引导式测验 · 中等 等级
GPU 调度和集群编排测验
练习 GPU 资源请求、组调度和拓扑布局,包括抢占和公平共享策略如何影响共享训练集群。
问题 1 的 8
Why coordinate resource admission for a fixed-size distributed training worker group?
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- Why coordinate resource admission for a fixed-size distributed training worker group?
- To guarantee every process starts at exactly the same nanosecond
- To guarantee the model converges
- To remove all worker communication
- To reduce partial-start situations where workers wait for unavailable peers
- How does Kubernetes normally express a device-plugin GPU resource request?
- As arbitrary fractions of CPU clock cycles
- As an integer count of advertised device resources
- As a DNS hostname only
- As a string of model output tokens
- 拓扑感知调度试图优化什么?
- 尽可能分散工作岗位
- 最大限度地减少用户数量
- 将通信等级置于 NVLink 等快速互连上
- 选择最便宜的电力供应商
- 哪个附加组件通常与 Kubernetes 一起用于 AI 作业的批量和组调度?
- 火山
- 阿帕奇·卡夫卡
- 雷迪斯
- 格拉法纳
- What must a training application provide if a preempted job should resume its previous progress?
- A guarantee that preemption saves optimizer state automatically
- A usable checkpoint and recovery procedure
- A renamed pod with no saved state
- A higher learning rate for its first resumed step
- 共享集群中公平份额配额的作用是什么?
- 确保一支球队永远获胜
- 禁用调度程序
- 防止任何一个团队垄断所有 GPU
- 加快单个作业的速度
- A fixed worker group needs eight GPU resources but only four are available. What can group admission accomplish?
- Keep the group pending until its configured minimum requirements can be met
- Invent the missing four GPU devices
- Turn every worker into a CPU process without configuration
- Guarantee completion even with no admitted workers
- Two communicating GPU jobs fit the same resource counts on different nodes. Why consider topology as well?
- The resource count alone fixes every transfer latency
- Topology determines the training labels
- Only node names affect all-reduce outputs
- Interconnect placement affects communication costs between their workers