What's the most cost-effective way to run hyperparameter sweeps using a job scheduler for GPUs?

The most cost-effective approach is usually:

Use the scheduler to run lots of small, independent training jobs on the cheapest available GPUs, with early stopping/pruning, and keep each job as short as possible.

Concretely:

1. Use random search or Bayesian optimization

   • Avoid brute-force grid search.
   • Random search is often the best cost/performance baseline.
   • If you can, use Optuna, Ray Tune, or Nevergrad for smarter sweeps.

2. Prune bad runs early

   • This saves far more money than micro-optimizing GPU utilization.
   • Use scheduler-friendly training scripts that report metrics frequently.
   • Enable:
     • Optuna pruning
     • ASHA / Hyperband
     • custom early-stop based on validation loss

3. Use small, fixed time allocations per trial

   • Submit each trial as a separate job or array task.
   • Set a reasonable walltime.
   • Kill/stop trials that don’t improve early.

4. Use job arrays when the scheduler supports them

   • Great for many independent trials.
   • Example schedulers: Slurm, PBS, LSF.
   • This reduces overhead and simplifies bookkeeping.

5. Use the smallest GPU that fits the model

   • Don’t run sweeps on expensive A100/H100s unless necessary.
   • For many models, consumer GPUs or older datacenter GPUs are much cheaper.
   • If your cluster supports it, prefer preemptible/spot/low-priority nodes.

6. Maximize GPU occupancy per job

   • If the model is small, pack multiple trials onto one GPU only if they won’t interfere too much.
   • Otherwise, run one trial per GPU and let the scheduler pack jobs efficiently.
   • Avoid leaving GPUs idle due to excessive CPU/data-loader bottlenecks.

7. Make data loading efficient

   • Cache datasets locally or on fast shared storage.
   • Use enough CPU threads/workers.
   • This prevents paying for expensive GPU time while waiting on I/O.

8. Checkpoint wisely

   • Save only what you need to resume or analyze.
   • Use checkpoints so promising runs can continue, but avoid huge unnecessary checkpoint overhead.

9. Reuse infrastructure

   • Containerize the environment once.
   • Use the same base image or module setup for all trials.
   • Minimizes setup failures and wasted allocations.

10. Tune the sweep budget

• Start with short pilot runs to identify promising regions.
• Then allocate longer runs only to the best configurations.

A practical “cheap and effective” setup

• Scheduler: Slurm job array
• Sweep engine: Optuna with pruning
• Training script: reports validation metric every N steps
• Hardware: lowest-cost GPU that supports the model
• Policy: stop trials early if they’re clearly underperforming

Rule of thumb

If you want to minimize cost, spend effort on:

1. fewer bad trials
2. shorter bad trials
3. cheaper GPUs
4. less idle time

If you want, I can also give you:

• a Slurm + Optuna example
• a job-array template
• or a cost comparison of grid search vs random search vs Hyperband