GPU 스케줄링 및 클러스터 오케스트레이션 퀴즈

퀴즈를 풀면 정답과 설명이 나타납니다.

1. 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
2. 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
3. 토폴로지 인식 스케줄링은 무엇을 최적화하려고 합니까?
   • 일자리를 최대한 멀리 분산시키세요
   • 사용자 수 최소화
   • NVLink와 같은 빠른 상호 연결에 통신 순위 배치
   • 가장 저렴한 전기 공급자 선택
4. AI 작업의 일괄 및 집단 예약을 위해 Kubernetes와 함께 일반적으로 사용되는 추가 기능은 무엇입니까?
   • 화산
   • 아파치 카프카
   • 레디스
   • 그라파나
5. 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
6. 공유 클러스터에서 공정 공유 할당량의 역할은 무엇입니까?
   • 한 팀이 항상 승리하도록 보장하기 위해
   • 스케줄러를 비활성화하려면
   • 단일 팀이 모든 GPU를 독점하는 것을 방지하기 위해
   • 단일 작업 속도를 높이려면
7. 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
8. 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