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{
"lesson": "03-gpu-autoscaling-kubernetes",
"title": "Kubernetes 上的 GPU 自动扩缩 —— Karpenter、KAI Scheduler、Gang Scheduling",
"questions": [
{
"stage": "pre",
"question": "HPA 默认通常基于哪个信号进行扩缩,而本课认为该信号对 vLLM 式的服务是失效的?",
"options": [
"队列深度(queue depth)",
"P99 TTFT",
"KV 缓存利用率",
"DCGM_FI_DEV_GPU_UTIL 占空比(duty cycle)"
],
"correct": 3,
"explanation": ""
},
{
"stage": "check",
"question": "KAI Scheduler 中的 gang scheduling 主要防止什么问题?",
"options": [
"GPU 显存碎片化",
"Tokenizer 的 GIL 争用",
"部分分配陷阱:8 块 GPU 中有 7 块空转等待第 8 块",
"冷启动延迟"
],
"correct": 2,
"explanation": ""
},
{
"stage": "check",
"question": "为什么 Karpenter 默认的 consolidationPolicy: WhenEmptyOrUnderutilized 对推理 GPU 池是危险的?",
"options": [
"它在突发流量下拒绝扩容",
"它只合并 spot 实例",
"它会终止正在运行的 GPU 节点以迁移 pod,从而驱逐正在处理的请求并重新加载权重",
"它会阻止 Karpenter 配置新节点"
],
"correct": 2,
"explanation": ""
},
{
"stage": "check",
"question": "与 Cluster Autoscaler 相比,Karpenter 配置一个 GPU 节点大约快多少?",
"options": [
"大约快 40%(约 45-60 秒 对比 约 90-120 秒)",
"大约慢 10 倍",
"一样快",
"大约快 5%"
],
"correct": 0,
"explanation": ""
},
{
"stage": "post",
"question": "对于 prefill / decode 分离的 pod(Phase 17 · 17),本课推荐使用哪些扩缩信号?",
"options": [
"仅手动扩缩",
"用单个基于占空比的 HPA 覆盖两类 pod",
"prefill pod 用队列深度,decode pod 用 KV 缓存压力,分别作为按角色独立的 HPA",
"两者都用 Cluster Autoscaler"
],
"correct": 2,
"explanation": ""
},
{
"stage": "post",
"question": "为避免驱逐正在运行的作业,本课为推理 GPU 池推荐哪种 Karpenter 中断(disruption)设置?",
"options": [
"consolidationPolicy: WhenEmptyOrUnderutilized 配合 consolidateAfter: 0s",
"consolidationPolicy: WhenEmpty 配合 consolidateAfter: 1h",
"始终使用 spot 实例且不进行合并",
"完全禁用 Karpenter"
],
"correct": 1,
"explanation": ""
}
]
}