coreweave-core-workflow-b

# CoreWeave Core Workflow: GPU Training ## Overview Run distributed GPU training on CoreWeave: single-node multi-GPU and multi-node training with PyTorch DDP, Slurm-on-Kubernetes, and shared storage. ## Prerequisites - CKS cluster with multi-GPU node pools (8xA100 or 8xH100) - Shared storage (CoreWeave PVC or NFS) - Training container with PyTorch and NCCL ## Instructions ### Step 1: Single-Node Multi-GPU Training ```yaml # training-job.yaml apiVersion: batch/v1 kind: Job metadata: name: llm-finetune spec: template: spec: restartPolicy: Never containers: - name: trainer image: ghcr.io/myorg/trainer:latest command: ["torchrun"] args: - "--nproc_per_node=8" - "train.py" - "--model_name=meta-llama/Llama-3.1-8B" - "--batch_size=4" - "--epochs=3" resources: limits: nvidia.com/gpu: "8" memory: 512Gi cpu: "64" volumeMounts: - name: data mountPath: /data - name: checkpoints mountPath: /checkpoints volumes: - name: data persistentVolumeClaim: claimName: training-data - name: checkpoints persistentVolumeClaim: claimName: model-checkpoints affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: ...