如何在 kubeflow 管道中使用训练运算符（例如 TFJob）定义组件/步骤
我知道有一种方法可以通过 kubectl 使用 tfjob 运算符，就像这里的示例 ( https://www.kubeflow.org/docs/components/training/tftraining/）：
kubectl create -f https://raw.githubusercontent.com/kubeflow/training-operator/master/examples/tensorflow/simple.yaml
但我不知道如何合并到 kubeflow 管道中。普通的组件/作业是通过 @component 装饰定义的，或者 ContainerOp 是一种在 Pod 中运行的 Kubernetes Job 类型，但我不知道如何定义组件特殊的训练运算符，例如 TFJob，以便我的代码
apiVersion: "kubeflow.org/v1"
kind: TFJob
运行为而不是：
apiVersion: "kubeflow.org/v1"
kind: Job
在 kubernetes 中
。 PS：这里有一个示例： https:// github.com/kubeflow/pipelines/blob/master/components/kubeflow/launcher/sample.py 但没有看到任何地方指定TFJob
I know there is a way to use tfjob operator via kubectl, like the example at here (https://www.kubeflow.org/docs/components/training/tftraining/):
kubectl create -f https://raw.githubusercontent.com/kubeflow/training-operator/master/examples/tensorflow/simple.yaml
But I don't know how to incorporate in kubeflow pipeline. A normal component/job is defined via @component decoration or ContainerOp is a Kubernetes Job kind which runs in a Pod, but I don't know how to define a component with special training operator such as TFJob, so that my code runs as
apiVersion: "kubeflow.org/v1"
kind: TFJob
rather than:
apiVersion: "kubeflow.org/v1"
kind: Job
in kubernetes.
P.S.: there is a example here: https://github.com/kubeflow/pipelines/blob/master/components/kubeflow/launcher/sample.py
but don't see anywhere specify TFJob
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您引用的示例利用了一些实际创建 TFJob 的代码（look在您的示例的文件夹）：
您提出的一般问题仍然受当前讨论。使用 tfjob_launcher_op 似乎是当前推荐的方式。相反，有些人还本机使用
ResourceOps来模拟您的kubectl create调用。The example you reference leverages some code that actually creates a TFJob (look at the folder of your example):
The general question you raised is still subject to current discussions. Using
tfjob_launcher_opappears to be the currently recommended way. Instead, some people also natively useResourceOpsto simulate yourkubectl createcall.