Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Iṣeto GPU ati adanwo Orchestration iṣupọ

Practice GPU resource requests, gang scheduling and topology placement, including how preemption and fair-share policies affect shared training clusters.

Jẹmọ itọsọna awọn ọnaGPU iṣeto ati iṣupọ orchestration
Ibeere 1 ti 8

Why coordinate resource admission for a fixed-size distributed training worker group?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  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. Kini eto eto-imọ-imọ-jinlẹ gbiyanju lati mu dara si?
    • Ntan awọn iṣẹ bi jina yato si bi o ti ṣee
    • Dinku nọmba awọn olumulo
    • Gbigbe awọn ipo ibaraẹnisọrọ sori awọn asopọ iyara bi NVLink
    • Yiyan olupese ina mọnamọna ti ko gbowolori
  4. Iru afikun wo ni a lo nigbagbogbo pẹlu Kubernetes fun ipele ati ṣiṣe eto onijagidijagan ti awọn iṣẹ AI?
    • Onina
    • Apache Kafka
    • Redis
    • Grafana
  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. Kini ipa ti awọn ipin ipin-itọtọ ninu iṣupọ ipin kan?
    • Lati ṣe iṣeduro ẹgbẹ kan nigbagbogbo bori
    • Lati mu oluṣeto kuro
    • Lati ṣe idiwọ ẹgbẹ kan lati monopolizing gbogbo GPUs
    • Lati ṣe iyara iṣẹ kan
  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