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setup.KubeConEU25/README.md

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@@ -824,7 +824,7 @@ This will create 2 Pods, each requesting 2 GPUs. On our cluster, it will take a
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to execute this training workload. We can check on the status of the PyTorchJob by using the command:
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```sh
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kubectl get pytorchjob -n blue
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kubectl get pytorchjob -n blue --watch
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```
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After the jobs completes, we can get the log of the worker Pod with

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