# Kubeflow Trainer v2 - TensorFlow Custom ClusterTrainingRuntime
# Trainer v2에는 TensorFlow runtime이 기본 제공되지 않으므로 Custom Runtime으로 생성
#
# 핵심 차이점:
#   - PyTorch runtime: mlPolicy.torch 사용 → torchrun 자동 주입, MASTER_ADDR/RANK 등 자동 설정
#   - TensorFlow runtime (custom): mlPolicy에 framework 없음 → TF_CONFIG를 학습 스크립트에서 자체 구성
#
# TF_CONFIG 자동 구성 방식:
#   1. Pod hostname에서 TrainJob 이름과 replica index 추출
#   2. Headless Service DNS를 이용한 worker 주소 목록 생성
#   3. tf.distribute.MultiWorkerMirroredStrategy로 분산학습 실행

apiVersion: trainer.kubeflow.org/v1alpha1
kind: ClusterTrainingRuntime
metadata:
  name: tensorflow-distributed
  labels:
    trainer.kubeflow.org/framework: tensorflow
spec:
  mlPolicy:
    numNodes: 1
  template:
    spec:
      network:
        publishNotReadyAddresses: true
      replicatedJobs:
      - name: node
        template:
          metadata:
            labels:
              trainer.kubeflow.org/trainjob-ancestor-step: trainer
          spec:
            template:
              spec:
                containers:
                - name: node
                  image: tensorflow/tensorflow:2.16.1-gpu
                  env:
                    - name: NUM_WORKERS
                      value: "1"
                    - name: TF_WORKER_PORT
                      value: "12345"
                    - name: NAMESPACE
                      valueFrom:
                        fieldRef:
                          fieldPath: metadata.namespace