Slurm kwa Maswali ya Makundi ya Mafunzo ya AI

Majibu na maelezo huonekana unapojibu maswali.

1. Watumiaji hutumia amri gani kwa kawaida kuwasilisha kazi ya kundi kwa Slurm?
   • sbatch
   • kubectl kuomba
   • kukimbia kwa docker
   • git push
2. Je, kazi ya Slurm inaomba vipi GPU?
   • Kwa kuhariri rekodi za DNS
   • Kupitia --gres=gpu maagizo
   • Kupitia fomu ya wavuti pekee
   • GPU haziwezi kuombwa katika Slurm
3. Ni sehemu gani ya Slurm hufanya maamuzi kuu ya kuratibu?
   • slurmd
   • srun
   • slurmctld
   • sbatch
4. Why can srun help launch distributed training tasks inside a Slurm allocation?
   • It automatically restores every model checkpoint
   • It replaces slurmctld as the scheduler
   • It converts a single GPU into multiple physical GPUs
   • It launches coordinated tasks across allocated nodes; the training setup may still need explicit DDP configuration
5. Madhumuni ya kuratibu kujaza tena katika Slurm ni nini?
   • Kuendesha kazi fupi katika mapengo yasiyo na kazi bila kuchelewesha kazi iliyopewa kipaumbele cha juu
   • Ili kufuta kazi za zamani
   • Ili kuzidi GPU
   • Ili kusimba kumbukumbu za kazi
6. What must an AI training script do so a requeued Slurm job can continue after a node failure?
   • Assume Slurm stores model weights automatically
   • Save checkpoints and reload them when the job restarts
   • Disable resource accounting
   • Submit a new job without restoring state
7. Which Slurm daemon normally runs on each compute node to launch tasks and report node status?
   • slurmctld
   • sbatchd
   • slurmd
   • gresctl
8. What can Slurm resource accounting help cluster administrators track?
   • The model’s validation accuracy automatically
   • The semantic content of training data
   • Whether every checkpoint is scientifically valid
   • Job resource use such as allocated CPUs, GPUs and time