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Kuis Penjadwalan GPU dan Orkestrasi Cluster

Latih permintaan sumber daya GPU, penjadwalan geng, dan penempatan topologi, termasuk bagaimana kebijakan preemption dan pembagian adil memengaruhi klaster pelatihan bersama.

Jalur panduan terkaitPenjadwalan GPU dan Orkestrasi Klaster
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Why coordinate resource admission for a fixed-size distributed training worker group?

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  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. Apa yang coba dioptimalkan oleh penjadwalan sadar topologi?
    • Menyebarkan pekerjaan sejauh mungkin
    • Meminimalkan jumlah pengguna
    • Menempatkan peringkat komunikasi pada interkoneksi cepat seperti NVLink
    • Memilih penyedia listrik termurah
  4. Add-on manakah yang biasa digunakan dengan Kubernetes untuk penjadwalan batch dan geng pekerjaan AI?
    • Gunung berapi
    • Apache Kafka
    • ulang
    • 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. Apa peran kuota pembagian adil dalam klaster bersama?
    • Untuk menjamin satu tim selalu menang
    • Untuk menonaktifkan penjadwal
    • Untuk mencegah satu tim memonopoli semua GPU
    • Untuk mempercepat satu pekerjaan
  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