache.org>.
yangwwei commented on a change in pull request #96:
URL: https://github.com/apache/incubator-yunikorn-site/pull/96#discussion_r762401121
##########
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:
Ah, I see
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[GitHub] [incubator-yunikorn-site] lowc1012 commented on a change in pull request #96: [YUNIKORN-953] Investigate of kubeflow/training-operator
Posted by GitBox <gi...@apache.org>.
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
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[GitHub] [incubator-yunikorn-site] yangwwei commented on a change in pull request #96: [YUNIKORN-953] Investigate of kubeflow/training-operator
Posted by GitBox <gi...@apache.org>.
yangwwei commented on a change in pull request #96:
URL: https://github.com/apache/incubator-yunikorn-site/pull/96#discussion_r762395993
##########
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,
Review comment:
You can the following command to install the training operator in the `kubeflow` namespace, please refer to [this doc|xxx] for details.
##########
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:
missing `-n kubeflow` in this command? since earlier it said to install in kubeflow namespace
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